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.gitignore

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+scaled_tiny_protenix_emb_1d/

.vscode/launch.json

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+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python Debugger: Current File",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        },
+        {
+            "name": "first stage",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "/home/hui007/anaconda3/envs/diffrna/bin/torchrun",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "subProcess": true,
+            "args": [
+                "--nproc_per_node",
+                "4",
+                "/home/hui007/rna/first_stage/main_parallel.py"
+            ],
+            "env": {
+                "CUDA_VISIBLE_DEVICES": "0,1,2,3"
+            }
+        }
+    ]
+}

debug.py

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+import torch
+
+data = torch.load('/home/hui007/Protenix/protenix_1d_embeddings/8PEG-0-Y.pt')
+
+print(data.device)

dmt.py

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+from torch_geometric.nn.conv import MessagePassing
+from torch_geometric.nn import global_mean_pool
+from torch_geometric.utils import softmax
+import math
+from typing import Tuple, Optional
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+from torch_geometric.typing import Adj, OptTensor
+import numpy as np
+
+def coord2dist(x, edge_index, sqrt=False, pos_unmask=None):
+    if x.dim() == 3 and pos_unmask is not None:
+        x = x * pos_unmask.unsqueeze(-1) # shape = [B, 3, 3]
+        x = x.sum(dim=1) / pos_unmask.sum(dim=1, keepdim=True).clamp(min=1)
+    elif x.shape[1] == 9 and x.dim() == 2:
+        # coordinates to distance
+        x = x.view(-1, 3, 3).mean(dim=1)
+    # coordinates to distance
+    row, col = edge_index
+    coord_diff = x[row] - x[col]
+    radial = torch.sum(coord_diff ** 2, 1).unsqueeze(1)
+    if sqrt:
+        radial = radial.sqrt()
+    return radial.detach()
+
+
+def modulate(x, shift, scale):
+    # return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+    return x * (1 + scale) + shift
+
+class TransLayer(MessagePassing):
+    """The version for involving the edge feature. Multiply Msg. Without FFN and norm."""
+
+    _alpha: OptTensor
+
+    def __init__(self, x_channels: int, out_channels: int,
+                 heads: int = 1, dropout: float = 0., edge_dim: Optional[int] = None,
+                 bias: bool = True, **kwargs):
+        kwargs.setdefault('aggr', 'add')
+        super(TransLayer, self).__init__(node_dim=0, **kwargs)
+
+        self.x_channels = x_channels
+        self.in_channels = in_channels = x_channels
+        self.out_channels = out_channels
+        self.heads = heads
+        self.dropout = dropout
+        self.edge_dim = edge_dim
+
+        self.lin_key = nn.Linear(in_channels, heads * out_channels, bias=bias)
+        self.lin_query = nn.Linear(in_channels, heads * out_channels, bias=bias)
+        self.lin_value = nn.Linear(in_channels, heads * out_channels, bias=bias)
+
+        self.lin_edge0 = nn.Linear(edge_dim, heads * out_channels, bias=False)
+        self.lin_edge1 = nn.Linear(edge_dim, heads * out_channels, bias=False)
+
+        self.proj = nn.Linear(heads * out_channels, heads * out_channels, bias=bias)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        self.lin_key.reset_parameters()
+        self.lin_query.reset_parameters()
+        self.lin_value.reset_parameters()
+        self.lin_edge0.reset_parameters()
+        self.lin_edge1.reset_parameters()
+        self.proj.reset_parameters()
+
+    def forward(self, x: OptTensor,
+                edge_index: Adj,
+                edge_attr: OptTensor = None
+                ) -> Tensor:
+        """"""
+
+        H, C = self.heads, self.out_channels
+
+        x_feat = x
+        query = self.lin_query(x_feat).view(-1, H, C)
+        key = self.lin_key(x_feat).view(-1, H, C)
+        value = self.lin_value(x_feat).view(-1, H, C)
+
+        # propagate_type: (x: PairTensor, edge_attr: OptTensor)
+        out_x = self.propagate(edge_index, query=query, key=key, value=value, edge_attr=edge_attr, size=None)
+
+        out_x = out_x.view(-1, self.heads * self.out_channels)
+
+        out_x = self.proj(out_x)
+        return out_x
+
+    def message(self, query_i: Tensor, key_j: Tensor, value_j: Tensor,
+                edge_attr: OptTensor,
+                index: Tensor, ptr: OptTensor,
+                size_i: Optional[int]) -> Tuple[Tensor, Tensor]:
+
+        edge_attn = self.lin_edge0(edge_attr).view(-1, self.heads, self.out_channels)
+        edge_attn = torch.tanh(edge_attn)
+        alpha = (query_i * key_j * edge_attn).sum(dim=-1) / math.sqrt(self.out_channels)
+
+        alpha = softmax(alpha, index, ptr, size_i)
+        alpha = F.dropout(alpha, p=self.dropout, training=self.training)
+
+        # node feature message
+        msg = value_j
+        msg = msg * torch.tanh(self.lin_edge1(edge_attr).view(-1, self.heads, self.out_channels))
+        msg = msg * alpha.view(-1, self.heads, 1)
+
+        return msg
+
+    def __repr__(self):
+        return '{}({}, {}, heads={})'.format(self.__class__.__name__,
+                                             self.in_channels,
+                                             self.out_channels, self.heads)
+
+
+class TransLayerOptim(MessagePassing):
+    """The version for involving the edge feature. Multiply Msg. Without FFN and norm."""
+
+    _alpha: OptTensor
+
+    def __init__(self, x_channels: int, out_channels: int,
+                 heads: int = 1, dropout: float = 0., edge_dim: Optional[int] = None,
+                 bias: bool = True, **kwargs):
+        kwargs.setdefault('aggr', 'add')
+        super(TransLayerOptim, self).__init__(node_dim=0, **kwargs)
+
+        self.x_channels = x_channels
+        self.in_channels = in_channels = x_channels
+        self.out_channels = out_channels
+        self.heads = heads
+        self.dropout = dropout
+        self.edge_dim = edge_dim
+
+        self.lin_qkv = nn.Linear(in_channels, heads * out_channels * 3, bias=bias)
+
+        self.lin_edge = nn.Linear(edge_dim, heads * out_channels * 2, bias=False)
+
+        self.proj = nn.Linear(heads * out_channels, heads * out_channels, bias=bias)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        self.lin_qkv.reset_parameters()
+        self.lin_edge.reset_parameters()
+        self.proj.reset_parameters()
+
+    def forward(self, x: OptTensor,
+                edge_index: Adj,
+                edge_attr: OptTensor = None
+                ) -> Tensor:
+        """"""
+
+        H, C = self.heads, self.out_channels
+        x_feat = x
+        qkv = self.lin_qkv(x_feat).view(-1, H, 3, C)
+        query, key, value = qkv.unbind(dim=2)
+
+        # propagate_type: (x: PairTensor, edge_attr: OptTensor)
+        out_x = self.propagate(edge_index, query=query, key=key, value=value, edge_attr=edge_attr, size=None)
+
+        out_x = out_x.view(-1, self.heads * self.out_channels)
+
+        out_x = self.proj(out_x)
+        return out_x
+
+    def message(self, query_i: Tensor, key_j: Tensor, value_j: Tensor,
+                edge_attr: OptTensor,
+                index: Tensor, ptr: OptTensor,
+                size_i: Optional[int]) -> Tuple[Tensor, Tensor]:
+
+        edge_key, edge_value = torch.tanh(self.lin_edge(edge_attr)).view(-1, self.heads, 2, self.out_channels).unbind(dim=2)
+        
+        alpha = (query_i * key_j * edge_key).sum(dim=-1) / math.sqrt(self.out_channels)
+
+        alpha = softmax(alpha, index, ptr, size_i)
+        alpha = F.dropout(alpha, p=self.dropout, training=self.training)
+
+        # node feature message
+        msg = value_j * edge_value * alpha.view(-1, self.heads, 1)
+        return msg
+
+    def __repr__(self):
+        return '{}({}, {}, heads={})'.format(self.__class__.__name__,
+                                             self.in_channels,
+                                             self.out_channels, self.heads)
+
+
+class TransLayerOptimV2(MessagePassing):
+    """The version for involving the edge feature. Multiply Msg. Without FFN and norm."""
+
+    _alpha: OptTensor
+
+    def __init__(self, x_channels: int, out_channels: int,
+                 heads: int = 1, dropout: float = 0., edge_dim: Optional[int] = None,
+                 bias: bool = True, **kwargs):
+        kwargs.setdefault('aggr', 'add')
+        super(TransLayerOptimV2, self).__init__(node_dim=0, **kwargs)
+
+        self.x_channels = x_channels
+        self.in_channels = in_channels = x_channels
+        self.out_channels = out_channels
+        self.heads = heads
+        self.dropout = dropout
+        self.edge_dim = edge_dim
+
+        self.lin_q = nn.Linear(in_channels, heads * out_channels, bias=bias)
+        self.edge_mlp = nn.Sequential(
+            nn.Linear(in_channels + edge_dim, in_channels, bias=bias),
+            nn.GELU(),
+        )
+
+        self.lin_kv = nn.Linear(in_channels, heads * out_channels * 2, bias=bias)
+
+        self.proj = nn.Linear(heads * out_channels, heads * out_channels, bias=bias)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        self.lin_q.reset_parameters()
+        self.lin_kv.reset_parameters()
+        # self.edge_mlp.reset_parameters()
+        self.proj.reset_parameters()
+    
+    
+    def forward(self, x: OptTensor,
+                edge_index: Adj,
+                edge_attr: OptTensor = None
+                ) -> Tensor:
+        """"""
+
+        H, C = self.heads, self.out_channels
+        x_feat = x
+        query = self.lin_q(x_feat).view(-1, H, C)
+
+        # propagate_type: (x: PairTensor, edge_attr: OptTensor)
+        out_x = self.propagate(edge_index, query=query, x_feat=x_feat, edge_attr=edge_attr)
+
+        out_x = out_x.view(-1, self.heads * self.out_channels)
+
+        out_x = self.proj(out_x)
+        return out_x
+
+    def message(self, query_i: Tensor, x_feat_j: Tensor,
+                edge_attr: OptTensor,
+                index: Tensor, ptr: OptTensor,
+                size_i: Optional[int]) -> Tuple[Tensor, Tensor]:
+
+        edge_feat_ij = self.edge_mlp(torch.cat([x_feat_j, edge_attr], dim=-1)) # shape [N * N, in_channels]
+        edge_key_ij, edge_value_ij = self.lin_kv(edge_feat_ij).view(-1, self.heads, 2, self.out_channels).unbind(dim=2) # shape [N * N, heads, out_channels]
+
+        alpha_ij = (query_i * edge_key_ij).sum(dim=-1) / math.sqrt(self.out_channels) # shape [N * N, heads]
+
+        alpha_ij = softmax(alpha_ij, index, ptr, size_i) 
+        alpha_ij = F.dropout(alpha_ij, p=self.dropout, training=self.training)
+
+        # node feature message
+        msg = edge_value_ij * alpha_ij.view(-1, self.heads, 1) # shape [N * N, heads, out_channels]
+        return msg
+
+    def __repr__(self):
+        return '{}({}, {}, heads={})'.format(self.__class__.__name__,
+                                             self.in_channels,
+                                             self.out_channels, self.heads)
+    
+
+class TransLayerOptimV3(MessagePassing):
+    """The version for involving the edge feature. Multiply Msg. Without FFN and norm."""
+
+    _alpha: OptTensor
+
+    def __init__(self, x_channels: int, out_channels: int,
+                 heads: int = 1, dropout: float = 0., edge_dim: Optional[int] = None,
+                 bias: bool = True, **kwargs):
+        kwargs.setdefault('aggr', 'add')
+        super(TransLayerOptimV3, self).__init__(node_dim=0, **kwargs)
+
+        self.x_channels = x_channels
+        self.in_channels = in_channels = x_channels
+        self.out_channels = out_channels
+        self.heads = heads
+        self.dropout = dropout
+        self.edge_dim = edge_dim
+
+        self.lin_q = nn.Linear(in_channels + edge_dim, heads * out_channels, bias=bias)
+        self.lin_kv = nn.Linear(in_channels + edge_dim, heads * out_channels * 2, bias=bias)
+        self.proj = nn.Linear(heads * out_channels, heads * out_channels, bias=bias)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        self.lin_q.reset_parameters()
+        self.lin_kv.reset_parameters()
+        self.proj.reset_parameters()
+    
+    
+    def forward(self, x: OptTensor,
+                edge_index: Adj,
+                edge_attr: OptTensor = None,
+                edge_mask: OptTensor = None
+                ) -> Tensor:
+        """"""
+        x_feat = x
+
+        # propagate_type: (x: PairTensor, edge_attr: OptTensor)
+        out_x = self.propagate(edge_index, x_feat=x_feat, edge_attr=edge_attr)
+
+        out_x = out_x.view(-1, self.heads * self.out_channels)
+
+        out_x = self.proj(out_x)
+        return out_x
+
+    def message(self, x_feat_i: Tensor, x_feat_j: Tensor,
+                edge_attr: OptTensor,
+                index: Tensor, ptr: OptTensor,
+                size_i: Optional[int]) -> Tuple[Tensor, Tensor]:
+        query_ij = self.lin_q(torch.cat([x_feat_i, edge_attr], dim=-1)).view(-1, self.heads, self.out_channels)
+        edge_key_ij, edge_value_ij = self.lin_kv(torch.cat([x_feat_j, edge_attr], dim=-1)).view(-1, self.heads, 2, self.out_channels).unbind(dim=2) # shape [N * N, heads, out_channels]
+
+        alpha_ij = (query_ij * edge_key_ij).sum(dim=-1) / math.sqrt(self.out_channels) # shape [N * N, heads]
+        alpha_ij = softmax(alpha_ij, index, ptr, size_i) 
+        alpha_ij = F.dropout(alpha_ij, p=self.dropout, training=self.training)
+
+        # node feature message
+        msg = edge_value_ij * alpha_ij.view(-1, self.heads, 1) # shape [N * N, heads, out_channels]
+        return msg
+
+    def __repr__(self):
+        return '{}({}, {}, heads={})'.format(self.__class__.__name__,
+                                             self.in_channels,
+                                             self.out_channels, self.heads)
+    
+class TransLayerOptimV3Mask(MessagePassing):
+    """The version for involving the edge feature. Multiply Msg. Without FFN and norm."""
+
+    _alpha: OptTensor
+
+    def __init__(self, x_channels: int, out_channels: int,
+                 heads: int = 1, dropout: float = 0., edge_dim: Optional[int] = None,
+                 bias: bool = True, **kwargs):
+        kwargs.setdefault('aggr', 'add')
+        super(TransLayerOptimV3Mask, self).__init__(node_dim=0, **kwargs)
+
+        self.x_channels = x_channels
+        self.in_channels = in_channels = x_channels
+        self.out_channels = out_channels
+        self.heads = heads
+        self.dropout = dropout
+        self.edge_dim = edge_dim
+
+        self.lin_q = nn.Linear(in_channels + edge_dim, heads * out_channels, bias=bias)
+        self.lin_kv = nn.Linear(in_channels + edge_dim, heads * out_channels * 2, bias=bias)
+        self.proj = nn.Linear(heads * out_channels, heads * out_channels, bias=bias)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        self.lin_q.reset_parameters()
+        self.lin_kv.reset_parameters()
+        self.proj.reset_parameters()
+    
+    
+    def forward(self, x: OptTensor,
+                edge_index: Adj,
+                edge_attr: OptTensor = None,
+                edge_mask: OptTensor = None
+                ) -> Tensor:
+        """"""
+        x_feat = x
+
+        # propagate_type: (x: PairTensor, edge_attr: OptTensor)
+        out_x = self.propagate(edge_index, x_feat=x_feat, edge_attr=edge_attr, edge_mask=edge_mask)
+
+        out_x = out_x.view(-1, self.heads * self.out_channels)
+
+        out_x = self.proj(out_x)
+        return out_x
+
+    def message(self, x_feat_i: Tensor, x_feat_j: Tensor,
+                edge_attr: OptTensor, edge_mask: OptTensor,
+                index: Tensor, ptr: OptTensor,
+                size_i: Optional[int]) -> Tuple[Tensor, Tensor]:
+        query_ij = self.lin_q(torch.cat([x_feat_i, edge_attr], dim=-1)).view(-1, self.heads, self.out_channels)
+        edge_key_ij, edge_value_ij = self.lin_kv(torch.cat([x_feat_j, edge_attr], dim=-1)).view(-1, self.heads, 2, self.out_channels).unbind(dim=2) # shape [N * N, heads, out_channels]
+
+        alpha_ij = (query_ij * edge_key_ij).sum(dim=-1) / math.sqrt(self.out_channels) # shape [N * N, heads]
+        min_dtype = torch.finfo(alpha_ij.dtype).min
+        alpha_ij = alpha_ij + min_dtype * edge_mask.view(-1, 1)
+        
+        alpha_ij = softmax(alpha_ij, index, ptr, size_i) 
+        alpha_ij = F.dropout(alpha_ij, p=self.dropout, training=self.training)
+
+        # node feature message
+        msg = edge_value_ij * alpha_ij.view(-1, self.heads, 1) # shape [N * N, heads, out_channels]
+        return msg
+
+    def __repr__(self):
+        return '{}({}, {}, heads={})'.format(self.__class__.__name__,
+                                             self.in_channels,
+                                             self.out_channels, self.heads)
+
+
+class TransLayerOptimV4(MessagePassing):
+    """The version for involving the edge feature. Multiply Msg. Without FFN and norm."""
+
+    _alpha: OptTensor
+
+    def __init__(self, x_channels: int, out_channels: int,
+                 heads: int = 1, dropout: float = 0., edge_dim: Optional[int] = None,
+                 bias: bool = True, **kwargs):
+        kwargs.setdefault('aggr', 'add')
+        super(TransLayerOptimV4, self).__init__(node_dim=0, **kwargs)
+
+        self.x_channels = x_channels
+        self.in_channels = in_channels = x_channels
+        self.out_channels = out_channels
+        self.heads = heads
+        self.dropout = dropout
+        self.edge_dim = edge_dim
+
+        self.lin_qkv = nn.Linear(in_channels, heads * out_channels * 3, bias=bias)
+        self.lin_qkv_e = nn.Linear(edge_dim, heads * out_channels * 3, bias=False)
+        self.proj = nn.Linear(heads * out_channels, heads * out_channels, bias=bias)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        self.lin_qkv.reset_parameters()
+        self.lin_qkv_e.reset_parameters()
+        self.proj.reset_parameters()
+    
+    
+    def forward(self, x: OptTensor,
+                edge_index: Adj,
+                edge_attr: OptTensor = None
+                ) -> Tensor:
+        """"""
+        x_feat = x
+
+        query, key, value = self.lin_qkv(x_feat).view(-1, self.heads, 3, self.out_channels).unbind(dim=2)
+        # propagate_type: (x: PairTensor, edge_attr: OptTensor)
+        out_x = self.propagate(edge_index, query=query, key=key, value=value, edge_attr=edge_attr)
+
+        out_x = out_x.view(-1, self.heads * self.out_channels)
+
+        out_x = self.proj(out_x)
+        return out_x
+
+    def message(self, query_i: Tensor, key_j: Tensor, value_j: Tensor,
+                edge_attr: OptTensor,
+                index: Tensor, ptr: OptTensor,
+                size_i: Optional[int]) -> Tuple[Tensor, Tensor]:
+
+        edge_query_ij, edge_key_ij, edge_value_ij = self.lin_qkv_e(edge_attr).view(-1, self.heads, 3, self.out_channels).unbind(dim=2)
+        
+        query_ij = query_i + edge_query_ij
+        key_ij = key_j + edge_key_ij
+        value_ij = value_j + edge_value_ij
+
+        alpha_ij = (query_ij * key_ij).sum(dim=-1) / math.sqrt(self.out_channels) # shape [N * N, heads]
+        alpha_ij = softmax(alpha_ij, index, ptr, size_i) 
+        alpha_ij = F.dropout(alpha_ij, p=self.dropout, training=self.training)
+
+        # node feature message
+        msg = value_ij * alpha_ij.view(-1, self.heads, 1) # shape [N * N, heads, out_channels]
+        return msg
+
+    def __repr__(self):
+        return '{}({}, {}, heads={})'.format(self.__class__.__name__,
+                                             self.in_channels,
+                                             self.out_channels, self.heads)
+    
+@torch.jit.script
+def gaussian(x, mean, std):
+    pi = 3.14159
+    a = (2 * pi) ** 0.5
+    return torch.exp(-0.5 * (((x - mean) / std) ** 2)) / (a * std)
+
+
+class GaussianLayer(nn.Module):
+    """Gaussian basis function layer for 3D distance features"""
+    def __init__(self, K, dist_mask_type=False, *args, **kwargs):
+        super().__init__()
+        self.K = K - 1
+        self.means = nn.Embedding(1, self.K)
+        self.stds = nn.Embedding(1, self.K)
+        nn.init.uniform_(self.means.weight, 0, 3)
+        nn.init.uniform_(self.stds.weight, 0, 3)
+
+        self.dist_mask_type = dist_mask_type
+        if self.dist_mask_type == 'replace':
+            self.mask_token = nn.Parameter(torch.zeros(1, K))
+            # self.init_mask_token()
+        elif self.dist_mask_type == 'add':
+            self.mask_token = nn.Parameter(torch.zeros(2, K))
+            nn.init.xavier_normal_(self.mask_token)
+        elif self.dist_mask_type == 'none':
+            pass
+        else:
+            raise ValueError(f'Unknown mask_token {dist_mask_type}')
+
+    def forward(self, x, x_mask=None, *args, **kwargs):
+        mean = self.means.weight.float().view(-1)
+        std = self.stds.weight.float().view(-1).abs() + 1e-5
+        out = torch.cat([x, gaussian(x, mean, std).type_as(self.means.weight)], dim=-1)
+        
+        if self.dist_mask_type == 'replace':
+            out[x_mask] = self.mask_token
+        elif self.dist_mask_type == 'add':
+            out = out + self.mask_token[x_mask.long()]
+        elif self.dist_mask_type == 'none':
+            pass
+        else:
+            assert False
+        return out
+
+
+class DMTBlock(nn.Module):
+    """Equivariant block based on graph relational transformer layer, without extra heads."""
+
+    def __init__(self, node_dim, edge_dim, num_heads, 
+                mlp_ratio=4, act=nn.GELU, dropout=0.0, pair_update=True, trans_ver='v3'):
+        super().__init__()
+        self.dropout = dropout
+        self.act = act()
+        self.pair_update = pair_update
+        
+        if not self.pair_update:
+            self.edge_emb = nn.Sequential(
+                nn.Linear(edge_dim, edge_dim * 2), 
+                nn.GELU(), 
+                nn.Linear(edge_dim * 2, edge_dim),
+                nn.LayerNorm(edge_dim),
+            )
+
+        if trans_ver == 'v2':
+            # message passing layer
+            self.attn_mpnn = TransLayerOptimV2(node_dim, node_dim // num_heads, num_heads, edge_dim=edge_dim, dropout=dropout)
+        elif trans_ver == 'v3':
+            # message passing layer
+            self.attn_mpnn = TransLayerOptimV3(node_dim, node_dim // num_heads, num_heads, edge_dim=edge_dim, dropout=dropout)
+        elif trans_ver == 'v4':
+            # message passing layer
+            self.attn_mpnn = TransLayerOptimV4(node_dim, node_dim // num_heads, num_heads, edge_dim=edge_dim, dropout=dropout)
+        else:
+            # message passing layer
+            self.attn_mpnn = TransLayerOptim(node_dim, node_dim // num_heads, num_heads, edge_dim=edge_dim, dropout=dropout)
+
+        # Feed forward block -> node.
+        self.ff_linear1 = nn.Linear(node_dim, node_dim * mlp_ratio)
+        self.ff_linear2 = nn.Linear(node_dim * mlp_ratio, node_dim)
+        
+        if pair_update:
+            self.node2edge_lin = nn.Linear(node_dim * 2 + edge_dim, edge_dim)
+            # Feed forward block -> edge.
+            self.ff_linear3 = nn.Linear(edge_dim, edge_dim * mlp_ratio)
+            self.ff_linear4 = nn.Linear(edge_dim * mlp_ratio, edge_dim)
+        
+        # equivariant edge update layer
+        self.norm1_node = nn.LayerNorm(node_dim, elementwise_affine=True, eps=1e-6)
+        self.norm2_node = nn.LayerNorm(node_dim, elementwise_affine=True, eps=1e-6)
+        if self.pair_update:
+            self.norm1_edge = nn.LayerNorm(edge_dim, elementwise_affine=True, eps=1e-6)
+            self.norm2_edge = nn.LayerNorm(edge_dim, elementwise_affine=True, eps=1e-6)
+
+    def _ff_block_node(self, x):
+        x = F.dropout(self.act(self.ff_linear1(x)), p=self.dropout, training=self.training)
+        return F.dropout(self.ff_linear2(x), p=self.dropout, training=self.training)
+
+    def _ff_block_edge(self, x):
+        x = F.dropout(self.act(self.ff_linear3(x)), p=self.dropout, training=self.training)
+        return F.dropout(self.ff_linear4(x), p=self.dropout, training=self.training)
+
+    def forward(self, h, edge_attr, edge_index):
+        """
+        A more optimized version of forward_old using torch.compile
+        Params:
+            h: [B*N, hid_dim]
+            edge_attr: [N_edge, edge_hid_dim]
+            edge_index: [2, N_edge]
+        """
+        h_in_node = h
+        h_in_edge = edge_attr
+
+        ## prepare node features
+        h = self.norm1_node(h)
+
+        ## prepare edge features
+        if self.pair_update:
+            edge_attr = self.norm1_edge(edge_attr)
+        else:
+            edge_attr = self.edge_emb(edge_attr)
+
+        # apply transformer-based message passing, update node features and edge features (FFN + norm)
+        h_node = self.attn_mpnn(h, edge_index, edge_attr)
+
+        ## update node features
+        h_out = self.node_update(h_in_node, h_node)
+        
+        ## update edge features
+        if self.pair_update:
+            # h_edge = h_node[edge_index[0]] + h_node[edge_index[1]]
+            h_edge = h_node[edge_index.transpose(0, 1)].flatten(1, 2) # shape [N_edge, 2 * edge_hid_dim]
+            h_edge = torch.cat([h_edge, h_in_edge], dim=-1)
+            h_edge_out = self.edge_update(h_in_edge, h_edge)
+        else:
+            h_edge_out = h_in_edge
+        return h_out, h_edge_out
+
+    # @torch.compile(dynamic=True, disable=disable_compile)
+    def node_update(self, h_in_node, h_node):
+        h_node = h_in_node + h_node
+        _h_node = self.norm2_node(h_node)
+        h_out = h_node + self._ff_block_node(_h_node)
+        return h_out
+    
+    # @torch.compile(dynamic=True, disable=disable_compile)
+    def edge_update(self, h_in_edge, h_edge):
+        h_edge = self.node2edge_lin(h_edge)
+        h_edge = h_in_edge + h_edge
+        _h_edge = self.norm2_edge(h_edge)
+        h_edge_out = h_edge + self._ff_block_edge(_h_edge)
+        return h_edge_out
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_hid, n_position=3000):
+        super(PositionalEncoding, self).__init__()
+
+        # Not a parameter
+        self.register_buffer('pos_table', self._get_sinusoid_encoding_table(n_position, d_hid))
+
+    def _get_sinusoid_encoding_table(self, n_position, d_hid):
+        ''' Sinusoid position encoding table '''
+        # TODO: make it with torch instead of numpy
+
+        def get_position_angle_vec(position):
+            return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]
+
+        sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) # shape = [n_position, d_hid]
+        sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
+        sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1
+
+        return torch.FloatTensor(sinusoid_table) # shape = [n_position, d_hid]
+
+    def forward(self, seq_pos):
+        '''
+        seq_pos: [\sum_i N_i, ]
+        '''
+        return self.pos_table[seq_pos].clone().detach() # shape = [\sum_i N_i, d_hid]
+
+class NodeEmbed(nn.Module):
+    def __init__(self, in_node_features, hidden_size, pos_dim=72, mlp_ratio=4, pos_mask_type='none', llm_embed=False, use_protenix_emb=True, protenix_hidden_dim=384):
+        super().__init__()
+        self.x_linear = nn.Linear(in_node_features, hidden_size * mlp_ratio, bias=False)
+        self.pos_linear = nn.Linear(pos_dim, hidden_size * mlp_ratio, bias=True)
+        self.seq_pos_emb = PositionalEncoding(hidden_size)
+        self.seq_pos_linear = nn.Linear(hidden_size, hidden_size * mlp_ratio, bias=False)
+        self.llm_embed = llm_embed
+        self.use_llm_mlp = False
+        self.use_protenix_emb = use_protenix_emb
+
+        if llm_embed:
+            if self.use_llm_mlp:
+                self.llm_mlp = nn.Sequential(
+                    nn.Linear(hidden_size, hidden_size * mlp_ratio),
+                    nn.GELU(),
+                    nn.Linear(hidden_size * mlp_ratio, hidden_size)
+                )
+            else:
+                self.llm_mlp = nn.Linear(hidden_size, hidden_size * mlp_ratio, bias=False)
+
+        if use_protenix_emb:
+            self.protenix_mlp = nn.Linear(protenix_hidden_dim, hidden_size * mlp_ratio, bias=False)
+
+        self.mlp = nn.Sequential(
+                nn.GELU(),
+                nn.Linear(hidden_size * mlp_ratio, hidden_size)
+            )
+
+        self.pos_mask_type = pos_mask_type
+        if pos_mask_type == 'replace':
+            self.mask_token = nn.Parameter(torch.zeros(1, hidden_size * mlp_ratio))
+            nn.init.normal_(self.mask_token, std=.02)
+        elif pos_mask_type == 'add':
+            self.mask_token = nn.Parameter(torch.zeros(2, hidden_size * mlp_ratio))
+            nn.init.xavier_normal_(self.mask_token)
+        elif pos_mask_type == 'none':
+            pass
+        else:
+            raise ValueError(f'Unknown pos_mask_type {pos_mask_type}')
+
+    def forward(self, x, pos, seq_pos, pos_mask=None, llm_embed=None, protenix_emb=None):
+        if pos.dim() == 3:
+            pos = pos.flatten(1,2)
+        x = self.x_linear(x)
+        pos = self.pos_linear(pos)
+        seq_pos = self.seq_pos_linear(self.seq_pos_emb(seq_pos))
+        
+        if self.pos_mask_type == 'replace':
+            pos[pos_mask] = self.mask_token.to(pos.dtype)
+        elif self.pos_mask_type == 'add':
+            pos = pos + self.mask_token[pos_mask.long()]
+        elif self.pos_mask_type == 'none':
+            pass
+        else:
+            assert False
+        
+        if self.llm_embed:
+            if self.use_llm_mlp:
+                return self.mlp(x + pos + seq_pos) + self.llm_mlp(llm_embed)
+            else:
+                return self.mlp(x + pos + seq_pos + self.llm_mlp(llm_embed))
+            
+        if self.use_protenix_emb:
+            return self.mlp(x + pos + seq_pos + self.protenix_mlp(protenix_emb))
+        
+        return self.mlp(x + pos + seq_pos)
+    
+class NodeEmbed_with_struc(nn.Module):
+    def __init__(self, in_node_features, hidden_size, pos_dim=3, mlp_ratio=4, pos_mask_type='none', llm_embed=False, struc_emb_dim=20):
+        super().__init__()
+        self.x_linear = nn.Linear(in_node_features, hidden_size * mlp_ratio, bias=False)
+        # self.pos_linear = nn.Linear(pos_dim+20, hidden_size * mlp_ratio, bias=True)
+        self.pos_linear = nn.Linear(pos_dim, hidden_size * mlp_ratio, bias=True)# add struc_emb [0414 by TIANRUI]
+        self.struc_linear = nn.Linear(struc_emb_dim*24, hidden_size * mlp_ratio, bias=False)# add struc_emb [0414 by TIANRUI]
+        self.seq_pos_emb = PositionalEncoding(hidden_size)
+        self.seq_pos_linear = nn.Linear(hidden_size, hidden_size * mlp_ratio, bias=False)
+        self.llm_embed = llm_embed
+        self.use_llm_mlp = False
+        if llm_embed:
+            if self.use_llm_mlp:
+                self.llm_mlp = nn.Sequential(
+                    nn.Linear(hidden_size, hidden_size * mlp_ratio),
+                    nn.GELU(),
+                    nn.Linear(hidden_size * mlp_ratio, hidden_size)
+                )
+            else:
+                self.llm_mlp = nn.Linear(hidden_size, hidden_size * mlp_ratio, bias=False)
+        self.mlp = nn.Sequential(
+                nn.GELU(),
+                nn.Linear(hidden_size * mlp_ratio, hidden_size)
+            )
+
+        self.pos_mask_type = pos_mask_type
+        if pos_mask_type == 'replace':
+            self.mask_token = nn.Parameter(torch.zeros(1, hidden_size * mlp_ratio))
+            nn.init.normal_(self.mask_token, std=.02)
+        elif pos_mask_type == 'add':
+            self.mask_token = nn.Parameter(torch.zeros(2, hidden_size * mlp_ratio))
+            nn.init.xavier_normal_(self.mask_token)
+        elif pos_mask_type == 'none':
+            pass
+        else:
+            raise ValueError(f'Unknown pos_mask_type {pos_mask_type}')
+
+    def forward(self, x, struc_emb, pos, seq_pos, pos_mask=None, llm_embed=None):
+        if pos.dim() == 3:
+            pos = pos.flatten(1,2)
+            struc_emb = struc_emb.flatten(1,2)
+        x = self.x_linear(x)
+        pos = self.pos_linear(pos)
+        # pos = self.pos_linear(pos).sum(dim=1)
+        struc_emb = self.struc_linear(struc_emb)
+        pos = pos + struc_emb
+
+        seq_pos = self.seq_pos_linear(self.seq_pos_emb(seq_pos))
+        
+        if self.pos_mask_type == 'replace':
+            pos[pos_mask] = self.mask_token.to(pos.dtype)
+        elif self.pos_mask_type == 'add':
+            pos = pos + self.mask_token[pos_mask.long()]
+        elif self.pos_mask_type == 'none':
+            pass
+        else:
+            assert False
+        
+        if self.llm_embed:
+            if self.use_llm_mlp:
+                return self.mlp(x + pos + seq_pos) + self.llm_mlp(llm_embed)
+            else:
+                return self.mlp(x + pos + seq_pos + self.llm_mlp(llm_embed))
+        return self.mlp(x + pos + seq_pos)
+    
+class DMT(nn.Module):
+    def __init__(self, configs):
+        super().__init__()
+
+        self.use_struc_emb = configs.use_struc_emb
+        self.disable_dist = configs.disable_dist
+        self.new_aa = configs.new_aa
+        self.sqrt_dis = configs.sqrt_dis
+        
+        edge_dim = configs.hidden_dim // configs.e2n_ratio
+        
+        if configs.use_struc_emb:
+            self.node_emb = NodeEmbed_with_struc(configs.in_res_node_features, configs.hidden_dim, configs.pos_dim, configs.mlp_ratio, configs.pos_mask_type, configs.enable_llm)
+        else:
+            self.node_emb = NodeEmbed(configs.in_res_node_features, configs.hidden_dim, configs.pos_dim, configs.mlp_ratio, configs.pos_mask_type, configs.enable_llm, configs.use_protenix_emb)
+        
+        if not configs.disable_dist:
+            self.dist_mask_type = configs.dist_mask_type
+            # distance GBF embedding
+            self.dist_gbf = GaussianLayer(edge_dim, configs.dist_mask_type)
+            in_edge_dim = configs.in_res_edge_features + edge_dim
+        else:
+            in_edge_dim = configs.in_res_edge_features
+            
+        self.edge_emb = nn.Sequential(
+            nn.Linear(in_edge_dim, 2 * edge_dim),
+            nn.GELU(),
+            nn.Linear(2 * edge_dim, edge_dim),
+        )
+
+        self.blocks = nn.ModuleList()
+        for _ in range(configs.n_blocks):
+            self.blocks.append(DMTBlock(configs.hidden_dim, edge_dim,
+                                configs.n_heads, mlp_ratio=configs.mlp_ratio, act=nn.GELU, dropout=configs.dropout, pair_update=not configs.not_pair_update, trans_ver=configs.trans_ver))
+
+        self.pooling_mlp = nn.Sequential(
+            nn.Linear(configs.hidden_dim, configs.hidden_dim * configs.mlp_ratio),
+            nn.GELU(),
+            nn.Linear(configs.hidden_dim * configs.mlp_ratio, configs.hidden_dim)
+        )
+
+        self.pred_layer = nn.Sequential(
+            nn.Linear(configs.hidden_dim, configs.hidden_dim * configs.mlp_ratio),
+            nn.Tanh(),
+            nn.Linear(configs.hidden_dim * configs.mlp_ratio, 72)
+        )
+
+    def forward(self, data):
+
+        assert hasattr(data, 'seq_pos')
+        seq_pos = data.seq_pos
+
+        # obtain node and edge feature
+        llm_embed = data.get('llm_embed', None)
+
+        if self.use_struc_emb:
+            # add struc_emb [0403 by TIANRUI]
+            # struc_input = torch.cat([data.pos, data.struc_emb], dim=-1)
+            node_h = self.node_emb(data.x, data.struc_emb, data.gt_pos, seq_pos, data['pos_mask'], llm_embed=llm_embed)
+        else:
+            # node_h = self.node_emb(data.x, data.gt_pos, seq_pos, data['pos_mask'], llm_embed=llm_embed, protenix_emb=data.get('protenix_emb', None))
+            node_h = self.node_emb(data.x, data.pos, seq_pos, data['pos_mask'], llm_embed=llm_embed, protenix_emb=data.get('protenix_emb', None))
+
+        # add distance to edge feature
+        if not self.disable_dist:
+            if self.new_aa:
+                # distance = coord2dist(data.gt_pos, data.edge_index, self.sqrt_dis, ~data.pos_mask)
+                distance = coord2dist(data.pos, data.edge_index, self.sqrt_dis, ~data.pos_mask)
+            else:
+                # distance = coord2dist(data.gt_pos, data.edge_index, self.sqrt_dis)
+                distance = coord2dist(data.pos, data.edge_index, self.sqrt_dis)
+            
+            edge_mask = None
+            if self.dist_mask_type != 'none':
+                edge_mask = data.pos_mask[data.edge_index].any(dim=0)
+            dist_emb = self.dist_gbf(distance, edge_mask)
+            edge_h = self.edge_emb(torch.cat([data.edge_attr, dist_emb], dim=-1))
+        else:
+            edge_h = self.edge_emb(data.edge_attr)
+
+        # run the DMT blocks
+        for layer in self.blocks:
+            node_h, edge_h = layer(node_h, edge_h, data.edge_index)
+
+        pred_noise = self.pred_layer(node_h).reshape(data.pos.shape)
+
+        denoising_loss = ((pred_noise[~data['pos_mask']] - data['noise'][~data['pos_mask']]) ** 2).mean()
+
+        graph_h = global_mean_pool(node_h, data.batch)  # [B, hidden_dim]
+
+        graph_h = self.pooling_mlp(graph_h)
+        return graph_h, denoising_loss
+
+# class InfoNCELoss(nn.Module):
+#     def __init__(self, temperature=0.05):
+#         super().__init__()
+#         self.temperature = temperature
+
+#     def forward(self, z1, z2):
+#         """
+#         z1, z2: (B, D) 两个视图经过 projection head 的表示
+#         """
+#         B = z1.shape[0]
+#         z = torch.cat([z1, z2], dim=0)  # (2B, D)
+#         sim = F.cosine_similarity(z.unsqueeze(1), z.unsqueeze(0), dim=-1)  # (2B, 2B)
+
+#         # Positive indices: i-th with (i + B)%2B
+#         labels = torch.arange(B, device=z.device)
+#         labels = torch.cat([labels + B, labels], dim=0)
+
+#         # Mask: remove self-similarity
+#         mask = ~torch.eye(2 * B, dtype=torch.bool, device=z.device)
+
+#         sim = sim / self.temperature
+#         sim_exp = torch.exp(sim) * mask  # (2B, 2B), exp(sim) and remove diagonal
+
+#         # Denominator
+#         denom = sim_exp.sum(dim=1)  # (2B,)
+
+#         # Numerator: select positive pairs
+#         numerator = torch.exp(sim[torch.arange(2 * B), labels])
+
+#         loss = -torch.log(numerator / denom)
+#         return loss.mean()

main_parallel.py

@@ -0,0 +1,258 @@
+import os
+import logging
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.optim as optim
+import datetime
+import torch.nn.functional as F
+
+from contextlib import nullcontext
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, DistributedSampler
+
+from ml_collections.config_dict import ConfigDict
+from rna_dataset import RNADataset, RNACollater, RNACollater_v2
+from dmt import DMT
+from torch_scatter import scatter
+from torch.optim.lr_scheduler import CosineAnnealingLR
+from collections import deque
+from torch.cuda.amp import GradScaler
+
+# CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 main_parallel.py
+# CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 main_parallel.py
+
+# CUDA_VISIBLE_DEVICES=4,5,6,7,8,9 torchrun --nnodes=1 --nproc_per_node=6 main_parallel.py --rdzv_backend=c10d --rdzv_endpoint=localhost:0
+
+def info_nce_loss(z1, z2, temperature=0.05):
+    z1 = F.normalize(z1, dim=1)
+    z2 = F.normalize(z2, dim=1)
+
+    B = z1.size(0)
+    z = torch.cat([z1, z2], dim=0)  # (2B, D)
+
+    sim = torch.matmul(z, z.T) / temperature  # (2B, 2B)
+    mask = torch.eye(2 * B, device=z.device, dtype=torch.bool)
+    sim.masked_fill_(mask, -9e15)
+
+    pos_idx = torch.cat([torch.arange(B, 2 * B), torch.arange(0, B)]).to(z.device)
+    pos_sim = sim[torch.arange(2 * B), pos_idx]
+
+    # Uncomment the following lines if you want to mask out the positive pairs
+    # pos_mask = torch.zeros_like(sim, dtype=torch.bool)
+    # pos_mask[torch.arange(2 * B), pos_idx] = True
+    # sim.masked_fill_(pos_mask, -9e15)
+
+    loss = -pos_sim + torch.logsumexp(sim, dim=1)
+    return loss.mean()
+
+class GatherLayer(torch.autograd.Function):
+    """
+    Gather tensors from all workers with support for backward propagation:
+    This implementation does not cut the gradients as torch.distributed.all_gather does.
+    """
+
+    @staticmethod
+    def forward(ctx, x):
+        output = [
+            torch.zeros_like(x) for _ in range(torch.distributed.get_world_size())
+        ]
+        torch.distributed.all_gather(output, x)
+        return tuple(output)
+
+    @staticmethod
+    def backward(ctx, *grads):
+        all_gradients = torch.stack(grads)
+        torch.distributed.all_reduce(all_gradients)
+        return all_gradients[torch.distributed.get_rank()]
+
+def all_gather_with_grad(tensors):
+    """
+    Performs all_gather operation on the provided tensors.
+    Graph remains connected for backward grad computation.
+    """
+    # Queue the gathered tensors
+    world_size = torch.distributed.get_world_size()
+    # There is no need for reduction in the single-proc case
+    if world_size == 1:
+        return tensors
+
+    tensor_all = GatherLayer.apply(tensors)
+
+    return torch.cat(tensor_all, dim=0)
+
+def train(configs):
+    local_rank, world_size, rank = int(os.environ["LOCAL_RANK"]), int(os.environ["WORLD_SIZE"]), int(os.environ["RANK"])
+
+    # os.environ['MASTER_ADDR'] = 'localhost'
+    # os.environ['MASTER_PORT'] = '29500'
+    dist.init_process_group("gloo", rank=rank, world_size=world_size)
+    torch.cuda.set_device(local_rank)
+
+    date_str = datetime.datetime.now().strftime("%Y%m%d_%H%M")
+    
+    if rank == 0:
+        total_batch_size = world_size * configs.batch_size
+        log_dir = f"/home/hui007/rna/first_stage/log/train_{date_str}_totalbatchsize{total_batch_size}"
+        os.makedirs(log_dir, exist_ok=True)
+        log_path = os.path.join(log_dir, f"train_{date_str}.log")
+        logger = logging.getLogger()
+        logger.setLevel(logging.INFO)
+        # 移除已有 handler
+        for handler in logger.handlers[:]:
+            logger.removeHandler(handler)
+        file_handler = logging.FileHandler(log_path, mode='a')
+        formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
+        file_handler.setFormatter(formatter)
+        logger.addHandler(file_handler)
+        logger.info(f"Using DDP with total {world_size} GPUS...")
+
+    # z1_queue = deque(maxlen=configs.memory_size)
+    # z2_queue = deque(maxlen=configs.memory_size)
+
+    model = DMT(configs)
+
+    resume_path = configs.get('resume_path', None)
+    if resume_path is not None and os.path.exists(resume_path):
+        model.load_state_dict(torch.load(resume_path, map_location=f"cuda:{local_rank}"))
+        if rank == 0:
+            logging.info(f"Loaded checkpoint from {resume_path}")
+    
+    device = torch.device(f'cuda:{local_rank}')
+    model = model.to(device)
+    ddp_model = DDP(model, device_ids=[local_rank], find_unused_parameters=True)
+    
+    # if rank == 0:
+    #     torch.save(ddp_model.module.state_dict(), f"/home/hui007/rna/first_stage/model_checkpoint/test.pth")
+    
+    dataset = RNADataset('/home/hui007/rna/rna_repr/zhiyuan/train_data_final.npz', protenix_emb_path='/home/hui007/rna/first_stage/scaled_tiny_protenix_emb_1d')
+    
+    sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank)
+    dataloader = DataLoader(dataset, batch_size=configs.batch_size, sampler=sampler, drop_last=True, collate_fn=RNACollater_v2())
+
+    # optimizer = optim.SGD(ddp_model.parameters(), lr=0.01)
+    optimizer = torch.optim.AdamW(ddp_model.parameters(), lr=1e-5, weight_decay=0.01)
+    scheduler = CosineAnnealingLR(optimizer, T_max=configs.epoch, eta_min=1e-6)
+
+    global_step = 0
+    loss_accum = 0.0
+    contrast_loss_accum = 0.0
+    denoising_loss_accum = 0.0
+    count_accum = 0
+
+    scaler = GradScaler(enabled=(configs.dtype in ["fp16", "bf16"]))
+
+    for epoch in range(configs.epoch):
+        sampler.set_epoch(epoch)
+        ddp_model.train()
+        for g1, g2 in dataloader:
+            g1 = g1.to(device)
+            g2 = g2.to(device)
+
+            eval_precision = {
+                "fp32": torch.float32,
+                "bf16": torch.bfloat16,
+                "fp16": torch.float16,
+            }[configs.dtype]
+
+            enable_amp = (
+                torch.autocast(device_type="cuda", dtype=eval_precision)
+                if torch.cuda.is_available()
+                else nullcontext()
+            )
+            optimizer.zero_grad()
+
+            with enable_amp:
+                z1, denoising_loss1 = ddp_model(g1)
+                z2, denoising_loss2 = ddp_model(g2)
+
+                z1_all = all_gather_with_grad(z1)
+                z2_all = all_gather_with_grad(z2)
+
+                # if len(msa_queue) > 0:
+                #     msa_output_extended = msa_output + list(msa_queue)
+                #     dmt_output_extended = dmt_output + list(dmt_queue)
+                # else:
+                #     msa_output_extended = msa_output
+                #     dmt_output_extended = dmt_output
+
+                # 更新队列（detach 防止梯度传播
+                # for x in msa_output:
+                #     msa_queue.append(x.detach())
+                # for x in dmt_output:
+                #     dmt_queue.append(x.detach())
+
+                contrast_loss = info_nce_loss(z1_all, z2_all, temperature=0.05)
+                denoising_loss = (denoising_loss1 + denoising_loss2) / 2
+                loss = 0.5 * contrast_loss + denoising_loss
+                
+                # print(loss, loss.requires_grad, loss.grad_fn)
+
+            # loss.backward()
+            scaler.scale(loss).backward()
+            # torch.nn.utils.clip_grad_norm_(ddp_model.parameters(), max_norm=1.0)  # 梯度裁剪
+            # optimizer.step()
+            scaler.step(optimizer)
+            scaler.update()
+            global_step += 1
+
+            if rank == 0:
+                print(f"Epoch [{epoch}/{configs.epoch}], Step [{global_step}], Loss: {loss.item():.4f}")
+                contrast_loss_accum += contrast_loss.item()
+                denoising_loss_accum += denoising_loss.item()
+                loss_accum += loss.item()
+                count_accum += 1
+                if global_step % 5 == 0:
+                    avg_loss = loss_accum / count_accum
+                    avg_denoising_loss = denoising_loss_accum / count_accum
+                    avg_contrast_loss = contrast_loss_accum / count_accum
+
+                    print(f"Epoch: {epoch}, Step: {global_step}, Avg Loss: {avg_loss:.4f}")
+                    logging.info(f"Epoch: [{epoch}/{configs.epoch}], Step: {global_step}, avg_contrast_loss: {avg_contrast_loss:.4f}, avg_denoising_loss: {avg_denoising_loss:.4f}, Avg Loss: {avg_loss:.4f}")
+                    loss_accum = 0.0
+                    contrast_loss_accum = 0.0
+                    denoising_loss_accum = 0.0
+                    count_accum = 0
+
+        if rank == 0 and epoch % 100 == 0:
+            os.makedirs(f'/home/hui007/rna/first_stage/model_checkpoint/{date_str}', exist_ok=True)
+            torch.save(ddp_model.module.state_dict(), f"/home/hui007/rna/first_stage/model_checkpoint/{date_str}/epoch{epoch}_{date_str}.pth")
+        scheduler.step()
+        torch.cuda.empty_cache()
+
+    dist.destroy_process_group()
+
+def main():
+    configs = {
+        # 'resume_path': '/home/hui007/rna/model_checkpoint/torchrun_model_epoch_15_20250610_1732.pth',
+        'resume_path': None,
+        'epoch': 10000,
+        'batch_size': 64,  # per GPU
+        # 'memory_size': 16,
+        "dtype": "bf16",  # default training dtype: bf16
+        'new_aa': True,
+        'sqrt_dis': False,
+        'hidden_dim': 384, 
+        'e2n_ratio': 6, 
+        'n_blocks': 12, 
+        'in_res_node_features': 4, 
+        'pos_mask_type': 'none', 
+        'enable_llm': False, 
+        'use_struc_emb': False, 
+        'pos_dim': 72, 
+        'mlp_ratio': 4, 
+        'disable_dist': False, 
+        'dist_mask_type': 'none', 
+        'n_heads': 8, 
+        'in_res_edge_features': 5, 
+        'not_pair_update': False, 
+        'dropout': 0.0, 
+        'trans_ver': 'v3',
+        "use_protenix_emb": True
+    }
+
+    configs = ConfigDict(configs)
+    train(configs)
+
+if __name__ == "__main__":
+    main()

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nucleotides/A.cif

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+data_A
+#
+_chem_comp.id A
+_chem_comp.name "ADENOSINE-5'-MONOPHOSPHATE"
+_chem_comp.type "RNA LINKING"
+_chem_comp.pdbx_type ATOMN
+_chem_comp.formula "C10 H14 N5 O7 P"
+_chem_comp.mon_nstd_parent_comp_id ?
+_chem_comp.pdbx_synonyms ?
+_chem_comp.pdbx_formal_charge 0
+_chem_comp.pdbx_initial_date 1999-07-08
+_chem_comp.pdbx_modified_date 2024-10-15
+_chem_comp.pdbx_ambiguous_flag N
+_chem_comp.pdbx_release_status REL
+_chem_comp.pdbx_replaced_by ?
+_chem_comp.pdbx_replaces ?
+_chem_comp.formula_weight 347.221
+_chem_comp.one_letter_code A
+_chem_comp.three_letter_code A
+_chem_comp.pdbx_model_coordinates_details ?
+_chem_comp.pdbx_model_coordinates_missing_flag N
+_chem_comp.pdbx_ideal_coordinates_details ?
+_chem_comp.pdbx_ideal_coordinates_missing_flag N
+_chem_comp.pdbx_model_coordinates_db_code ?
+_chem_comp.pdbx_subcomponent_list ?
+_chem_comp.pdbx_processing_site RCSB
+_chem_comp.pdbx_pcm Y
+#
+loop_
+_chem_comp_atom.comp_id
+_chem_comp_atom.atom_id
+_chem_comp_atom.alt_atom_id
+_chem_comp_atom.type_symbol
+_chem_comp_atom.charge
+_chem_comp_atom.pdbx_align
+_chem_comp_atom.pdbx_aromatic_flag
+_chem_comp_atom.pdbx_leaving_atom_flag
+_chem_comp_atom.pdbx_stereo_config
+_chem_comp_atom.pdbx_backbone_atom_flag
+_chem_comp_atom.pdbx_n_terminal_atom_flag
+_chem_comp_atom.pdbx_c_terminal_atom_flag
+_chem_comp_atom.model_Cartn_x
+_chem_comp_atom.model_Cartn_y
+_chem_comp_atom.model_Cartn_z
+_chem_comp_atom.pdbx_model_Cartn_x_ideal
+_chem_comp_atom.pdbx_model_Cartn_y_ideal
+_chem_comp_atom.pdbx_model_Cartn_z_ideal
+_chem_comp_atom.pdbx_component_atom_id
+_chem_comp_atom.pdbx_component_comp_id
+_chem_comp_atom.pdbx_ordinal
+A OP3 O3P O 0 1 N Y N N N N 22.586 9.736 -6.030 2.135 -1.141 -5.313 OP3 A 1
+A P P P 0 1 N N N N N N 23.014 10.223 -7.491 1.024 -0.137 -4.723 P A 2
+A OP1 O1P O 0 1 N N N N N N 21.938 9.966 -8.412 1.633 1.190 -4.488 OP1 A 3
+A OP2 O2P O 0 1 N N N N N N 24.378 9.686 -7.860 -0.183 0.005 -5.778 OP2 A 4
+A "O5'" O5* O 0 1 N N N N N N 23.144 11.720 -7.092 0.456 -0.720 -3.334 "O5'" A 5
+A "C5'" C5* C 0 1 N N N N N N 24.013 12.484 -7.839 -0.520 0.209 -2.863 "C5'" A 6
+A "C4'" C4* C 0 1 N N R N N N 23.996 13.899 -7.276 -1.101 -0.287 -1.538 "C4'" A 7
+A "O4'" O4* O 0 1 N N N N N N 24.523 13.928 -5.945 -0.064 -0.383 -0.538 "O4'" A 8
+A "C3'" C3* C 0 1 N N S N N N 24.868 14.877 -8.041 -2.105 0.739 -0.969 "C3'" A 9
+A "O3'" O3* O 0 1 N N N N N N 24.195 15.389 -9.178 -3.445 0.360 -1.287 "O3'" A 10
+A "C2'" C2* C 0 1 N N R N N N 25.172 15.942 -7.004 -1.874 0.684 0.558 "C2'" A 11
+A "O2'" O2* O 0 1 N N N N N N 24.060 16.759 -6.874 -3.065 0.271 1.231 "O2'" A 12
+A "C1'" C1* C 0 1 N N R N N N 25.387 15.094 -5.814 -0.755 -0.367 0.729 "C1'" A 13
+A N9 N9 N 0 1 Y N N N N N 26.745 14.503 -5.630 0.158 0.029 1.803 N9 A 14
+A C8 C8 C 0 1 Y N N N N N 27.163 13.163 -5.773 1.265 0.813 1.672 C8 A 15
+A N7 N7 N 0 1 Y N N N N N 28.488 13.043 -5.514 1.843 0.963 2.828 N7 A 16
+A C5 C5 C 0 1 Y N N N N N 28.887 14.292 -5.222 1.143 0.292 3.773 C5 A 17
+A C6 C6 C 0 1 Y N N N N N 30.177 14.748 -4.871 1.290 0.091 5.156 C6 A 18
+A N6 N6 N 0 1 N N N N N N 31.245 13.997 -4.775 2.344 0.664 5.846 N6 A 19
+A N1 N1 N 0 1 Y N N N N N 30.286 16.119 -4.615 0.391 -0.656 5.787 N1 A 20
+A C2 C2 C 0 1 Y N N N N N 29.272 16.866 -4.702 -0.617 -1.206 5.136 C2 A 21
+A N3 N3 N 0 1 Y N N N N N 27.992 16.509 -5.030 -0.792 -1.051 3.841 N3 A 22
+A C4 C4 C 0 1 Y N N N N N 27.856 15.249 -5.271 0.056 -0.320 3.126 C4 A 23
+A HOP3 3HOP H 0 0 N N N N N N 23.296 9.905 -5.422 2.448 -0.755 -6.142 HOP3 A 24
+A HOP2 2HOP H 0 0 N N N N N N 25.088 9.855 -7.252 -0.552 -0.879 -5.902 HOP2 A 25
+A "H5'" 1H5* H 0 1 N N N N N N 25.039 12.050 -7.885 -1.319 0.301 -3.599 "H5'" A 26
+A "H5''" 2H5* H 0 0 N N N N N N 23.788 12.450 -8.930 -0.052 1.182 -2.712 "H5''" A 27
+A "H4'" H4* H 0 1 N N N N N N 22.923 14.196 -7.338 -1.586 -1.254 -1.677 "H4'" A 28
+A "H3'" H3* H 0 1 N N N N N N 25.794 14.420 -8.461 -1.890 1.736 -1.353 "H3'" A 29
+A "HO3'" H3T H 0 0 N Y N N N N 24.741 16.001 -9.657 -4.024 1.035 -0.908 "HO3'" A 30
+A "H2'" H2* H 0 1 N N N N N N 26.025 16.626 -7.219 -1.543 1.654 0.930 "H2'" A 31
+A "HO2'" 2HO* H 0 0 N N N N N N 24.250 17.425 -6.224 -3.740 0.936 1.037 "HO2'" A 32
+A "H1'" H1* H 0 1 N N N N N N 25.197 15.783 -4.958 -1.185 -1.346 0.940 "H1'" A 33
+A H8 H8 H 0 1 N N N N N N 26.526 12.307 -6.055 1.611 1.246 0.745 H8 A 34
+A H61 1H6 H 0 1 N N N N N N 32.176 14.326 -4.521 2.432 0.522 6.801 H61 A 35
+A H62 2H6 H 0 1 N N N N N N 31.340 13.509 -5.666 2.996 1.205 5.374 H62 A 36
+A H2 H2 H 0 1 N N N N N N 29.524 17.915 -4.473 -1.325 -1.807 5.688 H2 A 37
+#
+loop_
+_chem_comp_bond.comp_id
+_chem_comp_bond.atom_id_1
+_chem_comp_bond.atom_id_2
+_chem_comp_bond.value_order
+_chem_comp_bond.pdbx_aromatic_flag
+_chem_comp_bond.pdbx_stereo_config
+_chem_comp_bond.pdbx_ordinal
+A OP3 P SING N N 1
+A OP3 HOP3 SING N N 2
+A P OP1 DOUB N N 3
+A P OP2 SING N N 4
+A P "O5'" SING N N 5
+A OP2 HOP2 SING N N 6
+A "O5'" "C5'" SING N N 7
+A "C5'" "C4'" SING N N 8
+A "C5'" "H5'" SING N N 9
+A "C5'" "H5''" SING N N 10
+A "C4'" "O4'" SING N N 11
+A "C4'" "C3'" SING N N 12
+A "C4'" "H4'" SING N N 13
+A "O4'" "C1'" SING N N 14
+A "C3'" "O3'" SING N N 15
+A "C3'" "C2'" SING N N 16
+A "C3'" "H3'" SING N N 17
+A "O3'" "HO3'" SING N N 18
+A "C2'" "O2'" SING N N 19
+A "C2'" "C1'" SING N N 20
+A "C2'" "H2'" SING N N 21
+A "O2'" "HO2'" SING N N 22
+A "C1'" N9 SING N N 23
+A "C1'" "H1'" SING N N 24
+A N9 C8 SING Y N 25
+A N9 C4 SING Y N 26
+A C8 N7 DOUB Y N 27
+A C8 H8 SING N N 28
+A N7 C5 SING Y N 29
+A C5 C6 SING Y N 30
+A C5 C4 DOUB Y N 31
+A C6 N6 SING N N 32
+A C6 N1 DOUB Y N 33
+A N6 H61 SING N N 34
+A N6 H62 SING N N 35
+A N1 C2 SING Y N 36
+A C2 N3 DOUB Y N 37
+A C2 H2 SING N N 38
+A N3 C4 SING Y N 39
+#
+loop_
+_pdbx_chem_comp_descriptor.comp_id
+_pdbx_chem_comp_descriptor.type
+_pdbx_chem_comp_descriptor.program
+_pdbx_chem_comp_descriptor.program_version
+_pdbx_chem_comp_descriptor.descriptor
+A SMILES ACDLabs 10.04 "O=P(O)(O)OCC3OC(n2cnc1c(ncnc12)N)C(O)C3O"
+A SMILES_CANONICAL CACTVS 3.341 "Nc1ncnc2n(cnc12)[C@@H]3O[C@H](CO[P](O)(O)=O)[C@@H](O)[C@H]3O"
+A SMILES CACTVS 3.341 "Nc1ncnc2n(cnc12)[CH]3O[CH](CO[P](O)(O)=O)[CH](O)[CH]3O"
+A SMILES_CANONICAL "OpenEye OEToolkits" 1.5.0 "c1nc(c2c(n1)n(cn2)[C@H]3[C@@H]([C@@H]([C@H](O3)COP(=O)(O)O)O)O)N"
+A SMILES "OpenEye OEToolkits" 1.5.0 "c1nc(c2c(n1)n(cn2)C3C(C(C(O3)COP(=O)(O)O)O)O)N"
+A InChI InChI 1.03 "InChI=1S/C10H14N5O7P/c11-8-5-9(13-2-12-8)15(3-14-5)10-7(17)6(16)4(22-10)1-21-23(18,19)20/h2-4,6-7,10,16-17H,1H2,(H2,11,12,13)(H2,18,19,20)/t4-,6-,7-,10-/m1/s1"
+A InChIKey InChI 1.03 UDMBCSSLTHHNCD-KQYNXXCUSA-N
+#
+loop_
+_pdbx_chem_comp_identifier.comp_id
+_pdbx_chem_comp_identifier.type
+_pdbx_chem_comp_identifier.program
+_pdbx_chem_comp_identifier.program_version
+_pdbx_chem_comp_identifier.identifier
+A "SYSTEMATIC NAME" ACDLabs 10.04
+;5'-adenylic acid
+;
+A "SYSTEMATIC NAME" "OpenEye OEToolkits" 1.5.0 "[(2R,3S,4R,5R)-5-(6-aminopurin-9-yl)-3,4-dihydroxy-oxolan-2-yl]methyl dihydrogen phosphate"
+#
+loop_
+_pdbx_chem_comp_audit.comp_id
+_pdbx_chem_comp_audit.action_type
+_pdbx_chem_comp_audit.date
+_pdbx_chem_comp_audit.processing_site
+A 'Create component' 1999-07-08 RCSB
+A 'Modify descriptor' 2011-06-04 RCSB
+A 'Modify PCM' 2024-09-27 PDBE
+A 'Modify PCM' 2024-10-15 PDBE
+#
+loop_
+_pdbx_chem_comp_pcm.pcm_id
+_pdbx_chem_comp_pcm.comp_id
+_pdbx_chem_comp_pcm.modified_residue_id
+_pdbx_chem_comp_pcm.type
+_pdbx_chem_comp_pcm.category
+_pdbx_chem_comp_pcm.position
+_pdbx_chem_comp_pcm.polypeptide_position
+_pdbx_chem_comp_pcm.comp_id_linking_atom
+_pdbx_chem_comp_pcm.modified_residue_id_linking_atom
+_pdbx_chem_comp_pcm.uniprot_specific_ptm_accession
+_pdbx_chem_comp_pcm.uniprot_generic_ptm_accession
+1 A ALA None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+2 A ASP None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+3 A GLN None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+4 A LEU None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+5 A LYS None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+6 A MET None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+7 A SER None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+8 A UNK None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+9 A VAL None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+10 A TRP None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+11 A PHE None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+12 A PRO None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+13 A SMC None 'Covalent chemical modification' 'Amino-acid side chain' 'Any position' C2 CS ? ?
+14 A ARG None 'Covalent chemical modification' 'Amino-acid backbone' C-terminal "O3'" C ? ?
+#
+_pdbe_chem_comp_drugbank_details.comp_id A
+_pdbe_chem_comp_drugbank_details.drugbank_id DB00131
+_pdbe_chem_comp_drugbank_details.type 'small molecule'
+_pdbe_chem_comp_drugbank_details.name 'Adenosine phosphate'
+_pdbe_chem_comp_drugbank_details.description
+'Adenosine phosphate, or adenylic acid, is an adenine nucleotide containing one phosphate group esterified to the sugar moiety in the 2'-, 3'-, or 5'-position. Adenosine phosphate was withdrawn by the FDA since it was considered neither safe nor effective for its intended uses as a vasodilator and an anti-inflammatory.[L43937]'
+_pdbe_chem_comp_drugbank_details.cas_number 61-19-8
+_pdbe_chem_comp_drugbank_details.mechanism_of_action
+"Nucleotides such as Adenosine-5'-Monophosphate affect a number of immune functions, including the reversal of malnutrition and starvation-induced immunosuppression, the enhancement of T-cell maturation and function, the enhancement of natural killer cell activity, the improvement of delayed cutaneous hypersensitivity, helping resistance to such infectious agents as Staphylococcus aureus and Candida albicans, and finally the modulation of T-cell responses toward type 1 CD4 helper lymphocytes or Th1 cells. Studies have shown that mice fed a nucleotide-free diet have both impaired humoral and cellular immune responses. The addition of dietary nucleotides normalizes both types of responses. RNA, a delivery form of nucleotides, and ribonucleotides were used in these studies. The mechanism of the immune-enhancing activity of nucleic acids/nucleotides is not clear."
+#
+loop_
+_pdbe_chem_comp_synonyms.comp_id
+_pdbe_chem_comp_synonyms.name
+_pdbe_chem_comp_synonyms.provenance
+_pdbe_chem_comp_synonyms.type
+A "5'-Adenosine monophosphate" DrugBank ?
+A "5'-Adenylic acid" DrugBank ?
+A "5'-AMP" DrugBank ?
+A "5'-O-Phosphonoadenosine" DrugBank ?
+A "Adenosine 5'-(dihydrogen phosphate)" DrugBank ?
+A "Adenosine 5'-monophosphate" DrugBank ?
+A "Adenosine 5'-phosphate" DrugBank ?
+A 'Adenosine monophosphate' DrugBank ?
+A 'Adenosine phosphate' DrugBank ?
+A "Adenosine-5'-Monophosphate" DrugBank ?
+A "Adenosine-5'-monophosphoric acid" DrugBank ?
+A "Adenosine-5'P" DrugBank ?
+A 'Adenosini phosphas' DrugBank ?
+A Adenylate DrugBank ?
+A 'Adenylic acid' DrugBank ?
+A "Ado5'P" DrugBank ?
+A AMP DrugBank ?
+A PAdo DrugBank ?
+A My-B-Den DrugBank 'International brand'
+#
+_pdbe_chem_comp_drugbank_classification.comp_id A
+_pdbe_chem_comp_drugbank_classification.drugbank_id DB00131
+_pdbe_chem_comp_drugbank_classification.parent 'Purine ribonucleoside monophosphates'
+_pdbe_chem_comp_drugbank_classification.kingdom 'Organic compounds'
+_pdbe_chem_comp_drugbank_classification.class 'Purine nucleotides'
+_pdbe_chem_comp_drugbank_classification.superclass 'Nucleosides, nucleotides, and analogues'
+_pdbe_chem_comp_drugbank_classification.description
+'This compound belongs to the class of organic compounds known as purine ribonucleoside monophosphates. These are nucleotides consisting of a purine base linked to a ribose to which one monophosphate group is attached.'
+#
+loop_
+_pdbe_chem_comp_drugbank_targets.comp_id
+_pdbe_chem_comp_drugbank_targets.drugbank_id
+_pdbe_chem_comp_drugbank_targets.name
+_pdbe_chem_comp_drugbank_targets.organism
+_pdbe_chem_comp_drugbank_targets.uniprot_id
+_pdbe_chem_comp_drugbank_targets.pharmacologically_active
+_pdbe_chem_comp_drugbank_targets.ordinal
+A DB00131 "5'-AMP-activated protein kinase catalytic subunit alpha-1" Humans Q13131 yes 1
+A DB00131 "5'-AMP-activated protein kinase catalytic subunit alpha-2" Humans P54646 yes 2
+A DB00131 "5'-AMP-activated protein kinase subunit beta-1" Humans Q9Y478 yes 3
+A DB00131 "5'-AMP-activated protein kinase subunit beta-2" Humans O43741 yes 4
+A DB00131 "5'-AMP-activated protein kinase subunit gamma-1" Humans P54619 yes 5
+A DB00131 "5'-AMP-activated protein kinase subunit gamma-2" Humans Q9UGJ0 yes 6
+A DB00131 "5'-AMP-activated protein kinase subunit gamma-3" Humans Q9UGI9 yes 7
+A DB00131 'Adenosine kinase' Humans P55263 unknown 8
+A DB00131 "5'-AMP-activated protein kinase subunit beta-1" Humans Q9Y478 unknown 9
+A DB00131 'Acetyl-coenzyme A synthetase, cytoplasmic' Humans Q9NR19 unknown 10
+A DB00131 "5'-AMP-activated protein kinase catalytic subunit alpha-1" Humans Q13131 unknown 11
+A DB00131 'Adenylate cyclase type 1' Humans Q08828 unknown 12
+A DB00131 'Long-chain-fatty-acid--CoA ligase 1' Humans P33121 unknown 13
+A DB00131 'Cyclic AMP-responsive element-binding protein 1' Humans P16220 unknown 14
+A DB00131 "cAMP-specific 3',5'-cyclic phosphodiesterase 4B" Humans Q07343 unknown 15
+A DB00131 'Histidine triad nucleotide-binding protein 1' Humans P49773 unknown 16
+A DB00131 "cAMP-specific 3',5'-cyclic phosphodiesterase 4D" Humans Q08499 unknown 17
+A DB00131 'Fructose-1,6-bisphosphatase 1' Humans P09467 unknown 18
+#
+loop_
+_software.name
+_software.version
+_software.description
+rdkit 2023.09.6 'Core functionality.'
+pdbeccdutils 0.8.6 'Wrapper to provide 2D templates and molecular fragments.'
+#
+loop_
+_pdbe_chem_comp_atom_depiction.comp_id
+_pdbe_chem_comp_atom_depiction.atom_id
+_pdbe_chem_comp_atom_depiction.element
+_pdbe_chem_comp_atom_depiction.model_Cartn_x
+_pdbe_chem_comp_atom_depiction.model_Cartn_y
+_pdbe_chem_comp_atom_depiction.pdbx_ordinal
+A OP3 O 7.110 6.615 1
+A P P 7.571 5.188 2
+A OP1 O 8.998 5.649 3
+A OP2 O 6.144 4.726 4
+A "O5'" O 8.032 3.760 5
+A "C5'" C 7.027 2.647 6
+A "C4'" C 7.487 1.220 7
+A "O4'" O 6.604 0.008 8
+A "C3'" C 8.913 0.754 9
+A "O3'" O 10.129 1.633 10
+A "C2'" C 8.911 -0.746 11
+A "O2'" O 10.123 -1.630 12
+A "C1'" C 7.483 -1.207 13
+A N9 N 7.018 -2.633 14
+A C8 C 7.893 -3.840 15
+A N7 N 7.018 -5.047 16
+A C5 C 5.598 -4.590 17
+A C6 C 4.299 -5.340 18
+A N6 N 4.299 -6.840 19
+A N1 N 3.000 -4.590 20
+A C2 C 3.000 -3.090 21
+A N3 N 4.299 -2.340 22
+A C4 C 5.598 -3.090 23
+#
+loop_
+_pdbe_chem_comp_bond_depiction.comp_id
+_pdbe_chem_comp_bond_depiction.atom_id_1
+_pdbe_chem_comp_bond_depiction.atom_id_2
+_pdbe_chem_comp_bond_depiction.value_order
+_pdbe_chem_comp_bond_depiction.bond_dir
+_pdbe_chem_comp_bond_depiction.pdbx_ordinal
+A OP3 P SINGLE NONE 1
+A P OP1 DOUBLE NONE 2
+A P OP2 SINGLE NONE 3
+A P "O5'" SINGLE NONE 4
+A "O5'" "C5'" SINGLE NONE 5
+A "C4'" "C5'" SINGLE BEGINDASH 6
+A "C4'" "O4'" SINGLE NONE 7
+A "C4'" "C3'" SINGLE NONE 8
+A "O4'" "C1'" SINGLE NONE 9
+A "C3'" "O3'" SINGLE BEGINWEDGE 10
+A "C3'" "C2'" SINGLE NONE 11
+A "C2'" "O2'" SINGLE BEGINWEDGE 12
+A "C2'" "C1'" SINGLE NONE 13
+A "C1'" N9 SINGLE BEGINDASH 14
+A N9 C8 SINGLE NONE 15
+A N9 C4 SINGLE NONE 16
+A C8 N7 DOUBLE NONE 17
+A N7 C5 SINGLE NONE 18
+A C5 C6 SINGLE NONE 19
+A C5 C4 DOUBLE NONE 20
+A C6 N6 SINGLE NONE 21
+A C6 N1 DOUBLE NONE 22
+A N1 C2 SINGLE NONE 23
+A C2 N3 DOUBLE NONE 24
+A N3 C4 SINGLE NONE 25
+#
+loop_
+_pdbe_chem_comp_substructure.comp_id
+_pdbe_chem_comp_substructure.substructure_name
+_pdbe_chem_comp_substructure.id
+_pdbe_chem_comp_substructure.substructure_type
+_pdbe_chem_comp_substructure.substructure_smiles
+_pdbe_chem_comp_substructure.substructure_inchis
+_pdbe_chem_comp_substructure.substructure_inchikeys
+A MurckoScaffold S1 scaffold 'c1ncc2ncn([C@H]3CCCO3)c2n1' InChI=1S/C9H10N4O/c1-2-8(14-3-1)13-6-12-7-4-10-5-11-9(7)13/h4-6,8H,1-3H2/t8-/m1/s1 DAKONNSVCLKUJN-MRVPVSSYSA-N
+A adenine F1 fragment 'Nc1ncnc2nc[nH]c12' InChI=1S/C5H5N5/c6-4-3-5(9-1-7-3)10-2-8-4/h1-2H,(H3,6,7,8,9,10) GFFGJBXGBJISGV-UHFFFAOYSA-N
+A imidazole F2 fragment 'c1c[nH]cn1' InChI=1S/C3H4N2/c1-2-5-3-4-1/h1-3H,(H,4,5) RAXXELZNTBOGNW-UHFFFAOYSA-N
+A phosphate F3 fragment O=P(O)(O)O InChI=1S/H3O4P/c1-5(2,3)4/h(H3,1,2,3,4) NBIIXXVUZAFLBC-UHFFFAOYSA-N
+A purine F4 fragment 'c1ncc2[nH]cnc2n1' InChI=1S/C5H4N4/c1-4-5(8-2-6-1)9-3-7-4/h1-3H,(H,6,7,8,9) KDCGOANMDULRCW-UHFFFAOYSA-N
+A pyrimidine F5 fragment c1cncnc1 InChI=1S/C4H4N2/c1-2-5-4-6-3-1/h1-4H CZPWVGJYEJSRLH-UHFFFAOYSA-N
+A ribose F6 fragment OCC1OCC(O)C1O InChI=1S/C5H10O4/c6-1-4-5(8)3(7)2-9-4/h3-8H,1-2H2 KZVAAIRBJJYZOW-UHFFFAOYSA-N
+#
+loop_
+_pdbe_chem_comp_substructure_mapping.comp_id
+_pdbe_chem_comp_substructure_mapping.atom_id
+_pdbe_chem_comp_substructure_mapping.substructure_id
+_pdbe_chem_comp_substructure_mapping.substructure_ordinal
+A "C4'" S1 1
+A "O4'" S1 1
+A "C3'" S1 1
+A "C2'" S1 1
+A "C1'" S1 1
+A N9 S1 1
+A C8 S1 1
+A N7 S1 1
+A C5 S1 1
+A C6 S1 1
+A N1 S1 1
+A C2 S1 1
+A N3 S1 1
+A C4 S1 1
+A N1 F1 1
+A C6 F1 1
+A C5 F1 1
+A C4 F1 1
+A N3 F1 1
+A C2 F1 1
+A N9 F1 1
+A C8 F1 1
+A N7 F1 1
+A N6 F1 1
+A C5 F2 1
+A C4 F2 1
+A N9 F2 1
+A C8 F2 1
+A N7 F2 1
+A OP3 F3 1
+A P F3 1
+A OP1 F3 1
+A OP2 F3 1
+A "O5'" F3 1
+A N7 F4 1
+A C8 F4 1
+A N9 F4 1
+A C4 F4 1
+A C5 F4 1
+A C6 F4 1
+A N1 F4 1
+A C2 F4 1
+A N3 F4 1
+A C5 F5 1
+A C6 F5 1
+A N1 F5 1
+A C2 F5 1
+A N3 F5 1
+A C4 F5 1
+A "C2'" F6 1
+A "C3'" F6 1
+A "C4'" F6 1
+A "O4'" F6 1
+A "C1'" F6 1
+A "C5'" F6 1
+A "O5'" F6 1
+A "O3'" F6 1
+A "O2'" F6 1
+#
+_pdbe_chem_comp_rdkit_properties.comp_id A
+_pdbe_chem_comp_rdkit_properties.exactmw 347.063
+_pdbe_chem_comp_rdkit_properties.amw 347.224
+_pdbe_chem_comp_rdkit_properties.lipinskiHBA 12
+_pdbe_chem_comp_rdkit_properties.lipinskiHBD 6
+_pdbe_chem_comp_rdkit_properties.NumRotatableBonds 9
+_pdbe_chem_comp_rdkit_properties.NumHBD 5
+_pdbe_chem_comp_rdkit_properties.NumHBA 12
+_pdbe_chem_comp_rdkit_properties.NumHeavyAtoms 23
+_pdbe_chem_comp_rdkit_properties.NumAtoms 37
+_pdbe_chem_comp_rdkit_properties.NumHeteroatoms 13
+_pdbe_chem_comp_rdkit_properties.NumAmideBonds 0
+_pdbe_chem_comp_rdkit_properties.FractionCSP3 0.500
+_pdbe_chem_comp_rdkit_properties.NumRings 3
+_pdbe_chem_comp_rdkit_properties.NumAromaticRings 2
+_pdbe_chem_comp_rdkit_properties.NumAliphaticRings 1
+_pdbe_chem_comp_rdkit_properties.NumSaturatedRings 1
+_pdbe_chem_comp_rdkit_properties.NumHeterocycles 3
+_pdbe_chem_comp_rdkit_properties.NumAromaticHeterocycles 2
+_pdbe_chem_comp_rdkit_properties.NumSaturatedHeterocycles 1
+_pdbe_chem_comp_rdkit_properties.NumAliphaticHeterocycles 1
+_pdbe_chem_comp_rdkit_properties.NumSpiroAtoms 0
+_pdbe_chem_comp_rdkit_properties.NumBridgeheadAtoms 0
+_pdbe_chem_comp_rdkit_properties.NumAtomStereoCenters 4
+_pdbe_chem_comp_rdkit_properties.NumUnspecifiedAtomStereoCenters 0
+_pdbe_chem_comp_rdkit_properties.labuteASA 148.436
+_pdbe_chem_comp_rdkit_properties.tpsa 186.070
+_pdbe_chem_comp_rdkit_properties.CrippenClogP -2.672
+_pdbe_chem_comp_rdkit_properties.CrippenMR 70.625
+_pdbe_chem_comp_rdkit_properties.chi0v 11.435
+_pdbe_chem_comp_rdkit_properties.chi1v 6.948
+_pdbe_chem_comp_rdkit_properties.chi2v 2.783
+_pdbe_chem_comp_rdkit_properties.chi3v 2.783
+_pdbe_chem_comp_rdkit_properties.chi4v 1.767
+_pdbe_chem_comp_rdkit_properties.chi0n 24.541
+_pdbe_chem_comp_rdkit_properties.chi1n 12.014
+_pdbe_chem_comp_rdkit_properties.chi2n 2.468
+_pdbe_chem_comp_rdkit_properties.chi3n 2.468
+_pdbe_chem_comp_rdkit_properties.chi4n 1.572
+_pdbe_chem_comp_rdkit_properties.hallKierAlpha -1.660
+_pdbe_chem_comp_rdkit_properties.kappa1 6.332
+_pdbe_chem_comp_rdkit_properties.kappa2 5.761
+_pdbe_chem_comp_rdkit_properties.kappa3 3.053
+_pdbe_chem_comp_rdkit_properties.Phi 1.586
+#
+loop_
+_pdbe_chem_comp_external_mappings.comp_id
+_pdbe_chem_comp_external_mappings.source
+_pdbe_chem_comp_external_mappings.resource
+_pdbe_chem_comp_external_mappings.resource_id
+A UniChem ChEMBL CHEMBL752
+A UniChem DrugBank DB00131
+A UniChem 'Guide to Pharmacology' 2455
+A UniChem 'KEGG LIGAND' C00020
+A UniChem ChEBI 16027
+A UniChem ZINC ZINC000003860156
+A UniChem eMolecules 711281
+A UniChem atlas 'adenosine monophosphate'
+A UniChem fdasrs 415SHH325A
+A UniChem SureChEMBL SCHEMBL18287224
+A UniChem SureChEMBL SCHEMBL5588
+A UniChem PharmGKB PA164744376
+A UniChem HMDB HMDB0000045
+A UniChem 'PubChem TPHARMA' 14778327
+A UniChem 'PubChem TPHARMA' 15447137
+A UniChem PubChem 6083
+A UniChem Mcule MCULE-3551661109
+A UniChem NMRShiftDB 60018778
+A UniChem LINCS LSM-5914
+A UniChem ACTor 13270-66-1
+A UniChem ACTor 162756-82-3
+A UniChem ACTor 24937-83-5
+A UniChem ACTor 34051-12-2
+A UniChem ACTor 55036-25-4
+A UniChem Nikkaji J4.814C
+A UniChem BindingDb 18137
+A UniChem 'EPA CompTox Dashboard' DTXSID5022560
+A UniChem DrugCentral 92
+A UniChem MetaboLights MTBLC16027
+A UniChem BRENDA 15563
+A UniChem BRENDA 15611
+A UniChem BRENDA 16056
+A UniChem BRENDA 16462
+A UniChem BRENDA 175495
+A UniChem BRENDA 176797
+A UniChem BRENDA 236
+A UniChem BRENDA 2942
+A UniChem BRENDA 30
+A UniChem BRENDA 43708
+A UniChem BRENDA 43765
+A UniChem BRENDA 45234
+A UniChem BRENDA 4780
+A UniChem BRENDA 6479
+A UniChem BRENDA 92278
+A UniChem DailyMed 'ADENOSINE PHOSPHATE'
+A UniChem ClinicalTrials "5'-ADENYLIC ACID"
+A UniChem ClinicalTrials 'A 5MP'
+A UniChem ClinicalTrials A-5MP
+A UniChem ClinicalTrials 'ADENOSINE PHOSPHATE'
+A UniChem rxnorm 'ADENOSINE MONOPHOSPHATE'
+A UniChem MedChemExpress HY-A0181
+A UniChem 'Probes And Drugs' PD010227
+#
+loop_
+_pdbe_chem_comp_rdkit_conformer.comp_id
+_pdbe_chem_comp_rdkit_conformer.atom_id
+_pdbe_chem_comp_rdkit_conformer.Cartn_x_rdkit
+_pdbe_chem_comp_rdkit_conformer.Cartn_y_rdkit
+_pdbe_chem_comp_rdkit_conformer.Cartn_z_rdkit
+_pdbe_chem_comp_rdkit_conformer.rdkit_method
+_pdbe_chem_comp_rdkit_conformer.rdkit_ordinal
+A OP3 3.900 -0.010 2.620 ETKDGv3 1
+A P 4.279 -1.335 1.637 ETKDGv3 2
+A OP1 3.820 -2.609 2.311 ETKDGv3 3
+A OP2 5.955 -1.402 1.420 ETKDGv3 4
+A "O5'" 3.528 -1.176 0.114 ETKDGv3 5
+A "C5'" 2.137 -1.079 0.310 ETKDGv3 6
+A "C4'" 1.440 -0.888 -1.043 ETKDGv3 7
+A "O4'" 0.042 -0.942 -0.849 ETKDGv3 8
+A "C3'" 1.800 0.452 -1.695 ETKDGv3 9
+A "O3'" 1.929 0.321 -3.093 ETKDGv3 10
+A "C2'" 0.634 1.330 -1.311 ETKDGv3 11
+A "O2'" 0.331 2.278 -2.302 ETKDGv3 12
+A "C1'" -0.504 0.329 -1.141 ETKDGv3 13
+A N9 -1.412 0.741 -0.069 ETKDGv3 14
+A C8 -2.065 2.018 0.035 ETKDGv3 15
+A N7 -3.184 1.990 0.698 ETKDGv3 16
+A C5 -3.337 0.625 1.061 ETKDGv3 17
+A C6 -4.485 -0.044 1.686 ETKDGv3 18
+A N6 -5.597 0.699 2.184 ETKDGv3 19
+A N1 -4.466 -1.348 1.769 ETKDGv3 20
+A C2 -3.352 -2.118 1.253 ETKDGv3 21
+A N3 -2.337 -1.519 0.686 ETKDGv3 22
+A C4 -2.343 -0.088 0.606 ETKDGv3 23
+A HOP3 4.172 0.782 2.091 ETKDGv3 24
+A HOP2 6.178 -0.573 0.928 ETKDGv3 25
+A "H5'" 1.880 -0.217 0.968 ETKDGv3 26
+A "H5''" 1.746 -2.017 0.768 ETKDGv3 27
+A "H4'" 1.739 -1.729 -1.707 ETKDGv3 28
+A "H3'" 2.748 0.877 -1.297 ETKDGv3 29
+A "HO3'" 1.090 -0.077 -3.447 ETKDGv3 30
+A "H2'" 0.851 1.834 -0.339 ETKDGv3 31
+A "HO2'" 1.034 2.977 -2.245 ETKDGv3 32
+A "H1'" -1.084 0.246 -2.088 ETKDGv3 33
+A H8 -1.681 2.923 -0.415 ETKDGv3 34
+A H61 -6.407 0.207 2.622 ETKDGv3 35
+A H62 -5.614 1.741 2.118 ETKDGv3 36
+A H2 -3.366 -3.197 1.327 ETKDGv3 37
+#

nucleotides/A.pdb

@@ -0,0 +1,75 @@
+HETATM    1  OP3   A     0       2.135  -1.141  -5.313  0.00  0.00           O  
+HETATM    2  P     A     0       1.024  -0.137  -4.723  0.00  0.00           P  
+HETATM    3  OP1   A     0       1.633   1.190  -4.488  0.00  0.00           O  
+HETATM    4  OP2   A     0      -0.183   0.005  -5.778  0.00  0.00           O  
+HETATM    5  O5'   A     0       0.456  -0.720  -3.334  0.00  0.00           O  
+HETATM    6  C5'   A     0      -0.520   0.209  -2.863  0.00  0.00           C  
+HETATM    7  C4'   A     0      -1.101  -0.287  -1.538  0.00  0.00           C  
+HETATM    8  O4'   A     0      -0.064  -0.383  -0.538  0.00  0.00           O  
+HETATM    9  C3'   A     0      -2.105   0.739  -0.969  0.00  0.00           C  
+HETATM   10  O3'   A     0      -3.445   0.360  -1.287  0.00  0.00           O  
+HETATM   11  C2'   A     0      -1.874   0.684   0.558  0.00  0.00           C  
+HETATM   12  O2'   A     0      -3.065   0.271   1.231  0.00  0.00           O  
+HETATM   13  C1'   A     0      -0.755  -0.367   0.729  0.00  0.00           C  
+HETATM   14  N9    A     0       0.158   0.029   1.803  0.00  0.00           N  
+HETATM   15  C8    A     0       1.265   0.813   1.672  0.00  0.00           C  
+HETATM   16  N7    A     0       1.843   0.963   2.828  0.00  0.00           N  
+HETATM   17  C5    A     0       1.143   0.292   3.773  0.00  0.00           C  
+HETATM   18  C6    A     0       1.290   0.091   5.156  0.00  0.00           C  
+HETATM   19  N6    A     0       2.344   0.664   5.846  0.00  0.00           N  
+HETATM   20  N1    A     0       0.391  -0.656   5.787  0.00  0.00           N  
+HETATM   21  C2    A     0      -0.617  -1.206   5.136  0.00  0.00           C  
+HETATM   22  N3    A     0      -0.792  -1.051   3.841  0.00  0.00           N  
+HETATM   23  C4    A     0       0.056  -0.320   3.126  0.00  0.00           C  
+HETATM   24 HOP3   A     0       2.448  -0.755  -6.142  0.00  0.00           H  
+HETATM   25 HOP2   A     0      -0.552  -0.879  -5.902  0.00  0.00           H  
+HETATM   26  H5'   A     0      -1.319   0.301  -3.599  0.00  0.00           H  
+HETATM   27 H5''   A     0      -0.052   1.182  -2.712  0.00  0.00           H  
+HETATM   28  H4'   A     0      -1.586  -1.254  -1.677  0.00  0.00           H  
+HETATM   29  H3'   A     0      -1.890   1.736  -1.353  0.00  0.00           H  
+HETATM   30 HO3'   A     0      -4.024   1.035  -0.908  0.00  0.00           H  
+HETATM   31  H2'   A     0      -1.543   1.654   0.930  0.00  0.00           H  
+HETATM   32 HO2'   A     0      -3.740   0.936   1.037  0.00  0.00           H  
+HETATM   33  H1'   A     0      -1.185  -1.346   0.940  0.00  0.00           H  
+HETATM   34  H8    A     0       1.611   1.246   0.745  0.00  0.00           H  
+HETATM   35  H61   A     0       2.432   0.522   6.801  0.00  0.00           H  
+HETATM   36  H62   A     0       2.996   1.205   5.374  0.00  0.00           H  
+HETATM   37  H2    A     0      -1.325  -1.807   5.688  0.00  0.00           H  
+CONECT    1    2   24
+CONECT    2    1    3    4    5
+CONECT    3    2
+CONECT    4    2   25
+CONECT    5    2    6
+CONECT    6    5    7   26   27
+CONECT    7    6    8    9   28
+CONECT    8    7   13
+CONECT    9    7   10   11   29
+CONECT   10    9   30
+CONECT   11    9   12   13   31
+CONECT   12   11   32
+CONECT   13    8   11   14   33
+CONECT   14   13   15   23
+CONECT   15   14   16   34
+CONECT   16   15   17
+CONECT   17   16   18   23
+CONECT   18   17   19   20
+CONECT   19   18   35   36
+CONECT   20   18   21
+CONECT   21   20   22   37
+CONECT   22   21   23
+CONECT   23   14   17   22
+CONECT   24    1
+CONECT   25    4
+CONECT   26    6
+CONECT   27    6
+CONECT   28    7
+CONECT   29    9
+CONECT   30   10
+CONECT   31   11
+CONECT   32   12
+CONECT   33   13
+CONECT   34   15
+CONECT   35   19
+CONECT   36   19
+CONECT   37   21
+END

nucleotides/C.cif

@@ -0,0 +1,453 @@
+data_C
+#
+_chem_comp.id C
+_chem_comp.name "CYTIDINE-5'-MONOPHOSPHATE"
+_chem_comp.type "RNA LINKING"
+_chem_comp.pdbx_type ATOMN
+_chem_comp.formula "C9 H14 N3 O8 P"
+_chem_comp.mon_nstd_parent_comp_id ?
+_chem_comp.pdbx_synonyms ?
+_chem_comp.pdbx_formal_charge 0
+_chem_comp.pdbx_initial_date 1999-07-08
+_chem_comp.pdbx_modified_date 2011-06-04
+_chem_comp.pdbx_ambiguous_flag N
+_chem_comp.pdbx_release_status REL
+_chem_comp.pdbx_replaced_by ?
+_chem_comp.pdbx_replaces ?
+_chem_comp.formula_weight 323.197
+_chem_comp.one_letter_code C
+_chem_comp.three_letter_code C
+_chem_comp.pdbx_model_coordinates_details ?
+_chem_comp.pdbx_model_coordinates_missing_flag N
+_chem_comp.pdbx_ideal_coordinates_details ?
+_chem_comp.pdbx_ideal_coordinates_missing_flag N
+_chem_comp.pdbx_model_coordinates_db_code ?
+_chem_comp.pdbx_subcomponent_list ?
+_chem_comp.pdbx_processing_site RCSB
+#
+loop_
+_chem_comp_atom.comp_id
+_chem_comp_atom.atom_id
+_chem_comp_atom.alt_atom_id
+_chem_comp_atom.type_symbol
+_chem_comp_atom.charge
+_chem_comp_atom.pdbx_align
+_chem_comp_atom.pdbx_aromatic_flag
+_chem_comp_atom.pdbx_leaving_atom_flag
+_chem_comp_atom.pdbx_stereo_config
+_chem_comp_atom.pdbx_backbone_atom_flag
+_chem_comp_atom.pdbx_n_terminal_atom_flag
+_chem_comp_atom.pdbx_c_terminal_atom_flag
+_chem_comp_atom.model_Cartn_x
+_chem_comp_atom.model_Cartn_y
+_chem_comp_atom.model_Cartn_z
+_chem_comp_atom.pdbx_model_Cartn_x_ideal
+_chem_comp_atom.pdbx_model_Cartn_y_ideal
+_chem_comp_atom.pdbx_model_Cartn_z_ideal
+_chem_comp_atom.pdbx_component_atom_id
+_chem_comp_atom.pdbx_component_comp_id
+_chem_comp_atom.pdbx_ordinal
+C OP3 O3P O 0 1 N Y N N N N 26.803 20.514 -11.017 2.147 -1.021 -4.678 OP3 C 1
+C P P P 0 1 N N N N N N 27.386 20.433 -12.503 1.049 -0.039 -4.028 P C 2
+C OP1 O1P O 0 1 N N N N N N 26.539 21.293 -13.322 1.692 1.237 -3.646 OP1 C 3
+C OP2 O2P O 0 1 N N N N N N 27.570 19.015 -12.877 -0.116 0.246 -5.102 OP2 C 4
+C "O5'" O5* O 0 1 N N N N N N 28.830 21.055 -12.361 0.415 -0.733 -2.721 "O5'" C 5
+C "C5'" C5* C 0 1 N N N N N N 29.051 22.423 -11.973 -0.546 0.181 -2.193 "C5'" C 6
+C "C4'" C4* C 0 1 N N R N N N 30.525 22.652 -11.850 -1.189 -0.419 -0.942 "C4'" C 7
+C "O4'" O4* O 0 1 N N N N N N 30.993 22.001 -10.600 -0.190 -0.648 0.076 "O4'" C 8
+C "C3'" C3* C 0 1 N N S N N N 31.457 22.096 -12.933 -2.178 0.583 -0.307 "C3'" C 9
+C "O3'" O3* O 0 1 N N N N N N 31.346 22.915 -14.074 -3.518 0.283 -0.703 "O3'" C 10
+C "C2'" C2* C 0 1 N N R N N N 32.751 22.157 -12.194 -2.001 0.373 1.215 "C2'" C 11
+C "O2'" O2* O 0 1 N N N N N N 33.186 23.463 -12.031 -3.228 -0.059 1.806 "O2'" C 12
+C "C1'" C1* C 0 1 N N R N N N 32.361 21.627 -10.851 -0.924 -0.729 1.317 "C1'" C 13
+C N1 N1 N 0 1 N N N N N N 32.476 20.131 -10.779 -0.036 -0.470 2.453 N1 C 14
+C C2 C2 C 0 1 N N N N N N 33.674 19.589 -10.493 0.652 0.683 2.514 C2 C 15
+C O2 O2 O 0 1 N N N N N N 34.680 20.354 -10.277 0.529 1.504 1.620 O2 C 16
+C N3 N3 N 0 1 N N N N N N 33.855 18.230 -10.434 1.467 0.945 3.535 N3 C 17
+C C4 C4 C 0 1 N N N N N N 32.804 17.495 -10.663 1.620 0.070 4.520 C4 C 18
+C N4 N4 N 0 1 N N N N N N 32.905 16.139 -10.606 2.464 0.350 5.569 N4 C 19
+C C5 C5 C 0 1 N N N N N N 31.488 18.044 -10.975 0.916 -1.151 4.483 C5 C 20
+C C6 C6 C 0 1 N N N N N N 31.389 19.360 -11.041 0.087 -1.399 3.442 C6 C 21
+C HOP3 3HOP H 0 0 N N N N N N 27.354 19.953 -10.483 2.501 -0.569 -5.456 HOP3 C 22
+C HOP2 2HOP H 0 0 N N N N N N 28.121 18.454 -12.343 -0.508 -0.608 -5.323 HOP2 C 23
+C "H5'" 1H5* H 0 1 N N N N N N 28.562 23.147 -12.665 -1.315 0.371 -2.941 "H5'" C 24
+C "H5''" 2H5* H 0 0 N N N N N N 28.496 22.699 -11.045 -0.052 1.118 -1.933 "H5''" C 25
+C "H4'" H4* H 0 1 N N N N N N 30.596 23.763 -11.911 -1.699 -1.350 -1.188 "H4'" C 26
+C "H3'" H3* H 0 1 N N N N N N 31.269 21.074 -13.339 -1.917 1.604 -0.586 "H3'" C 27
+C "HO3'" H3T H 0 0 N Y N N N N 31.923 22.570 -14.745 -4.088 0.939 -0.278 "HO3'" C 28
+C "H2'" H2* H 0 1 N N N N N N 33.568 21.607 -12.716 -1.653 1.290 1.689 "H2'" C 29
+C "HO2'" 2HO* H 0 0 N N N N N N 34.010 23.501 -11.560 -3.874 0.644 1.656 "HO2'" C 30
+C "H1'" H1* H 0 1 N N N N N N 33.051 22.057 -10.088 -1.392 -1.708 1.418 "H1'" C 31
+C H41 1H4 H 0 1 N N N N N N 33.824 15.755 -10.388 2.950 1.189 5.590 H41 C 32
+C H42 2H4 H 0 1 N N N N N N 32.564 15.734 -11.478 2.571 -0.289 6.290 H42 C 33
+C H5 H5 H 0 1 N N N N N N 30.568 17.464 -11.160 1.030 -1.873 5.278 H5 C 34
+C H6 H6 H 0 1 N N N N N N 30.417 19.806 -11.309 -0.465 -2.326 3.393 H6 C 35
+#
+loop_
+_chem_comp_bond.comp_id
+_chem_comp_bond.atom_id_1
+_chem_comp_bond.atom_id_2
+_chem_comp_bond.value_order
+_chem_comp_bond.pdbx_aromatic_flag
+_chem_comp_bond.pdbx_stereo_config
+_chem_comp_bond.pdbx_ordinal
+C OP3 P SING N N 1
+C OP3 HOP3 SING N N 2
+C P OP1 DOUB N N 3
+C P OP2 SING N N 4
+C P "O5'" SING N N 5
+C OP2 HOP2 SING N N 6
+C "O5'" "C5'" SING N N 7
+C "C5'" "C4'" SING N N 8
+C "C5'" "H5'" SING N N 9
+C "C5'" "H5''" SING N N 10
+C "C4'" "O4'" SING N N 11
+C "C4'" "C3'" SING N N 12
+C "C4'" "H4'" SING N N 13
+C "O4'" "C1'" SING N N 14
+C "C3'" "O3'" SING N N 15
+C "C3'" "C2'" SING N N 16
+C "C3'" "H3'" SING N N 17
+C "O3'" "HO3'" SING N N 18
+C "C2'" "O2'" SING N N 19
+C "C2'" "C1'" SING N N 20
+C "C2'" "H2'" SING N N 21
+C "O2'" "HO2'" SING N N 22
+C "C1'" N1 SING N N 23
+C "C1'" "H1'" SING N N 24
+C N1 C2 SING N N 25
+C N1 C6 SING N N 26
+C C2 O2 DOUB N N 27
+C C2 N3 SING N N 28
+C N3 C4 DOUB N N 29
+C C4 N4 SING N N 30
+C C4 C5 SING N N 31
+C N4 H41 SING N N 32
+C N4 H42 SING N N 33
+C C5 C6 DOUB N N 34
+C C5 H5 SING N N 35
+C C6 H6 SING N N 36
+#
+loop_
+_pdbx_chem_comp_descriptor.comp_id
+_pdbx_chem_comp_descriptor.type
+_pdbx_chem_comp_descriptor.program
+_pdbx_chem_comp_descriptor.program_version
+_pdbx_chem_comp_descriptor.descriptor
+C SMILES ACDLabs 10.04 O=C1N=C(N)C=CN1C2OC(C(O)C2O)COP(=O)(O)O
+C SMILES_CANONICAL CACTVS 3.341 NC1=NC(=O)N(C=C1)[C@@H]2O[C@H](CO[P](O)(O)=O)[C@@H](O)[C@H]2O
+C SMILES CACTVS 3.341 NC1=NC(=O)N(C=C1)[CH]2O[CH](CO[P](O)(O)=O)[CH](O)[CH]2O
+C SMILES_CANONICAL "OpenEye OEToolkits" 1.5.0 C1=CN(C(=O)N=C1N)[C@H]2[C@@H]([C@@H]([C@H](O2)COP(=O)(O)O)O)O
+C SMILES "OpenEye OEToolkits" 1.5.0 C1=CN(C(=O)N=C1N)C2C(C(C(O2)COP(=O)(O)O)O)O
+C InChI InChI 1.03 InChI=1S/C9H14N3O8P/c10-5-1-2-12(9(15)11-5)8-7(14)6(13)4(20-8)3-19-21(16,17)18/h1-2,4,6-8,13-14H,3H2,(H2,10,11,15)(H2,16,17,18)/t4-,6-,7-,8-/m1/s1
+C InChIKey InChI 1.03 IERHLVCPSMICTF-XVFCMESISA-N
+#
+loop_
+_pdbx_chem_comp_identifier.comp_id
+_pdbx_chem_comp_identifier.type
+_pdbx_chem_comp_identifier.program
+_pdbx_chem_comp_identifier.program_version
+_pdbx_chem_comp_identifier.identifier
+C "SYSTEMATIC NAME" ACDLabs 10.04 "5'-cytidylic acid"
+C "SYSTEMATIC NAME" "OpenEye OEToolkits" 1.5.0 "[(2R,3S,4R,5R)-5-(4-amino-2-oxo-pyrimidin-1-yl)-3,4-dihydroxy-oxolan-2-yl]methyl dihydrogen phosphate"
+#
+loop_
+_pdbx_chem_comp_audit.comp_id
+_pdbx_chem_comp_audit.action_type
+_pdbx_chem_comp_audit.date
+_pdbx_chem_comp_audit.processing_site
+C 'Create component' 1999-07-08 RCSB
+C 'Modify descriptor' 2011-06-04 RCSB
+#
+_pdbe_chem_comp_drugbank_details.comp_id C
+_pdbe_chem_comp_drugbank_details.drugbank_id DB03403
+_pdbe_chem_comp_drugbank_details.type 'small molecule'
+_pdbe_chem_comp_drugbank_details.name "Cytidine-5'-Monophosphate"
+_pdbe_chem_comp_drugbank_details.description
+"A pyrimidine ribonucleoside 5'-monophosphate having cytosine as the nucleobase."
+_pdbe_chem_comp_drugbank_details.cas_number 63-37-6
+_pdbe_chem_comp_drugbank_details.mechanism_of_action ?
+#
+loop_
+_pdbe_chem_comp_synonyms.comp_id
+_pdbe_chem_comp_synonyms.name
+_pdbe_chem_comp_synonyms.provenance
+_pdbe_chem_comp_synonyms.type
+C "5'-CMP" DrugBank ?
+C CMP DrugBank ?
+C 'Cytidine monophosphate' DrugBank ?
+C cytidylate DrugBank ?
+C 'Cytidylic acid' DrugBank ?
+C pC DrugBank ?
+#
+_pdbe_chem_comp_drugbank_classification.comp_id C
+_pdbe_chem_comp_drugbank_classification.drugbank_id DB03403
+_pdbe_chem_comp_drugbank_classification.parent 'Pyrimidine ribonucleoside monophosphates'
+_pdbe_chem_comp_drugbank_classification.kingdom 'Organic compounds'
+_pdbe_chem_comp_drugbank_classification.class 'Pyrimidine nucleotides'
+_pdbe_chem_comp_drugbank_classification.superclass 'Nucleosides, nucleotides, and analogues'
+_pdbe_chem_comp_drugbank_classification.description
+'This compound belongs to the class of organic compounds known as pyrimidine ribonucleoside monophosphates. These are pyrimidine ribobucleotides with monophosphate group  linked to the ribose moiety.'
+#
+loop_
+_pdbe_chem_comp_drugbank_targets.comp_id
+_pdbe_chem_comp_drugbank_targets.drugbank_id
+_pdbe_chem_comp_drugbank_targets.name
+_pdbe_chem_comp_drugbank_targets.organism
+_pdbe_chem_comp_drugbank_targets.uniprot_id
+_pdbe_chem_comp_drugbank_targets.pharmacologically_active
+_pdbe_chem_comp_drugbank_targets.ordinal
+C DB03403 'Uridine-cytidine kinase 2' Humans Q9BZX2 unknown 1
+C DB03403 '3-deoxy-manno-octulosonate cytidylyltransferase' 'Escherichia coli' P42216 unknown 2
+C DB03403 '2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase' 'Shigella flexneri' P62619 unknown 3
+C DB03403 'Bifunctional enzyme IspD/IspF' 'Campylobacter jejuni subsp. jejuni serotype O:2 (strain ATCC 700819 / NCTC 11168)' Q9PM68 unknown 4
+C DB03403 'UMP-CMP kinase' Humans P30085 unknown 5
+C DB03403 '2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase' 'Thermus thermophilus (strain HB8 / ATCC 27634 / DSM 579)' Q8RQP5 unknown 6
+C DB03403 'Cytidylate kinase' 'Escherichia coli (strain K12)' P0A6I0 unknown 7
+C DB03403 'Coenzyme A biosynthesis bifunctional protein CoaBC' 'Escherichia coli (strain K12)' P0ABQ0 unknown 8
+C DB03403 Alpha-2,3-/2,8-sialyltransferase 'Campylobacter jejuni' Q9LAK3 unknown 9
+#
+loop_
+_software.name
+_software.version
+_software.description
+rdkit 2023.09.6 'Core functionality.'
+pdbeccdutils 0.8.6 'Wrapper to provide 2D templates and molecular fragments.'
+#
+loop_
+_pdbe_chem_comp_atom_depiction.comp_id
+_pdbe_chem_comp_atom_depiction.atom_id
+_pdbe_chem_comp_atom_depiction.element
+_pdbe_chem_comp_atom_depiction.model_Cartn_x
+_pdbe_chem_comp_atom_depiction.model_Cartn_y
+_pdbe_chem_comp_atom_depiction.pdbx_ordinal
+C OP3 O 11.218 -6.129 1
+C P P 10.336 -4.915 2
+C OP1 O 11.550 -4.033 3
+C OP2 O 9.123 -5.797 4
+C "O5'" O 9.455 -3.701 5
+C "C5'" C 7.963 -3.858 6
+C "C4'" C 7.081 -2.645 7
+C "O4'" O 7.545 -1.218 8
+C "C3'" C 5.581 -2.645 9
+C "O3'" O 4.699 -3.858 10
+C "C2'" C 5.118 -1.218 11
+C "O2'" O 3.691 -0.755 12
+C "C1'" C 6.331 -0.337 13
+C N1 N 6.331 1.163 14
+C C2 C 7.630 1.914 15
+C O2 O 8.929 1.163 16
+C N3 N 7.630 3.413 17
+C C4 C 6.331 4.163 18
+C N4 N 6.331 5.663 19
+C C5 C 5.032 3.413 20
+C C6 C 5.032 1.914 21
+#
+loop_
+_pdbe_chem_comp_bond_depiction.comp_id
+_pdbe_chem_comp_bond_depiction.atom_id_1
+_pdbe_chem_comp_bond_depiction.atom_id_2
+_pdbe_chem_comp_bond_depiction.value_order
+_pdbe_chem_comp_bond_depiction.bond_dir
+_pdbe_chem_comp_bond_depiction.pdbx_ordinal
+C OP3 P SINGLE NONE 1
+C P OP1 DOUBLE NONE 2
+C P OP2 SINGLE NONE 3
+C P "O5'" SINGLE NONE 4
+C "O5'" "C5'" SINGLE NONE 5
+C "C4'" "C5'" SINGLE BEGINDASH 6
+C "C4'" "O4'" SINGLE NONE 7
+C "C4'" "C3'" SINGLE NONE 8
+C "O4'" "C1'" SINGLE NONE 9
+C "C3'" "O3'" SINGLE BEGINWEDGE 10
+C "C3'" "C2'" SINGLE NONE 11
+C "C2'" "O2'" SINGLE BEGINWEDGE 12
+C "C2'" "C1'" SINGLE NONE 13
+C "C1'" N1 SINGLE BEGINDASH 14
+C N1 C2 SINGLE NONE 15
+C N1 C6 SINGLE NONE 16
+C C2 O2 DOUBLE NONE 17
+C C2 N3 SINGLE NONE 18
+C N3 C4 DOUBLE NONE 19
+C C4 N4 SINGLE NONE 20
+C C4 C5 SINGLE NONE 21
+C C5 C6 DOUBLE NONE 22
+#
+loop_
+_pdbe_chem_comp_substructure.comp_id
+_pdbe_chem_comp_substructure.substructure_name
+_pdbe_chem_comp_substructure.id
+_pdbe_chem_comp_substructure.substructure_type
+_pdbe_chem_comp_substructure.substructure_smiles
+_pdbe_chem_comp_substructure.substructure_inchis
+_pdbe_chem_comp_substructure.substructure_inchikeys
+C MurckoScaffold S1 scaffold 'O=c1ncccn1[C@H]1CCCO1' InChI=1S/C8H10N2O2/c11-8-9-4-2-5-10(8)7-3-1-6-12-7/h2,4-5,7H,1,3,6H2/t7-/m1/s1 AKXKPKRAUWTVBW-SSDOTTSWSA-N
+C phosphate F1 fragment O=P(O)(O)O InChI=1S/H3O4P/c1-5(2,3)4/h(H3,1,2,3,4) NBIIXXVUZAFLBC-UHFFFAOYSA-N
+C pyrimidine F2 fragment c1cncnc1 InChI=1S/C4H4N2/c1-2-5-4-6-3-1/h1-4H CZPWVGJYEJSRLH-UHFFFAOYSA-N
+C ribose F3 fragment OCC1OCC(O)C1O InChI=1S/C5H10O4/c6-1-4-5(8)3(7)2-9-4/h3-8H,1-2H2 KZVAAIRBJJYZOW-UHFFFAOYSA-N
+#
+loop_
+_pdbe_chem_comp_substructure_mapping.comp_id
+_pdbe_chem_comp_substructure_mapping.atom_id
+_pdbe_chem_comp_substructure_mapping.substructure_id
+_pdbe_chem_comp_substructure_mapping.substructure_ordinal
+C "C4'" S1 1
+C "O4'" S1 1
+C "C3'" S1 1
+C "C2'" S1 1
+C "C1'" S1 1
+C N1 S1 1
+C C2 S1 1
+C O2 S1 1
+C N3 S1 1
+C C4 S1 1
+C C5 S1 1
+C C6 S1 1
+C OP3 F1 1
+C P F1 1
+C OP1 F1 1
+C OP2 F1 1
+C "O5'" F1 1
+C C5 F2 1
+C C4 F2 1
+C N3 F2 1
+C C2 F2 1
+C N1 F2 1
+C C6 F2 1
+C "C2'" F3 1
+C "C3'" F3 1
+C "C4'" F3 1
+C "O4'" F3 1
+C "C1'" F3 1
+C "C5'" F3 1
+C "O5'" F3 1
+C "O3'" F3 1
+C "O2'" F3 1
+#
+_pdbe_chem_comp_rdkit_properties.comp_id C
+_pdbe_chem_comp_rdkit_properties.exactmw 323.052
+_pdbe_chem_comp_rdkit_properties.amw 323.198
+_pdbe_chem_comp_rdkit_properties.lipinskiHBA 11
+_pdbe_chem_comp_rdkit_properties.lipinskiHBD 6
+_pdbe_chem_comp_rdkit_properties.NumRotatableBonds 9
+_pdbe_chem_comp_rdkit_properties.NumHBD 5
+_pdbe_chem_comp_rdkit_properties.NumHBA 11
+_pdbe_chem_comp_rdkit_properties.NumHeavyAtoms 21
+_pdbe_chem_comp_rdkit_properties.NumAtoms 35
+_pdbe_chem_comp_rdkit_properties.NumHeteroatoms 12
+_pdbe_chem_comp_rdkit_properties.NumAmideBonds 0
+_pdbe_chem_comp_rdkit_properties.FractionCSP3 0.556
+_pdbe_chem_comp_rdkit_properties.NumRings 2
+_pdbe_chem_comp_rdkit_properties.NumAromaticRings 1
+_pdbe_chem_comp_rdkit_properties.NumAliphaticRings 1
+_pdbe_chem_comp_rdkit_properties.NumSaturatedRings 1
+_pdbe_chem_comp_rdkit_properties.NumHeterocycles 2
+_pdbe_chem_comp_rdkit_properties.NumAromaticHeterocycles 1
+_pdbe_chem_comp_rdkit_properties.NumSaturatedHeterocycles 1
+_pdbe_chem_comp_rdkit_properties.NumAliphaticHeterocycles 1
+_pdbe_chem_comp_rdkit_properties.NumSpiroAtoms 0
+_pdbe_chem_comp_rdkit_properties.NumBridgeheadAtoms 0
+_pdbe_chem_comp_rdkit_properties.NumAtomStereoCenters 4
+_pdbe_chem_comp_rdkit_properties.NumUnspecifiedAtomStereoCenters 0
+_pdbe_chem_comp_rdkit_properties.labuteASA 137.487
+_pdbe_chem_comp_rdkit_properties.tpsa 177.360
+_pdbe_chem_comp_rdkit_properties.CrippenClogP -1.927
+_pdbe_chem_comp_rdkit_properties.CrippenMR 66.113
+_pdbe_chem_comp_rdkit_properties.chi0v 10.449
+_pdbe_chem_comp_rdkit_properties.chi1v 6.257
+_pdbe_chem_comp_rdkit_properties.chi2v 2.246
+_pdbe_chem_comp_rdkit_properties.chi3v 2.246
+_pdbe_chem_comp_rdkit_properties.chi4v 1.356
+_pdbe_chem_comp_rdkit_properties.chi0n 23.555
+_pdbe_chem_comp_rdkit_properties.chi1n 11.324
+_pdbe_chem_comp_rdkit_properties.chi2n 1.931
+_pdbe_chem_comp_rdkit_properties.chi3n 1.931
+_pdbe_chem_comp_rdkit_properties.chi4n 1.161
+_pdbe_chem_comp_rdkit_properties.hallKierAlpha -1.330
+_pdbe_chem_comp_rdkit_properties.kappa1 5.704
+_pdbe_chem_comp_rdkit_properties.kappa2 5.812
+_pdbe_chem_comp_rdkit_properties.kappa3 3.656
+_pdbe_chem_comp_rdkit_properties.Phi 1.579
+#
+loop_
+_pdbe_chem_comp_external_mappings.comp_id
+_pdbe_chem_comp_external_mappings.source
+_pdbe_chem_comp_external_mappings.resource
+_pdbe_chem_comp_external_mappings.resource_id
+C UniChem ChEMBL CHEMBL307679
+C UniChem DrugBank DB03403
+C UniChem 'KEGG LIGAND' C00055
+C UniChem ChEBI 17361
+C UniChem ZINC ZINC000003861744
+C UniChem eMolecules 496251
+C UniChem fdasrs F469818O25
+C UniChem SureChEMBL SCHEMBL16189
+C UniChem HMDB HMDB0000095
+C UniChem 'PubChem TPHARMA' 14752809
+C UniChem 'PubChem TPHARMA' 14801515
+C UniChem PubChem 6131
+C UniChem NMRShiftDB 60021148
+C UniChem ACTor 162756-87-8
+C UniChem Nikkaji J60.869F
+C UniChem BindingDb 50310540
+C UniChem 'EPA CompTox Dashboard' DTXSID50889322
+C UniChem MetaboLights MTBLC17361
+C UniChem BRENDA 100
+C UniChem BRENDA 102643
+C UniChem BRENDA 105789
+C UniChem BRENDA 124491
+C UniChem BRENDA 1572
+C UniChem BRENDA 16658
+C UniChem BRENDA 210730
+C UniChem BRENDA 24116
+C UniChem BRENDA 24532
+C UniChem BRENDA 47166
+C UniChem BRENDA 67069
+C UniChem MedChemExpress HY-W009162
+C UniChem 'Probes And Drugs' PD059654
+C UniChem CCDC WIWYIO
+#
+loop_
+_pdbe_chem_comp_rdkit_conformer.comp_id
+_pdbe_chem_comp_rdkit_conformer.atom_id
+_pdbe_chem_comp_rdkit_conformer.Cartn_x_rdkit
+_pdbe_chem_comp_rdkit_conformer.Cartn_y_rdkit
+_pdbe_chem_comp_rdkit_conformer.Cartn_z_rdkit
+_pdbe_chem_comp_rdkit_conformer.rdkit_method
+_pdbe_chem_comp_rdkit_conformer.rdkit_ordinal
+C OP3 3.558 2.639 -1.696 ETKDGv3 1
+C P 3.829 2.734 -0.025 ETKDGv3 2
+C OP1 5.159 2.098 0.319 ETKDGv3 3
+C OP2 3.865 4.363 0.430 ETKDGv3 4
+C "O5'" 2.573 1.958 0.829 ETKDGv3 5
+C "C5'" 2.479 0.627 0.377 ETKDGv3 6
+C "C4'" 1.396 -0.098 1.180 ETKDGv3 7
+C "O4'" 0.146 0.515 0.967 ETKDGv3 8
+C "C3'" 1.271 -1.561 0.778 ETKDGv3 9
+C "O3'" 2.166 -2.361 1.507 ETKDGv3 10
+C "C2'" -0.181 -1.868 1.091 ETKDGv3 11
+C "O2'" -0.336 -2.574 2.301 ETKDGv3 12
+C "C1'" -0.838 -0.479 1.165 ETKDGv3 13
+C N1 -1.901 -0.347 0.157 ETKDGv3 14
+C C2 -1.617 -0.162 -1.245 ETKDGv3 15
+C O2 -0.433 -0.066 -1.663 ETKDGv3 16
+C N3 -2.702 -0.092 -2.176 ETKDGv3 17
+C C4 -3.937 -0.217 -1.787 ETKDGv3 18
+C N4 -4.990 -0.142 -2.747 ETKDGv3 19
+C C5 -4.255 -0.443 -0.360 ETKDGv3 20
+C C6 -3.277 -0.507 0.548 ETKDGv3 21
+C HOP3 3.982 1.790 -1.980 ETKDGv3 22
+C HOP2 4.558 4.772 -0.147 ETKDGv3 23
+C "H5'" 3.444 0.089 0.529 ETKDGv3 24
+C "H5''" 2.200 0.601 -0.700 ETKDGv3 25
+C "H4'" 1.647 -0.026 2.264 ETKDGv3 26
+C "H3'" 1.449 -1.679 -0.318 ETKDGv3 27
+C "HO3'" 2.139 -3.261 1.090 ETKDGv3 28
+C "H2'" -0.617 -2.482 0.272 ETKDGv3 29
+C "HO2'" 0.072 -2.034 3.029 ETKDGv3 30
+C "H1'" -1.271 -0.314 2.178 ETKDGv3 31
+C H41 -5.987 -0.242 -2.454 ETKDGv3 32
+C H42 -4.775 0.015 -3.757 ETKDGv3 33
+C H5 -5.285 -0.562 -0.049 ETKDGv3 34
+C H6 -3.533 -0.682 1.586 ETKDGv3 35
+#

nucleotides/C.pdb

@@ -0,0 +1,71 @@
+HETATM    1  OP3   C     0       2.147  -1.021  -4.678  0.00  0.00           O  
+HETATM    2  P     C     0       1.049  -0.039  -4.028  0.00  0.00           P  
+HETATM    3  OP1   C     0       1.692   1.237  -3.646  0.00  0.00           O  
+HETATM    4  OP2   C     0      -0.116   0.246  -5.102  0.00  0.00           O  
+HETATM    5  O5'   C     0       0.415  -0.733  -2.721  0.00  0.00           O  
+HETATM    6  C5'   C     0      -0.546   0.181  -2.193  0.00  0.00           C  
+HETATM    7  C4'   C     0      -1.189  -0.419  -0.942  0.00  0.00           C  
+HETATM    8  O4'   C     0      -0.190  -0.648   0.076  0.00  0.00           O  
+HETATM    9  C3'   C     0      -2.178   0.583  -0.307  0.00  0.00           C  
+HETATM   10  O3'   C     0      -3.518   0.283  -0.703  0.00  0.00           O  
+HETATM   11  C2'   C     0      -2.001   0.373   1.215  0.00  0.00           C  
+HETATM   12  O2'   C     0      -3.228  -0.059   1.806  0.00  0.00           O  
+HETATM   13  C1'   C     0      -0.924  -0.729   1.317  0.00  0.00           C  
+HETATM   14  N1    C     0      -0.036  -0.470   2.453  0.00  0.00           N  
+HETATM   15  C2    C     0       0.652   0.683   2.514  0.00  0.00           C  
+HETATM   16  O2    C     0       0.529   1.504   1.620  0.00  0.00           O  
+HETATM   17  N3    C     0       1.467   0.945   3.535  0.00  0.00           N  
+HETATM   18  C4    C     0       1.620   0.070   4.520  0.00  0.00           C  
+HETATM   19  N4    C     0       2.464   0.350   5.569  0.00  0.00           N  
+HETATM   20  C5    C     0       0.916  -1.151   4.483  0.00  0.00           C  
+HETATM   21  C6    C     0       0.087  -1.399   3.442  0.00  0.00           C  
+HETATM   22 HOP3   C     0       2.501  -0.569  -5.456  0.00  0.00           H  
+HETATM   23 HOP2   C     0      -0.508  -0.608  -5.323  0.00  0.00           H  
+HETATM   24  H5'   C     0      -1.315   0.371  -2.941  0.00  0.00           H  
+HETATM   25 H5''   C     0      -0.052   1.118  -1.933  0.00  0.00           H  
+HETATM   26  H4'   C     0      -1.699  -1.350  -1.188  0.00  0.00           H  
+HETATM   27  H3'   C     0      -1.917   1.604  -0.586  0.00  0.00           H  
+HETATM   28 HO3'   C     0      -4.088   0.939  -0.278  0.00  0.00           H  
+HETATM   29  H2'   C     0      -1.653   1.290   1.689  0.00  0.00           H  
+HETATM   30 HO2'   C     0      -3.874   0.644   1.656  0.00  0.00           H  
+HETATM   31  H1'   C     0      -1.392  -1.708   1.418  0.00  0.00           H  
+HETATM   32  H41   C     0       2.950   1.189   5.590  0.00  0.00           H  
+HETATM   33  H42   C     0       2.571  -0.289   6.290  0.00  0.00           H  
+HETATM   34  H5    C     0       1.030  -1.873   5.278  0.00  0.00           H  
+HETATM   35  H6    C     0      -0.465  -2.326   3.393  0.00  0.00           H  
+CONECT    1    2   22
+CONECT    2    1    3    4    5
+CONECT    3    2
+CONECT    4    2   23
+CONECT    5    2    6
+CONECT    6    5    7   24   25
+CONECT    7    6    8    9   26
+CONECT    8    7   13
+CONECT    9    7   10   11   27
+CONECT   10    9   28
+CONECT   11    9   12   13   29
+CONECT   12   11   30
+CONECT   13    8   11   14   31
+CONECT   14   13   15   21
+CONECT   15   14   16   17
+CONECT   16   15
+CONECT   17   15   18
+CONECT   18   17   19   20
+CONECT   19   18   32   33
+CONECT   20   18   21   34
+CONECT   21   14   20   35
+CONECT   22    1
+CONECT   23    4
+CONECT   24    6
+CONECT   25    6
+CONECT   26    7
+CONECT   27    9
+CONECT   28   10
+CONECT   29   11
+CONECT   30   12
+CONECT   31   13
+CONECT   32   19
+CONECT   33   19
+CONECT   34   20
+CONECT   35   21
+END

nucleotides/G.cif

@@ -0,0 +1,504 @@
+data_G
+#
+_chem_comp.id G
+_chem_comp.name "GUANOSINE-5'-MONOPHOSPHATE"
+_chem_comp.type 'RNA LINKING'
+_chem_comp.pdbx_type ATOMN
+_chem_comp.formula 'C10 H14 N5 O8 P'
+_chem_comp.mon_nstd_parent_comp_id ?
+_chem_comp.pdbx_synonyms ?
+_chem_comp.pdbx_formal_charge 0
+_chem_comp.pdbx_initial_date 1999-07-08
+_chem_comp.pdbx_modified_date 2024-09-27
+_chem_comp.pdbx_ambiguous_flag N
+_chem_comp.pdbx_release_status REL
+_chem_comp.pdbx_replaced_by ?
+_chem_comp.pdbx_replaces ?
+_chem_comp.formula_weight 363.221
+_chem_comp.one_letter_code G
+_chem_comp.three_letter_code G
+_chem_comp.pdbx_model_coordinates_details ?
+_chem_comp.pdbx_model_coordinates_missing_flag N
+_chem_comp.pdbx_ideal_coordinates_details ?
+_chem_comp.pdbx_ideal_coordinates_missing_flag N
+_chem_comp.pdbx_model_coordinates_db_code ?
+_chem_comp.pdbx_subcomponent_list ?
+_chem_comp.pdbx_processing_site RCSB
+_chem_comp.pdbx_pcm Y
+#
+loop_
+_chem_comp_atom.comp_id
+_chem_comp_atom.atom_id
+_chem_comp_atom.alt_atom_id
+_chem_comp_atom.type_symbol
+_chem_comp_atom.charge
+_chem_comp_atom.pdbx_align
+_chem_comp_atom.pdbx_aromatic_flag
+_chem_comp_atom.pdbx_leaving_atom_flag
+_chem_comp_atom.pdbx_stereo_config
+_chem_comp_atom.pdbx_backbone_atom_flag
+_chem_comp_atom.pdbx_n_terminal_atom_flag
+_chem_comp_atom.pdbx_c_terminal_atom_flag
+_chem_comp_atom.model_Cartn_x
+_chem_comp_atom.model_Cartn_y
+_chem_comp_atom.model_Cartn_z
+_chem_comp_atom.pdbx_model_Cartn_x_ideal
+_chem_comp_atom.pdbx_model_Cartn_y_ideal
+_chem_comp_atom.pdbx_model_Cartn_z_ideal
+_chem_comp_atom.pdbx_component_atom_id
+_chem_comp_atom.pdbx_component_comp_id
+_chem_comp_atom.pdbx_ordinal
+G OP3 O3P O 0 1 N Y N N N N 34.313 3.314 -0.422 -1.945 -1.360 5.599 OP3 G 1
+G P P P 0 1 N N N N N N 33.741 4.431 0.367 -0.911 -0.277 5.008 P G 2
+G OP1 O1P O 0 1 N N N N N N 33.537 5.671 -0.451 -1.598 1.022 4.844 OP1 G 3
+G OP2 O2P O 0 1 N N N N N N 34.442 4.727 1.661 0.325 -0.105 6.025 OP2 G 4
+G "O5'" O5* O 0 1 N N N N N N 32.289 3.932 0.811 -0.365 -0.780 3.580 "O5'" G 5
+G "C5'" C5* C 0 1 N N N N N N 32.101 2.551 1.198 0.542 0.217 3.109 "C5'" G 6
+G "C4'" C4* C 0 1 N N R N N N 30.760 2.450 1.879 1.100 -0.200 1.748 "C4'" G 7
+G "O4'" O4* O 0 1 N N N N N N 30.797 3.202 3.104 0.033 -0.318 0.782 "O4'" G 8
+G "C3'" C3* C 0 1 N N S N N N 29.597 3.022 1.070 2.025 0.898 1.182 "C3'" G 9
+G "O3'" O3* O 0 1 N N N N N N 29.106 2.045 0.152 3.395 0.582 1.439 "O3'" G 10
+G "C2'" C2* C 0 1 N N R N N N 28.603 3.421 2.118 1.741 0.884 -0.338 "C2'" G 11
+G "O2'" O2* O 0 1 N N N N N N 27.930 2.319 2.657 2.927 0.560 -1.066 "O2'" G 12
+G "C1'" C1* C 0 1 N N R N N N 29.487 3.936 3.170 0.675 -0.220 -0.507 "C1'" G 13
+G N9 N9 N 0 1 Y N N N N N 29.942 5.378 3.195 -0.297 0.162 -1.534 N9 G 14
+G C8 C8 C 0 1 Y N N N N N 31.187 5.907 3.065 -1.440 0.880 -1.334 C8 G 15
+G N7 N7 N 0 1 Y N N N N N 31.237 7.191 3.136 -2.066 1.037 -2.464 N7 G 16
+G C5 C5 C 0 1 Y N N N N N 29.896 7.536 3.341 -1.364 0.431 -3.453 C5 G 17
+G C6 C6 C 0 1 N N N N N N 29.331 8.813 3.503 -1.556 0.279 -4.846 C6 G 18
+G O6 O6 O 0 1 N N N N N N 29.901 9.926 3.495 -2.534 0.755 -5.397 O6 G 19
+G N1 N1 N 0 1 N N N N N N 27.948 8.749 3.683 -0.626 -0.401 -5.551 N1 G 20
+G C2 C2 C 0 1 N N N N N N 27.233 7.615 3.707 0.459 -0.934 -4.923 C2 G 21
+G N2 N2 N 0 1 N N N N N N 25.894 7.743 3.899 1.384 -1.626 -5.664 N2 G 22
+G N3 N3 N 0 1 N N N N N N 27.758 6.393 3.559 0.649 -0.800 -3.630 N3 G 23
+G C4 C4 C 0 1 Y N N N N N 29.079 6.431 3.382 -0.226 -0.134 -2.868 C4 G 24
+G HOP3 3HOP H 0 0 N N N N N N 34.442 2.528 0.096 -2.247 -1.021 6.453 HOP3 G 25
+G HOP2 2HOP H 0 0 N N N N N N 34.571 3.941 2.179 0.745 -0.973 6.104 HOP2 G 26
+G "H5'" 1H5* H 0 1 N N N N N N 32.209 1.841 0.344 1.362 0.327 3.820 "H5'" G 27
+G "H5''" 2H5* H 0 0 N N N N N N 32.936 2.156 1.822 0.018 1.168 3.011 "H5''" G 28
+G "H4'" H4* H 0 1 N N N N N N 30.585 1.358 2.025 1.640 -1.144 1.833 "H4'" G 29
+G "H3'" H3* H 0 1 N N N N N N 29.867 3.891 0.426 1.772 1.868 1.610 "H3'" G 30
+G "HO3'" H3T H 0 0 N Y N N N N 28.382 2.400 -0.351 3.923 1.300 1.065 "HO3'" G 31
+G "H2'" H2* H 0 1 N N N N N N 27.827 4.115 1.719 1.346 1.847 -0.662 "H2'" G 32
+G "HO2'" 2HO* H 0 0 N N N N N N 27.299 2.572 3.321 3.573 1.254 -0.871 "HO2'" G 33
+G "H1'" H1* H 0 1 N N N N N N 28.814 3.801 4.048 1.148 -1.167 -0.769 "H1'" G 34
+G H8 H8 H 0 1 N N N N N N 32.110 5.323 2.909 -1.776 1.261 -0.381 H8 G 35
+G H1 H1 H 0 1 N N N N N N 27.411 9.607 3.808 -0.736 -0.518 -6.508 H1 G 36
+G H21 1H2 H 0 1 N N N N N N 25.350 6.880 3.917 2.165 -2.007 -5.232 H21 G 37
+G H22 2H2 H 0 1 N N N N N N 25.507 8.377 3.200 1.256 -1.736 -6.619 H22 G 38
+#
+loop_
+_chem_comp_bond.comp_id
+_chem_comp_bond.atom_id_1
+_chem_comp_bond.atom_id_2
+_chem_comp_bond.value_order
+_chem_comp_bond.pdbx_aromatic_flag
+_chem_comp_bond.pdbx_stereo_config
+_chem_comp_bond.pdbx_ordinal
+G OP3 P SING N N 1
+G OP3 HOP3 SING N N 2
+G P OP1 DOUB N N 3
+G P OP2 SING N N 4
+G P "O5'" SING N N 5
+G OP2 HOP2 SING N N 6
+G "O5'" "C5'" SING N N 7
+G "C5'" "C4'" SING N N 8
+G "C5'" "H5'" SING N N 9
+G "C5'" "H5''" SING N N 10
+G "C4'" "O4'" SING N N 11
+G "C4'" "C3'" SING N N 12
+G "C4'" "H4'" SING N N 13
+G "O4'" "C1'" SING N N 14
+G "C3'" "O3'" SING N N 15
+G "C3'" "C2'" SING N N 16
+G "C3'" "H3'" SING N N 17
+G "O3'" "HO3'" SING N N 18
+G "C2'" "O2'" SING N N 19
+G "C2'" "C1'" SING N N 20
+G "C2'" "H2'" SING N N 21
+G "O2'" "HO2'" SING N N 22
+G "C1'" N9 SING N N 23
+G "C1'" "H1'" SING N N 24
+G N9 C8 SING Y N 25
+G N9 C4 SING Y N 26
+G C8 N7 DOUB Y N 27
+G C8 H8 SING N N 28
+G N7 C5 SING Y N 29
+G C5 C6 SING N N 30
+G C5 C4 DOUB Y N 31
+G C6 O6 DOUB N N 32
+G C6 N1 SING N N 33
+G N1 C2 SING N N 34
+G N1 H1 SING N N 35
+G C2 N2 SING N N 36
+G C2 N3 DOUB N N 37
+G N2 H21 SING N N 38
+G N2 H22 SING N N 39
+G N3 C4 SING N N 40
+#
+loop_
+_pdbx_chem_comp_descriptor.comp_id
+_pdbx_chem_comp_descriptor.type
+_pdbx_chem_comp_descriptor.program
+_pdbx_chem_comp_descriptor.program_version
+_pdbx_chem_comp_descriptor.descriptor
+G SMILES ACDLabs 10.04 'O=C1c2ncn(c2N=C(N)N1)C3OC(C(O)C3O)COP(=O)(O)O'
+G SMILES_CANONICAL CACTVS 3.341 'NC1=Nc2n(cnc2C(=O)N1)[C@@H]3O[C@H](CO[P](O)(O)=O)[C@@H](O)[C@H]3O'
+G SMILES CACTVS 3.341 'NC1=Nc2n(cnc2C(=O)N1)[CH]3O[CH](CO[P](O)(O)=O)[CH](O)[CH]3O'
+G SMILES_CANONICAL 'OpenEye OEToolkits' 1.5.0 'c1nc2c(n1[C@H]3[C@@H]([C@@H]([C@H](O3)COP(=O)(O)O)O)O)N=C(NC2=O)N'
+G SMILES 'OpenEye OEToolkits' 1.5.0 'c1nc2c(n1C3C(C(C(O3)COP(=O)(O)O)O)O)N=C(NC2=O)N'
+G InChI InChI 1.03 'InChI=1S/C10H14N5O8P/c11-10-13-7-4(8(18)14-10)12-2-15(7)9-6(17)5(16)3(23-9)1-22-24(19,20)21/h2-3,5-6,9,16-17H,1H2,(H2,19,20,21)(H3,11,13,14,18)/t3-,5-,6-,9-/m1/s1'
+G InChIKey InChI 1.03 RQFCJASXJCIDSX-UUOKFMHZSA-N
+#
+loop_
+_pdbx_chem_comp_identifier.comp_id
+_pdbx_chem_comp_identifier.type
+_pdbx_chem_comp_identifier.program
+_pdbx_chem_comp_identifier.program_version
+_pdbx_chem_comp_identifier.identifier
+G 'SYSTEMATIC NAME' ACDLabs 10.04
+;5'-guanylic acid
+;
+G 'SYSTEMATIC NAME' 'OpenEye OEToolkits' 1.5.0 '[(2R,3S,4R,5R)-5-(2-amino-6-oxo-1H-purin-9-yl)-3,4-dihydroxy-oxolan-2-yl]methyl dihydrogen phosphate'
+#
+loop_
+_pdbx_chem_comp_audit.comp_id
+_pdbx_chem_comp_audit.action_type
+_pdbx_chem_comp_audit.date
+_pdbx_chem_comp_audit.processing_site
+G 'Create component' 1999-07-08 RCSB
+G 'Modify descriptor' 2011-06-04 RCSB
+G 'Modify PCM' 2024-09-27 PDBE
+#
+_pdbx_chem_comp_pcm.pcm_id 1
+_pdbx_chem_comp_pcm.comp_id G
+_pdbx_chem_comp_pcm.modified_residue_id LYS
+_pdbx_chem_comp_pcm.type GMPylation
+_pdbx_chem_comp_pcm.category 'Nucleotide monophosphate'
+_pdbx_chem_comp_pcm.position 'Amino-acid side chain'
+_pdbx_chem_comp_pcm.polypeptide_position 'Any position'
+_pdbx_chem_comp_pcm.comp_id_linking_atom P
+_pdbx_chem_comp_pcm.modified_residue_id_linking_atom NZ
+_pdbx_chem_comp_pcm.uniprot_specific_ptm_accession ?
+_pdbx_chem_comp_pcm.uniprot_generic_ptm_accession ?
+#
+_pdbe_chem_comp_drugbank_details.comp_id G
+_pdbe_chem_comp_drugbank_details.drugbank_id DB01972
+_pdbe_chem_comp_drugbank_details.type 'small molecule'
+_pdbe_chem_comp_drugbank_details.name "Guanosine-5'-Monophosphate"
+_pdbe_chem_comp_drugbank_details.description
+'Guanosine 5'-monophosphate. A guanine nucleotide containing one phosphate group esterified to the sugar moiety and found widely in nature. [PubChem]'
+_pdbe_chem_comp_drugbank_details.cas_number 85-32-5
+_pdbe_chem_comp_drugbank_details.mechanism_of_action ?
+#
+_pdbe_chem_comp_drugbank_classification.comp_id G
+_pdbe_chem_comp_drugbank_classification.drugbank_id DB01972
+_pdbe_chem_comp_drugbank_classification.parent 'Purine ribonucleoside monophosphates'
+_pdbe_chem_comp_drugbank_classification.kingdom 'Organic compounds'
+_pdbe_chem_comp_drugbank_classification.class 'Purine nucleotides'
+_pdbe_chem_comp_drugbank_classification.superclass 'Nucleosides, nucleotides, and analogues'
+_pdbe_chem_comp_drugbank_classification.description
+'This compound belongs to the class of organic compounds known as purine ribonucleoside monophosphates. These are nucleotides consisting of a purine base linked to a ribose to which one monophosphate group is attached.'
+#
+loop_
+_pdbe_chem_comp_drugbank_targets.comp_id
+_pdbe_chem_comp_drugbank_targets.drugbank_id
+_pdbe_chem_comp_drugbank_targets.name
+_pdbe_chem_comp_drugbank_targets.organism
+_pdbe_chem_comp_drugbank_targets.uniprot_id
+_pdbe_chem_comp_drugbank_targets.pharmacologically_active
+_pdbe_chem_comp_drugbank_targets.ordinal
+G DB01972 'Guanylate kinase' Humans Q16774 unknown 1
+G DB01972 'Response regulator PleD' 'Caulobacter crescentus (strain ATCC 19089 / CB15)' Q9A5I5 unknown 2
+G DB01972 'DNA polymerase' 'Enterobacteria phage RB69' Q38087 unknown 3
+G DB01972 'Disks large homolog 4' Humans P78352 unknown 4
+G DB01972 'GMP reductase 1' Humans P36959 unknown 5
+G DB01972 "cGMP-specific 3',5'-cyclic phosphodiesterase" Humans O76074 unknown 6
+G DB01972 'Histidine triad nucleotide-binding protein 1' Humans P49773 unknown 7
+G DB01972 'Bifunctional purine biosynthesis protein PURH' Humans P31939 unknown 8
+G DB01972 Amidophosphoribosyltransferase 'Bacillus subtilis (strain 168)' P00497 unknown 9
+G DB01972 'Protein-glutamine gamma-glutamyltransferase E' Humans Q08188 unknown 10
+G DB01972 'Bifunctional protein PyrR' 'Bacillus caldolyticus' P41007 unknown 11
+G DB01972 'Xanthine phosphoribosyltransferase' 'Escherichia coli (strain K12)' P0A9M5 unknown 12
+G DB01972 Amidophosphoribosyltransferase 'Escherichia coli (strain K12)' P0AG16 unknown 13
+G DB01972 'Bifunctional adenosylcobalamin biosynthesis protein CobU' 'Salmonella typhimurium (strain LT2 / SGSC1412 / ATCC 700720)' Q05599 unknown 14
+#
+loop_
+_software.name
+_software.version
+_software.description
+rdkit 2023.09.6 'Core functionality.'
+pdbeccdutils 0.8.6 'Wrapper to provide 2D templates and molecular fragments.'
+#
+loop_
+_pdbe_chem_comp_atom_depiction.comp_id
+_pdbe_chem_comp_atom_depiction.atom_id
+_pdbe_chem_comp_atom_depiction.element
+_pdbe_chem_comp_atom_depiction.model_Cartn_x
+_pdbe_chem_comp_atom_depiction.model_Cartn_y
+_pdbe_chem_comp_atom_depiction.pdbx_ordinal
+G OP3 O 16.032 4.532 1
+G P P 14.817 3.652 2
+G OP1 O 15.697 2.437 3
+G OP2 O 13.938 4.867 4
+G "O5'" O 13.602 2.772 5
+G "C5'" C 12.233 3.385 6
+G "C4'" C 11.018 2.505 7
+G "O4'" O 11.015 1.005 8
+G "C3'" C 9.592 2.971 9
+G "O3'" O 9.131 4.399 10
+G "C2'" C 8.708 1.759 11
+G "O2'" O 7.208 1.762 12
+G "C1'" C 9.588 0.544 13
+G N9 N 9.122 -0.882 14
+G C8 C 9.997 -2.089 15
+G N7 N 9.122 -3.296 16
+G C5 C 7.702 -2.839 17
+G C6 C 6.404 -3.589 18
+G O6 O 6.404 -5.089 19
+G N1 N 5.104 -2.839 20
+G C2 C 5.104 -1.339 21
+G N2 N 3.805 -0.589 22
+G N3 N 6.404 -0.589 23
+G C4 C 7.702 -1.339 24
+#
+loop_
+_pdbe_chem_comp_bond_depiction.comp_id
+_pdbe_chem_comp_bond_depiction.atom_id_1
+_pdbe_chem_comp_bond_depiction.atom_id_2
+_pdbe_chem_comp_bond_depiction.value_order
+_pdbe_chem_comp_bond_depiction.bond_dir
+_pdbe_chem_comp_bond_depiction.pdbx_ordinal
+G OP3 P SINGLE NONE 1
+G P OP1 DOUBLE NONE 2
+G P OP2 SINGLE NONE 3
+G P "O5'" SINGLE NONE 4
+G "O5'" "C5'" SINGLE NONE 5
+G "C4'" "C5'" SINGLE BEGINWEDGE 6
+G "C4'" "O4'" SINGLE NONE 7
+G "C4'" "C3'" SINGLE NONE 8
+G "O4'" "C1'" SINGLE NONE 9
+G "C3'" "O3'" SINGLE BEGINDASH 10
+G "C3'" "C2'" SINGLE NONE 11
+G "C2'" "O2'" SINGLE BEGINDASH 12
+G "C2'" "C1'" SINGLE NONE 13
+G "C1'" N9 SINGLE BEGINWEDGE 14
+G N9 C8 SINGLE NONE 15
+G N9 C4 SINGLE NONE 16
+G C8 N7 DOUBLE NONE 17
+G N7 C5 SINGLE NONE 18
+G C5 C6 SINGLE NONE 19
+G C5 C4 DOUBLE NONE 20
+G C6 O6 DOUBLE NONE 21
+G C6 N1 SINGLE NONE 22
+G N1 C2 SINGLE NONE 23
+G C2 N2 SINGLE NONE 24
+G C2 N3 DOUBLE NONE 25
+G N3 C4 SINGLE NONE 26
+#
+loop_
+_pdbe_chem_comp_substructure.comp_id
+_pdbe_chem_comp_substructure.substructure_name
+_pdbe_chem_comp_substructure.id
+_pdbe_chem_comp_substructure.substructure_type
+_pdbe_chem_comp_substructure.substructure_smiles
+_pdbe_chem_comp_substructure.substructure_inchis
+_pdbe_chem_comp_substructure.substructure_inchikeys
+G MurckoScaffold S1 scaffold 'O=c1[nH]cnc2c1ncn2[C@H]1CCCO1' InChI=1S/C9H10N4O2/c14-9-7-8(10-4-11-9)13(5-12-7)6-2-1-3-15-6/h4-6H,1-3H2,(H,10,11,14)/t6-/m1/s1 JBXHFRZBULGPSC-ZCFIWIBFSA-N
+G imidazole F1 fragment 'c1c[nH]cn1' InChI=1S/C3H4N2/c1-2-5-3-4-1/h1-3H,(H,4,5) RAXXELZNTBOGNW-UHFFFAOYSA-N
+G phosphate F2 fragment O=P(O)(O)O InChI=1S/H3O4P/c1-5(2,3)4/h(H3,1,2,3,4) NBIIXXVUZAFLBC-UHFFFAOYSA-N
+G purine F3 fragment 'c1ncc2[nH]cnc2n1' InChI=1S/C5H4N4/c1-4-5(8-2-6-1)9-3-7-4/h1-3H,(H,6,7,8,9) KDCGOANMDULRCW-UHFFFAOYSA-N
+G pyrimidine F4 fragment c1cncnc1 InChI=1S/C4H4N2/c1-2-5-4-6-3-1/h1-4H CZPWVGJYEJSRLH-UHFFFAOYSA-N
+G ribose F5 fragment OCC1OCC(O)C1O InChI=1S/C5H10O4/c6-1-4-5(8)3(7)2-9-4/h3-8H,1-2H2 KZVAAIRBJJYZOW-UHFFFAOYSA-N
+#
+loop_
+_pdbe_chem_comp_substructure_mapping.comp_id
+_pdbe_chem_comp_substructure_mapping.atom_id
+_pdbe_chem_comp_substructure_mapping.substructure_id
+_pdbe_chem_comp_substructure_mapping.substructure_ordinal
+G "C4'" S1 1
+G "O4'" S1 1
+G "C3'" S1 1
+G "C2'" S1 1
+G "C1'" S1 1
+G N9 S1 1
+G C8 S1 1
+G N7 S1 1
+G C5 S1 1
+G C6 S1 1
+G O6 S1 1
+G N1 S1 1
+G C2 S1 1
+G N3 S1 1
+G C4 S1 1
+G C5 F1 1
+G C4 F1 1
+G N9 F1 1
+G C8 F1 1
+G N7 F1 1
+G OP3 F2 1
+G P F2 1
+G OP1 F2 1
+G OP2 F2 1
+G "O5'" F2 1
+G N7 F3 1
+G C8 F3 1
+G N9 F3 1
+G C4 F3 1
+G C5 F3 1
+G C6 F3 1
+G N1 F3 1
+G C2 F3 1
+G N3 F3 1
+G C5 F4 1
+G C6 F4 1
+G N1 F4 1
+G C2 F4 1
+G N3 F4 1
+G C4 F4 1
+G "C2'" F5 1
+G "C3'" F5 1
+G "C4'" F5 1
+G "O4'" F5 1
+G "C1'" F5 1
+G "C5'" F5 1
+G "O5'" F5 1
+G "O3'" F5 1
+G "O2'" F5 1
+#
+_pdbe_chem_comp_rdkit_properties.comp_id G
+_pdbe_chem_comp_rdkit_properties.exactmw 363.058
+_pdbe_chem_comp_rdkit_properties.amw 363.223
+_pdbe_chem_comp_rdkit_properties.lipinskiHBA 13
+_pdbe_chem_comp_rdkit_properties.lipinskiHBD 7
+_pdbe_chem_comp_rdkit_properties.NumRotatableBonds 9
+_pdbe_chem_comp_rdkit_properties.NumHBD 6
+_pdbe_chem_comp_rdkit_properties.NumHBA 12
+_pdbe_chem_comp_rdkit_properties.NumHeavyAtoms 24
+_pdbe_chem_comp_rdkit_properties.NumAtoms 38
+_pdbe_chem_comp_rdkit_properties.NumHeteroatoms 14
+_pdbe_chem_comp_rdkit_properties.NumAmideBonds 0
+_pdbe_chem_comp_rdkit_properties.FractionCSP3 0.500
+_pdbe_chem_comp_rdkit_properties.NumRings 3
+_pdbe_chem_comp_rdkit_properties.NumAromaticRings 2
+_pdbe_chem_comp_rdkit_properties.NumAliphaticRings 1
+_pdbe_chem_comp_rdkit_properties.NumSaturatedRings 1
+_pdbe_chem_comp_rdkit_properties.NumHeterocycles 3
+_pdbe_chem_comp_rdkit_properties.NumAromaticHeterocycles 2
+_pdbe_chem_comp_rdkit_properties.NumSaturatedHeterocycles 1
+_pdbe_chem_comp_rdkit_properties.NumAliphaticHeterocycles 1
+_pdbe_chem_comp_rdkit_properties.NumSpiroAtoms 0
+_pdbe_chem_comp_rdkit_properties.NumBridgeheadAtoms 0
+_pdbe_chem_comp_rdkit_properties.NumAtomStereoCenters 4
+_pdbe_chem_comp_rdkit_properties.NumUnspecifiedAtomStereoCenters 0
+_pdbe_chem_comp_rdkit_properties.labuteASA 152.656
+_pdbe_chem_comp_rdkit_properties.tpsa 206.040
+_pdbe_chem_comp_rdkit_properties.CrippenClogP -2.479
+_pdbe_chem_comp_rdkit_properties.CrippenMR 73.559
+_pdbe_chem_comp_rdkit_properties.chi0v 11.844
+_pdbe_chem_comp_rdkit_properties.chi1v 7.152
+_pdbe_chem_comp_rdkit_properties.chi2v 2.869
+_pdbe_chem_comp_rdkit_properties.chi3v 2.869
+_pdbe_chem_comp_rdkit_properties.chi4v 1.851
+_pdbe_chem_comp_rdkit_properties.chi0n 24.949
+_pdbe_chem_comp_rdkit_properties.chi1n 12.166
+_pdbe_chem_comp_rdkit_properties.chi2n 2.554
+_pdbe_chem_comp_rdkit_properties.chi3n 2.554
+_pdbe_chem_comp_rdkit_properties.chi4n 1.656
+_pdbe_chem_comp_rdkit_properties.hallKierAlpha -1.860
+_pdbe_chem_comp_rdkit_properties.kappa1 6.802
+_pdbe_chem_comp_rdkit_properties.kappa2 5.895
+_pdbe_chem_comp_rdkit_properties.kappa3 3.055
+_pdbe_chem_comp_rdkit_properties.Phi 1.671
+#
+loop_
+_pdbe_chem_comp_external_mappings.comp_id
+_pdbe_chem_comp_external_mappings.source
+_pdbe_chem_comp_external_mappings.resource
+_pdbe_chem_comp_external_mappings.resource_id
+G UniChem ChEMBL CHEMBL283807
+G UniChem DrugBank DB01972
+G UniChem 'Guide to Pharmacology' 5123
+G UniChem 'KEGG LIGAND' C00144
+G UniChem ChEBI 17345
+G UniChem ZINC ZINC000002159505
+G UniChem eMolecules 29480205
+G UniChem eMolecules 5747772
+G UniChem atlas CpG
+G UniChem fdasrs 16597955EP
+G UniChem SureChEMBL SCHEMBL19222125
+G UniChem SureChEMBL SCHEMBL5854
+G UniChem HMDB HMDB0001397
+G UniChem 'PubChem TPHARMA' 14901517
+G UniChem 'PubChem TPHARMA' 16002058
+G UniChem 'PubChem TPHARMA' 16014337
+G UniChem PubChem 135398631
+G UniChem NMRShiftDB 60020700
+G UniChem ACTor 25191-14-4
+G UniChem ACTor 29593-02-0
+G UniChem ACTor 573-48-8
+G UniChem Nikkaji J10.615A
+G UniChem BindingDb 426061
+G UniChem BindingDb 50010318
+G UniChem 'EPA CompTox Dashboard' DTXSID9044295
+G UniChem MetaboLights MTBLC17345
+G UniChem BRENDA 100656
+G UniChem BRENDA 1022
+G UniChem BRENDA 14093
+G UniChem BRENDA 162
+G UniChem BRENDA 27054
+G UniChem BRENDA 37344
+G UniChem BRENDA 5973
+G UniChem BRENDA 96075
+G UniChem ChemicalBook CB6271952
+G UniChem MedChemExpress HY-N5134
+G UniChem 'Probes And Drugs' PD051028
+G UniChem CCDC CIQYEH
+#
+loop_
+_pdbe_chem_comp_rdkit_conformer.comp_id
+_pdbe_chem_comp_rdkit_conformer.atom_id
+_pdbe_chem_comp_rdkit_conformer.Cartn_x_rdkit
+_pdbe_chem_comp_rdkit_conformer.Cartn_y_rdkit
+_pdbe_chem_comp_rdkit_conformer.Cartn_z_rdkit
+_pdbe_chem_comp_rdkit_conformer.rdkit_method
+_pdbe_chem_comp_rdkit_conformer.rdkit_ordinal
+G OP3 2.436 3.188 0.780 ETKDGv3 1
+G P 2.202 2.289 -0.638 ETKDGv3 2
+G OP1 3.528 2.073 -1.334 ETKDGv3 3
+G OP2 1.173 3.150 -1.669 ETKDGv3 4
+G "O5'" 1.483 0.784 -0.287 ETKDGv3 5
+G "C5'" 2.221 0.160 0.739 ETKDGv3 6
+G "C4'" 1.716 -1.274 0.945 ETKDGv3 7
+G "O4'" 0.359 -1.247 1.344 ETKDGv3 8
+G "C3'" 1.859 -2.121 -0.331 ETKDGv3 9
+G "O3'" 2.407 -3.378 -0.021 ETKDGv3 10
+G "C2'" 0.430 -2.268 -0.793 ETKDGv3 11
+G "O2'" 0.185 -3.510 -1.401 ETKDGv3 12
+G "C1'" -0.344 -2.134 0.506 ETKDGv3 13
+G N9 -1.699 -1.642 0.265 ETKDGv3 14
+G C8 -2.751 -2.375 -0.380 ETKDGv3 15
+G N7 -3.676 -1.622 -0.901 ETKDGv3 16
+G C5 -3.240 -0.297 -0.622 ETKDGv3 17
+G C6 -3.864 0.969 -0.984 ETKDGv3 18
+G O6 -4.950 1.016 -1.619 ETKDGv3 19
+G N1 -3.181 2.167 -0.583 ETKDGv3 20
+G C2 -1.935 2.065 0.148 ETKDGv3 21
+G N2 -1.260 3.248 0.570 ETKDGv3 22
+G N3 -1.429 0.893 0.432 ETKDGv3 23
+G C4 -2.106 -0.305 0.025 ETKDGv3 24
+G HOP3 3.307 2.884 1.140 ETKDGv3 25
+G HOP2 1.582 4.049 -1.741 ETKDGv3 26
+G "H5'" 3.307 0.110 0.491 ETKDGv3 27
+G "H5''" 2.091 0.713 1.695 ETKDGv3 28
+G "H4'" 2.313 -1.725 1.769 ETKDGv3 29
+G "H3'" 2.467 -1.625 -1.125 ETKDGv3 30
+G "HO3'" 3.385 -3.240 0.085 ETKDGv3 31
+G "H2'" 0.166 -1.432 -1.485 ETKDGv3 32
+G "HO2'" 0.595 -3.464 -2.304 ETKDGv3 33
+G "H1'" -0.406 -3.122 1.016 ETKDGv3 34
+G H8 -2.769 -3.455 -0.451 ETKDGv3 35
+G H1 -3.584 3.102 -0.814 ETKDGv3 36
+G H21 -0.378 3.185 1.125 ETKDGv3 37
+G H22 -1.645 4.190 0.338 ETKDGv3 38
+#

nucleotides/G.pdb

@@ -0,0 +1,77 @@
+HETATM    1  OP3   G     0      -1.945  -1.360   5.599  0.00  0.00           O  
+HETATM    2  P     G     0      -0.911  -0.277   5.008  0.00  0.00           P  
+HETATM    3  OP1   G     0      -1.598   1.022   4.844  0.00  0.00           O  
+HETATM    4  OP2   G     0       0.325  -0.105   6.025  0.00  0.00           O  
+HETATM    5  O5'   G     0      -0.365  -0.780   3.580  0.00  0.00           O  
+HETATM    6  C5'   G     0       0.542   0.217   3.109  0.00  0.00           C  
+HETATM    7  C4'   G     0       1.100  -0.200   1.748  0.00  0.00           C  
+HETATM    8  O4'   G     0       0.033  -0.318   0.782  0.00  0.00           O  
+HETATM    9  C3'   G     0       2.025   0.898   1.182  0.00  0.00           C  
+HETATM   10  O3'   G     0       3.395   0.582   1.439  0.00  0.00           O  
+HETATM   11  C2'   G     0       1.741   0.884  -0.338  0.00  0.00           C  
+HETATM   12  O2'   G     0       2.927   0.560  -1.066  0.00  0.00           O  
+HETATM   13  C1'   G     0       0.675  -0.220  -0.507  0.00  0.00           C  
+HETATM   14  N9    G     0      -0.297   0.162  -1.534  0.00  0.00           N  
+HETATM   15  C8    G     0      -1.440   0.880  -1.334  0.00  0.00           C  
+HETATM   16  N7    G     0      -2.066   1.037  -2.464  0.00  0.00           N  
+HETATM   17  C5    G     0      -1.364   0.431  -3.453  0.00  0.00           C  
+HETATM   18  C6    G     0      -1.556   0.279  -4.846  0.00  0.00           C  
+HETATM   19  O6    G     0      -2.534   0.755  -5.397  0.00  0.00           O  
+HETATM   20  N1    G     0      -0.626  -0.401  -5.551  0.00  0.00           N  
+HETATM   21  C2    G     0       0.459  -0.934  -4.923  0.00  0.00           C  
+HETATM   22  N2    G     0       1.384  -1.626  -5.664  0.00  0.00           N  
+HETATM   23  N3    G     0       0.649  -0.800  -3.630  0.00  0.00           N  
+HETATM   24  C4    G     0      -0.226  -0.134  -2.868  0.00  0.00           C  
+HETATM   25 HOP3   G     0      -2.247  -1.021   6.453  0.00  0.00           H  
+HETATM   26 HOP2   G     0       0.745  -0.973   6.104  0.00  0.00           H  
+HETATM   27  H5'   G     0       1.362   0.327   3.820  0.00  0.00           H  
+HETATM   28 H5''   G     0       0.018   1.168   3.011  0.00  0.00           H  
+HETATM   29  H4'   G     0       1.640  -1.144   1.833  0.00  0.00           H  
+HETATM   30  H3'   G     0       1.772   1.868   1.610  0.00  0.00           H  
+HETATM   31 HO3'   G     0       3.923   1.300   1.065  0.00  0.00           H  
+HETATM   32  H2'   G     0       1.346   1.847  -0.662  0.00  0.00           H  
+HETATM   33 HO2'   G     0       3.573   1.254  -0.871  0.00  0.00           H  
+HETATM   34  H1'   G     0       1.148  -1.167  -0.769  0.00  0.00           H  
+HETATM   35  H8    G     0      -1.776   1.261  -0.381  0.00  0.00           H  
+HETATM   36  H1    G     0      -0.736  -0.518  -6.508  0.00  0.00           H  
+HETATM   37  H21   G     0       2.165  -2.007  -5.232  0.00  0.00           H  
+HETATM   38  H22   G     0       1.256  -1.736  -6.619  0.00  0.00           H  
+CONECT    1    2   25
+CONECT    2    1    3    4    5
+CONECT    3    2
+CONECT    4    2   26
+CONECT    5    2    6
+CONECT    6    5    7   27   28
+CONECT    7    6    8    9   29
+CONECT    8    7   13
+CONECT    9    7   10   11   30
+CONECT   10    9   31
+CONECT   11    9   12   13   32
+CONECT   12   11   33
+CONECT   13    8   11   14   34
+CONECT   14   13   15   24
+CONECT   15   14   16   35
+CONECT   16   15   17
+CONECT   17   16   18   24
+CONECT   18   17   19   20
+CONECT   19   18
+CONECT   20   18   21   36
+CONECT   21   20   22   23
+CONECT   22   21   37   38
+CONECT   23   21   24
+CONECT   24   14   17   23
+CONECT   25    1
+CONECT   26    4
+CONECT   27    6
+CONECT   28    6
+CONECT   29    7
+CONECT   30    9
+CONECT   31   10
+CONECT   32   11
+CONECT   33   12
+CONECT   34   13
+CONECT   35   15
+CONECT   36   20
+CONECT   37   22
+CONECT   38   22
+END

nucleotides/U.cif

@@ -0,0 +1,456 @@
+data_U
+#
+_chem_comp.id U
+_chem_comp.name "URIDINE-5'-MONOPHOSPHATE"
+_chem_comp.type "RNA LINKING"
+_chem_comp.pdbx_type ATOMN
+_chem_comp.formula "C9 H13 N2 O9 P"
+_chem_comp.mon_nstd_parent_comp_id ?
+_chem_comp.pdbx_synonyms ?
+_chem_comp.pdbx_formal_charge 0
+_chem_comp.pdbx_initial_date 1999-07-08
+_chem_comp.pdbx_modified_date 2011-06-04
+_chem_comp.pdbx_ambiguous_flag N
+_chem_comp.pdbx_release_status REL
+_chem_comp.pdbx_replaced_by ?
+_chem_comp.pdbx_replaces ?
+_chem_comp.formula_weight 324.181
+_chem_comp.one_letter_code U
+_chem_comp.three_letter_code U
+_chem_comp.pdbx_model_coordinates_details ?
+_chem_comp.pdbx_model_coordinates_missing_flag N
+_chem_comp.pdbx_ideal_coordinates_details ?
+_chem_comp.pdbx_ideal_coordinates_missing_flag N
+_chem_comp.pdbx_model_coordinates_db_code ?
+_chem_comp.pdbx_subcomponent_list ?
+_chem_comp.pdbx_processing_site PDBJ
+#
+loop_
+_chem_comp_atom.comp_id
+_chem_comp_atom.atom_id
+_chem_comp_atom.alt_atom_id
+_chem_comp_atom.type_symbol
+_chem_comp_atom.charge
+_chem_comp_atom.pdbx_align
+_chem_comp_atom.pdbx_aromatic_flag
+_chem_comp_atom.pdbx_leaving_atom_flag
+_chem_comp_atom.pdbx_stereo_config
+_chem_comp_atom.pdbx_backbone_atom_flag
+_chem_comp_atom.pdbx_n_terminal_atom_flag
+_chem_comp_atom.pdbx_c_terminal_atom_flag
+_chem_comp_atom.model_Cartn_x
+_chem_comp_atom.model_Cartn_y
+_chem_comp_atom.model_Cartn_z
+_chem_comp_atom.pdbx_model_Cartn_x_ideal
+_chem_comp_atom.pdbx_model_Cartn_y_ideal
+_chem_comp_atom.pdbx_model_Cartn_z_ideal
+_chem_comp_atom.pdbx_component_atom_id
+_chem_comp_atom.pdbx_component_comp_id
+_chem_comp_atom.pdbx_ordinal
+U OP3 O3P O 0 1 N Y N N N N 29.106 2.045 0.152 -2.122 1.033 -4.690 OP3 U 1
+U P P P 0 1 N N N N N N 28.940 2.442 -1.379 -1.030 0.047 -4.037 P U 2
+U OP1 O1P O 0 1 N N N N N N 28.520 1.217 -2.078 -1.679 -1.228 -3.660 OP1 U 3
+U OP2 O2P O 0 1 N N N N N N 30.133 3.129 -1.866 0.138 -0.241 -5.107 OP2 U 4
+U "O5'" O5* O 0 1 N N N N N N 27.784 3.522 -1.387 -0.399 0.736 -2.726 "O5'" U 5
+U "C5'" C5* C 0 1 N N N N N N 26.432 3.117 -1.047 0.557 -0.182 -2.196 "C5'" U 6
+U "C4'" C4* C 0 1 N N R N N N 25.647 4.373 -0.834 1.197 0.415 -0.942 "C4'" U 7
+U "O4'" O4* O 0 1 N N N N N N 26.122 5.093 0.327 0.194 0.645 0.074 "O4'" U 8
+U "C3'" C3* C 0 1 N N S N N N 25.763 5.465 -1.895 2.181 -0.588 -0.301 "C3'" U 9
+U "O3'" O3* O 0 1 N N N N N N 25.041 5.077 -3.062 3.524 -0.288 -0.686 "O3'" U 10
+U "C2'" C2* C 0 1 N N R N N N 25.213 6.663 -1.148 1.995 -0.383 1.218 "C2'" U 11
+U "O2'" O2* O 0 1 N N N N N N 23.829 6.500 -1.012 3.219 0.046 1.819 "O2'" U 12
+U "C1'" C1* C 0 1 N N R N N N 25.917 6.524 0.133 0.922 0.723 1.319 "C1'" U 13
+U N1 N1 N 0 1 N N N N N N 27.224 7.194 0.137 0.028 0.464 2.451 N1 U 14
+U C2 C2 C 0 1 N N N N N N 27.201 8.578 0.406 -0.690 -0.671 2.486 C2 U 15
+U O2 O2 O 0 1 N N N N N N 26.156 9.121 0.619 -0.587 -1.474 1.580 O2 U 16
+U N3 N3 N 0 1 N N N N N N 28.408 9.189 0.403 -1.515 -0.936 3.517 N3 U 17
+U C4 C4 C 0 1 N N N N N N 29.660 8.606 0.152 -1.641 -0.055 4.530 C4 U 18
+U O4 O4 O 0 1 N N N N N N 30.676 9.330 0.195 -2.391 -0.292 5.460 O4 U 19
+U C5 C5 C 0 1 N N N N N N 29.604 7.215 -0.113 -0.894 1.146 4.502 C5 U 20
+U C6 C6 C 0 1 N N N N N N 28.447 6.605 -0.111 -0.070 1.384 3.459 C6 U 21
+U HOP3 3HOP H 0 0 N N N N N N 29.377 2.835 0.603 -2.475 0.583 -5.470 HOP3 U 22
+U HOP2 2HOP H 0 0 N N N N N N 30.404 3.919 -1.414 0.534 0.613 -5.325 HOP2 U 23
+U "H5'" 1H5* H 0 1 N N N N N N 25.974 2.434 -1.800 1.329 -0.373 -2.942 "H5'" U 24
+U "H5''" 2H5* H 0 0 N N N N N N 26.387 2.417 -0.179 0.060 -1.117 -1.940 "H5''" U 25
+U "H4'" H4* H 0 1 N N N N N N 24.613 3.957 -0.795 1.712 1.345 -1.185 "H4'" U 26
+U "H3'" H3* H 0 1 N N N N N N 26.782 5.677 -2.293 1.923 -1.609 -0.583 "H3'" U 27
+U "HO3'" H3T H 0 0 N Y N N N N 25.113 5.756 -3.722 4.094 -0.926 -0.234 "HO3'" U 28
+U "H2'" H2* H 0 1 N N N N N N 25.358 7.661 -1.622 1.643 -1.301 1.688 "H2'" U 29
+U "HO2'" 2HO* H 0 0 N N N N N N 23.484 7.251 -0.543 3.865 -0.657 1.671 "HO2'" U 30
+U "H1'" H1* H 0 1 N N N N N N 25.312 6.996 0.941 1.392 1.700 1.423 "H1'" U 31
+U H3 H3 H 0 1 N N N N N N 28.370 10.187 0.610 -2.024 -1.762 3.528 H3 U 32
+U H5 H5 H 0 1 N N N N N N 30.486 6.589 -0.327 -0.982 1.863 5.305 H5 U 33
+U H6 H6 H 0 1 N N N N N N 28.506 5.526 -0.332 0.507 2.295 3.421 H6 U 34
+#
+loop_
+_chem_comp_bond.comp_id
+_chem_comp_bond.atom_id_1
+_chem_comp_bond.atom_id_2
+_chem_comp_bond.value_order
+_chem_comp_bond.pdbx_aromatic_flag
+_chem_comp_bond.pdbx_stereo_config
+_chem_comp_bond.pdbx_ordinal
+U OP3 P SING N N 1
+U OP3 HOP3 SING N N 2
+U P OP1 DOUB N N 3
+U P OP2 SING N N 4
+U P "O5'" SING N N 5
+U OP2 HOP2 SING N N 6
+U "O5'" "C5'" SING N N 7
+U "C5'" "C4'" SING N N 8
+U "C5'" "H5'" SING N N 9
+U "C5'" "H5''" SING N N 10
+U "C4'" "O4'" SING N N 11
+U "C4'" "C3'" SING N N 12
+U "C4'" "H4'" SING N N 13
+U "O4'" "C1'" SING N N 14
+U "C3'" "O3'" SING N N 15
+U "C3'" "C2'" SING N N 16
+U "C3'" "H3'" SING N N 17
+U "O3'" "HO3'" SING N N 18
+U "C2'" "O2'" SING N N 19
+U "C2'" "C1'" SING N N 20
+U "C2'" "H2'" SING N N 21
+U "O2'" "HO2'" SING N N 22
+U "C1'" N1 SING N N 23
+U "C1'" "H1'" SING N N 24
+U N1 C2 SING N N 25
+U N1 C6 SING N N 26
+U C2 O2 DOUB N N 27
+U C2 N3 SING N N 28
+U N3 C4 SING N N 29
+U N3 H3 SING N N 30
+U C4 O4 DOUB N N 31
+U C4 C5 SING N N 32
+U C5 C6 DOUB N N 33
+U C5 H5 SING N N 34
+U C6 H6 SING N N 35
+#
+loop_
+_pdbx_chem_comp_descriptor.comp_id
+_pdbx_chem_comp_descriptor.type
+_pdbx_chem_comp_descriptor.program
+_pdbx_chem_comp_descriptor.program_version
+_pdbx_chem_comp_descriptor.descriptor
+U SMILES ACDLabs 10.04 O=C1NC(=O)N(C=C1)C2OC(C(O)C2O)COP(=O)(O)O
+U SMILES_CANONICAL CACTVS 3.341 O[C@H]1[C@@H](O)[C@@H](O[C@@H]1CO[P](O)(O)=O)N2C=CC(=O)NC2=O
+U SMILES CACTVS 3.341 O[CH]1[CH](O)[CH](O[CH]1CO[P](O)(O)=O)N2C=CC(=O)NC2=O
+U SMILES_CANONICAL "OpenEye OEToolkits" 1.5.0 C1=CN(C(=O)NC1=O)[C@H]2[C@@H]([C@@H]([C@H](O2)COP(=O)(O)O)O)O
+U SMILES "OpenEye OEToolkits" 1.5.0 C1=CN(C(=O)NC1=O)C2C(C(C(O2)COP(=O)(O)O)O)O
+U InChI InChI 1.03 InChI=1S/C9H13N2O9P/c12-5-1-2-11(9(15)10-5)8-7(14)6(13)4(20-8)3-19-21(16,17)18/h1-2,4,6-8,13-14H,3H2,(H,10,12,15)(H2,16,17,18)/t4-,6-,7-,8-/m1/s1
+U InChIKey InChI 1.03 DJJCXFVJDGTHFX-XVFCMESISA-N
+#
+loop_
+_pdbx_chem_comp_identifier.comp_id
+_pdbx_chem_comp_identifier.type
+_pdbx_chem_comp_identifier.program
+_pdbx_chem_comp_identifier.program_version
+_pdbx_chem_comp_identifier.identifier
+U "SYSTEMATIC NAME" ACDLabs 10.04 "5'-uridylic acid"
+U "SYSTEMATIC NAME" "OpenEye OEToolkits" 1.5.0 "[(2R,3S,4R,5R)-5-(2,4-dioxopyrimidin-1-yl)-3,4-dihydroxy-oxolan-2-yl]methyl dihydrogen phosphate"
+#
+loop_
+_pdbx_chem_comp_audit.comp_id
+_pdbx_chem_comp_audit.action_type
+_pdbx_chem_comp_audit.date
+_pdbx_chem_comp_audit.processing_site
+U 'Create component' 1999-07-08 PDBJ
+U 'Modify descriptor' 2011-06-04 RCSB
+#
+_pdbe_chem_comp_drugbank_details.comp_id U
+_pdbe_chem_comp_drugbank_details.drugbank_id DB03685
+_pdbe_chem_comp_drugbank_details.type 'small molecule'
+_pdbe_chem_comp_drugbank_details.name 'Uridine monophosphate'
+_pdbe_chem_comp_drugbank_details.description
+"5'-Uridylic acid. A uracil nucleotide containing one phosphate group esterified to the sugar moiety in the 2', 3' or 5' position."
+_pdbe_chem_comp_drugbank_details.cas_number 58-97-9
+_pdbe_chem_comp_drugbank_details.mechanism_of_action ?
+#
+loop_
+_pdbe_chem_comp_synonyms.comp_id
+_pdbe_chem_comp_synonyms.name
+_pdbe_chem_comp_synonyms.provenance
+_pdbe_chem_comp_synonyms.type
+U "5'-uridylic acid" DrugBank ?
+U UMP DrugBank ?
+U "Uridine 5'-monophosphate" DrugBank ?
+U "Uridine 5'-phosphoric acid" DrugBank ?
+U 'Uridine phosphate' DrugBank ?
+U "Uridine-5'-monophosphate" DrugBank ?
+#
+_pdbe_chem_comp_drugbank_classification.comp_id U
+_pdbe_chem_comp_drugbank_classification.drugbank_id DB03685
+_pdbe_chem_comp_drugbank_classification.parent 'Pyrimidine ribonucleoside monophosphates'
+_pdbe_chem_comp_drugbank_classification.kingdom 'Organic compounds'
+_pdbe_chem_comp_drugbank_classification.class 'Pyrimidine nucleotides'
+_pdbe_chem_comp_drugbank_classification.superclass 'Nucleosides, nucleotides, and analogues'
+_pdbe_chem_comp_drugbank_classification.description
+'This compound belongs to the class of organic compounds known as pyrimidine ribonucleoside monophosphates. These are pyrimidine ribobucleotides with monophosphate group  linked to the ribose moiety.'
+#
+loop_
+_pdbe_chem_comp_drugbank_targets.comp_id
+_pdbe_chem_comp_drugbank_targets.drugbank_id
+_pdbe_chem_comp_drugbank_targets.name
+_pdbe_chem_comp_drugbank_targets.organism
+_pdbe_chem_comp_drugbank_targets.uniprot_id
+_pdbe_chem_comp_drugbank_targets.pharmacologically_active
+_pdbe_chem_comp_drugbank_targets.ordinal
+U DB03685 'Uridine-cytidine kinase-like 1' Humans Q9NWZ5 unknown 1
+U DB03685 'Galactose-1-phosphate uridylyltransferase' 'Escherichia coli (strain K12)' P09148 unknown 2
+U DB03685 'Thymidylate synthase' 'Lactobacillus casei' P00469 unknown 3
+U DB03685 'Glycosyltransferase 6 domain-containing protein 1' Humans Q7Z4J2 unknown 4
+U DB03685 'U6 snRNA-associated Sm-like protein LSm6' Humans P62312 unknown 5
+U DB03685 'Uracil phosphoribosyltransferase' 'Thermotoga maritima (strain ATCC 43589 / MSB8 / DSM 3109 / JCM 10099)' Q9WZI0 unknown 6
+U DB03685 'Beta-1,4-galactosyltransferase 1' Humans P15291 unknown 7
+U DB03685 'Interferon-stimulated gene 20 kDa protein' Humans Q96AZ6 unknown 8
+U DB03685 "Orotidine 5'-phosphate decarboxylase" 'Bacillus subtilis (strain 168)' P25971 unknown 9
+U DB03685 'Bifunctional protein PyrR' 'Bacillus caldolyticus' P41007 unknown 10
+U DB03685 'Uracil phosphoribosyltransferase' 'Geobacillus kaustophilus (strain HTA426)' Q5KUI3 unknown 11
+U DB03685 'Bifunctional polymyxin resistance protein ArnA' 'Escherichia coli (strain K12)' P77398 unknown 12
+U DB03685 'Ribosomal small subunit pseudouridine synthase A' 'Escherichia coli (strain K12)' P0AA43 unknown 13
+#
+loop_
+_software.name
+_software.version
+_software.description
+rdkit 2023.09.6 'Core functionality.'
+pdbeccdutils 0.8.6 'Wrapper to provide 2D templates and molecular fragments.'
+#
+loop_
+_pdbe_chem_comp_atom_depiction.comp_id
+_pdbe_chem_comp_atom_depiction.atom_id
+_pdbe_chem_comp_atom_depiction.element
+_pdbe_chem_comp_atom_depiction.model_Cartn_x
+_pdbe_chem_comp_atom_depiction.model_Cartn_y
+_pdbe_chem_comp_atom_depiction.pdbx_ordinal
+U OP3 O 11.218 -5.896 1
+U P P 10.336 -4.683 2
+U OP1 O 11.550 -3.801 3
+U OP2 O 9.123 -5.564 4
+U "O5'" O 9.455 -3.469 5
+U "C5'" C 7.963 -3.626 6
+U "C4'" C 7.081 -2.412 7
+U "O4'" O 7.545 -0.986 8
+U "C3'" C 5.581 -2.412 9
+U "O3'" O 4.699 -3.626 10
+U "C2'" C 5.118 -0.986 11
+U "O2'" O 3.691 -0.522 12
+U "C1'" C 6.331 -0.104 13
+U N1 N 6.331 1.396 14
+U C2 C 7.630 2.146 15
+U O2 O 8.929 1.396 16
+U N3 N 7.630 3.646 17
+U C4 C 6.331 4.396 18
+U O4 O 6.331 5.896 19
+U C5 C 5.032 3.646 20
+U C6 C 5.032 2.146 21
+#
+loop_
+_pdbe_chem_comp_bond_depiction.comp_id
+_pdbe_chem_comp_bond_depiction.atom_id_1
+_pdbe_chem_comp_bond_depiction.atom_id_2
+_pdbe_chem_comp_bond_depiction.value_order
+_pdbe_chem_comp_bond_depiction.bond_dir
+_pdbe_chem_comp_bond_depiction.pdbx_ordinal
+U OP3 P SINGLE NONE 1
+U P OP1 DOUBLE NONE 2
+U P OP2 SINGLE NONE 3
+U P "O5'" SINGLE NONE 4
+U "O5'" "C5'" SINGLE NONE 5
+U "C4'" "C5'" SINGLE BEGINDASH 6
+U "C4'" "O4'" SINGLE NONE 7
+U "C4'" "C3'" SINGLE NONE 8
+U "O4'" "C1'" SINGLE NONE 9
+U "C3'" "O3'" SINGLE BEGINWEDGE 10
+U "C3'" "C2'" SINGLE NONE 11
+U "C2'" "O2'" SINGLE BEGINWEDGE 12
+U "C2'" "C1'" SINGLE NONE 13
+U "C1'" N1 SINGLE BEGINDASH 14
+U N1 C2 SINGLE NONE 15
+U N1 C6 SINGLE NONE 16
+U C2 O2 DOUBLE NONE 17
+U C2 N3 SINGLE NONE 18
+U N3 C4 SINGLE NONE 19
+U C4 O4 DOUBLE NONE 20
+U C4 C5 SINGLE NONE 21
+U C5 C6 DOUBLE NONE 22
+#
+loop_
+_pdbe_chem_comp_substructure.comp_id
+_pdbe_chem_comp_substructure.substructure_name
+_pdbe_chem_comp_substructure.id
+_pdbe_chem_comp_substructure.substructure_type
+_pdbe_chem_comp_substructure.substructure_smiles
+_pdbe_chem_comp_substructure.substructure_inchis
+_pdbe_chem_comp_substructure.substructure_inchikeys
+U MurckoScaffold S1 scaffold 'O=c1ccn([C@H]2CCCO2)c(=O)[nH]1' InChI=1S/C8H10N2O3/c11-6-3-4-10(8(12)9-6)7-2-1-5-13-7/h3-4,7H,1-2,5H2,(H,9,11,12)/t7-/m1/s1 CWWIKVUHBBTKHC-SSDOTTSWSA-N
+U phosphate F1 fragment O=P(O)(O)O InChI=1S/H3O4P/c1-5(2,3)4/h(H3,1,2,3,4) NBIIXXVUZAFLBC-UHFFFAOYSA-N
+U pyrimidine F2 fragment c1cncnc1 InChI=1S/C4H4N2/c1-2-5-4-6-3-1/h1-4H CZPWVGJYEJSRLH-UHFFFAOYSA-N
+U ribose F3 fragment OCC1OCC(O)C1O InChI=1S/C5H10O4/c6-1-4-5(8)3(7)2-9-4/h3-8H,1-2H2 KZVAAIRBJJYZOW-UHFFFAOYSA-N
+#
+loop_
+_pdbe_chem_comp_substructure_mapping.comp_id
+_pdbe_chem_comp_substructure_mapping.atom_id
+_pdbe_chem_comp_substructure_mapping.substructure_id
+_pdbe_chem_comp_substructure_mapping.substructure_ordinal
+U "C4'" S1 1
+U "O4'" S1 1
+U "C3'" S1 1
+U "C2'" S1 1
+U "C1'" S1 1
+U N1 S1 1
+U C2 S1 1
+U O2 S1 1
+U N3 S1 1
+U C4 S1 1
+U O4 S1 1
+U C5 S1 1
+U C6 S1 1
+U OP3 F1 1
+U P F1 1
+U OP1 F1 1
+U OP2 F1 1
+U "O5'" F1 1
+U C5 F2 1
+U C4 F2 1
+U N3 F2 1
+U C2 F2 1
+U N1 F2 1
+U C6 F2 1
+U "C2'" F3 1
+U "C3'" F3 1
+U "C4'" F3 1
+U "O4'" F3 1
+U "C1'" F3 1
+U "C5'" F3 1
+U "O5'" F3 1
+U "O3'" F3 1
+U "O2'" F3 1
+#
+_pdbe_chem_comp_rdkit_properties.comp_id U
+_pdbe_chem_comp_rdkit_properties.exactmw 324.036
+_pdbe_chem_comp_rdkit_properties.amw 324.182
+_pdbe_chem_comp_rdkit_properties.lipinskiHBA 11
+_pdbe_chem_comp_rdkit_properties.lipinskiHBD 5
+_pdbe_chem_comp_rdkit_properties.NumRotatableBonds 8
+_pdbe_chem_comp_rdkit_properties.NumHBD 5
+_pdbe_chem_comp_rdkit_properties.NumHBA 10
+_pdbe_chem_comp_rdkit_properties.NumHeavyAtoms 21
+_pdbe_chem_comp_rdkit_properties.NumAtoms 34
+_pdbe_chem_comp_rdkit_properties.NumHeteroatoms 12
+_pdbe_chem_comp_rdkit_properties.NumAmideBonds 0
+_pdbe_chem_comp_rdkit_properties.FractionCSP3 0.556
+_pdbe_chem_comp_rdkit_properties.NumRings 2
+_pdbe_chem_comp_rdkit_properties.NumAromaticRings 1
+_pdbe_chem_comp_rdkit_properties.NumAliphaticRings 1
+_pdbe_chem_comp_rdkit_properties.NumSaturatedRings 1
+_pdbe_chem_comp_rdkit_properties.NumHeterocycles 2
+_pdbe_chem_comp_rdkit_properties.NumAromaticHeterocycles 1
+_pdbe_chem_comp_rdkit_properties.NumSaturatedHeterocycles 1
+_pdbe_chem_comp_rdkit_properties.NumAliphaticHeterocycles 1
+_pdbe_chem_comp_rdkit_properties.NumSpiroAtoms 0
+_pdbe_chem_comp_rdkit_properties.NumBridgeheadAtoms 0
+_pdbe_chem_comp_rdkit_properties.NumAtomStereoCenters 4
+_pdbe_chem_comp_rdkit_properties.NumUnspecifiedAtomStereoCenters 0
+_pdbe_chem_comp_rdkit_properties.labuteASA 134.846
+_pdbe_chem_comp_rdkit_properties.tpsa 171.310
+_pdbe_chem_comp_rdkit_properties.CrippenClogP -0.935
+_pdbe_chem_comp_rdkit_properties.CrippenMR 65.458
+_pdbe_chem_comp_rdkit_properties.chi0v 10.410
+_pdbe_chem_comp_rdkit_properties.chi1v 6.238
+_pdbe_chem_comp_rdkit_properties.chi2v 2.237
+_pdbe_chem_comp_rdkit_properties.chi3v 2.237
+_pdbe_chem_comp_rdkit_properties.chi4v 1.350
+_pdbe_chem_comp_rdkit_properties.chi0n 22.516
+_pdbe_chem_comp_rdkit_properties.chi1n 10.857
+_pdbe_chem_comp_rdkit_properties.chi2n 1.922
+_pdbe_chem_comp_rdkit_properties.chi3n 1.922
+_pdbe_chem_comp_rdkit_properties.chi4n 1.155
+_pdbe_chem_comp_rdkit_properties.hallKierAlpha -1.330
+_pdbe_chem_comp_rdkit_properties.kappa1 6.048
+_pdbe_chem_comp_rdkit_properties.kappa2 5.812
+_pdbe_chem_comp_rdkit_properties.kappa3 3.656
+_pdbe_chem_comp_rdkit_properties.Phi 1.674
+#
+loop_
+_pdbe_chem_comp_external_mappings.comp_id
+_pdbe_chem_comp_external_mappings.source
+_pdbe_chem_comp_external_mappings.resource
+_pdbe_chem_comp_external_mappings.resource_id
+U UniChem ChEMBL CHEMBL214393
+U UniChem DrugBank DB03685
+U UniChem 'Guide to Pharmacology' 5125
+U UniChem 'KEGG LIGAND' C00105
+U UniChem ChEBI 16695
+U UniChem ZINC ZINC000002123545
+U UniChem fdasrs E2OU15WN0N
+U UniChem SureChEMBL SCHEMBL157644
+U UniChem HMDB HMDB0000288
+U UniChem 'PubChem TPHARMA' 14846393
+U UniChem 'PubChem TPHARMA' 14899484
+U UniChem PubChem 6030
+U UniChem NMRShiftDB 60020947
+U UniChem ACTor 27416-86-0
+U UniChem ACTor 53624-79-6
+U UniChem ACTor 86472-36-8
+U UniChem Nikkaji J4.594B
+U UniChem BindingDb 50398699
+U UniChem 'EPA CompTox Dashboard' DTXSID20883211
+U UniChem MetaboLights MTBLC16695
+U UniChem BRENDA 101700
+U UniChem BRENDA 113279
+U UniChem BRENDA 124492
+U UniChem BRENDA 133
+U UniChem BRENDA 140759
+U UniChem BRENDA 24533
+U UniChem BRENDA 7434
+U UniChem BRENDA 915
+U UniChem BRENDA 95340
+U UniChem ChemicalBook CB1271654
+U UniChem MedChemExpress HY-101981
+U UniChem 'Probes And Drugs' PD051725
+U UniChem CCDC QAXGUV
+#
+loop_
+_pdbe_chem_comp_rdkit_conformer.comp_id
+_pdbe_chem_comp_rdkit_conformer.atom_id
+_pdbe_chem_comp_rdkit_conformer.Cartn_x_rdkit
+_pdbe_chem_comp_rdkit_conformer.Cartn_y_rdkit
+_pdbe_chem_comp_rdkit_conformer.Cartn_z_rdkit
+_pdbe_chem_comp_rdkit_conformer.rdkit_method
+_pdbe_chem_comp_rdkit_conformer.rdkit_ordinal
+U OP3 -3.956 1.729 2.677 ETKDGv3 1
+U P -4.263 0.891 1.237 ETKDGv3 2
+U OP1 -4.203 -0.600 1.487 ETKDGv3 3
+U OP2 -5.813 1.296 0.695 ETKDGv3 4
+U "O5'" -3.130 1.326 0.037 ETKDGv3 5
+U "C5'" -1.834 1.125 0.551 ETKDGv3 6
+U "C4'" -0.803 1.383 -0.554 ETKDGv3 7
+U "O4'" 0.495 1.343 0.006 ETKDGv3 8
+U "C3'" -0.905 0.344 -1.686 ETKDGv3 9
+U "O3'" -0.888 0.986 -2.936 ETKDGv3 10
+U "C2'" 0.343 -0.484 -1.498 ETKDGv3 11
+U "O2'" 0.891 -0.919 -2.715 ETKDGv3 12
+U "C1'" 1.274 0.491 -0.801 ETKDGv3 13
+U N1 2.280 -0.219 0.007 ETKDGv3 14
+U C2 1.957 -0.802 1.289 ETKDGv3 15
+U O2 0.798 -0.702 1.774 ETKDGv3 16
+U N3 2.964 -1.508 2.015 ETKDGv3 17
+U C4 4.265 -1.655 1.445 ETKDGv3 18
+U O4 5.165 -2.273 2.072 ETKDGv3 19
+U C5 4.546 -1.085 0.122 ETKDGv3 20
+U C6 3.599 -0.417 -0.544 ETKDGv3 21
+U HOP3 -3.289 1.179 3.161 ETKDGv3 22
+U HOP2 -5.899 0.846 -0.184 ETKDGv3 23
+U "H5'" -1.700 0.084 0.925 ETKDGv3 24
+U "H5''" -1.637 1.842 1.379 ETKDGv3 25
+U "H4'" -0.977 2.408 -0.953 ETKDGv3 26
+U "H3'" -1.806 -0.309 -1.606 ETKDGv3 27
+U "HO3'" -1.801 1.348 -3.079 ETKDGv3 28
+U "H2'" 0.123 -1.352 -0.833 ETKDGv3 29
+U "HO2'" 0.306 -1.654 -3.037 ETKDGv3 30
+U "H1'" 1.781 1.128 -1.563 ETKDGv3 31
+U H3 2.749 -1.922 2.951 ETKDGv3 32
+U H5 5.527 -1.210 -0.320 ETKDGv3 33
+U H6 3.840 -0.017 -1.522 ETKDGv3 34
+#

nucleotides/U.pdb

@@ -0,0 +1,69 @@
+HETATM    1  OP3   U     0      -2.122   1.033  -4.690  0.00  0.00           O  
+HETATM    2  P     U     0      -1.030   0.047  -4.037  0.00  0.00           P  
+HETATM    3  OP1   U     0      -1.679  -1.228  -3.660  0.00  0.00           O  
+HETATM    4  OP2   U     0       0.138  -0.241  -5.107  0.00  0.00           O  
+HETATM    5  O5'   U     0      -0.399   0.736  -2.726  0.00  0.00           O  
+HETATM    6  C5'   U     0       0.557  -0.182  -2.196  0.00  0.00           C  
+HETATM    7  C4'   U     0       1.197   0.415  -0.942  0.00  0.00           C  
+HETATM    8  O4'   U     0       0.194   0.645   0.074  0.00  0.00           O  
+HETATM    9  C3'   U     0       2.181  -0.588  -0.301  0.00  0.00           C  
+HETATM   10  O3'   U     0       3.524  -0.288  -0.686  0.00  0.00           O  
+HETATM   11  C2'   U     0       1.995  -0.383   1.218  0.00  0.00           C  
+HETATM   12  O2'   U     0       3.219   0.046   1.819  0.00  0.00           O  
+HETATM   13  C1'   U     0       0.922   0.723   1.319  0.00  0.00           C  
+HETATM   14  N1    U     0       0.028   0.464   2.451  0.00  0.00           N  
+HETATM   15  C2    U     0      -0.690  -0.671   2.486  0.00  0.00           C  
+HETATM   16  O2    U     0      -0.587  -1.474   1.580  0.00  0.00           O  
+HETATM   17  N3    U     0      -1.515  -0.936   3.517  0.00  0.00           N  
+HETATM   18  C4    U     0      -1.641  -0.055   4.530  0.00  0.00           C  
+HETATM   19  O4    U     0      -2.391  -0.292   5.460  0.00  0.00           O  
+HETATM   20  C5    U     0      -0.894   1.146   4.502  0.00  0.00           C  
+HETATM   21  C6    U     0      -0.070   1.384   3.459  0.00  0.00           C  
+HETATM   22 HOP3   U     0      -2.475   0.583  -5.470  0.00  0.00           H  
+HETATM   23 HOP2   U     0       0.534   0.613  -5.325  0.00  0.00           H  
+HETATM   24  H5'   U     0       1.329  -0.373  -2.942  0.00  0.00           H  
+HETATM   25 H5''   U     0       0.060  -1.117  -1.940  0.00  0.00           H  
+HETATM   26  H4'   U     0       1.712   1.345  -1.185  0.00  0.00           H  
+HETATM   27  H3'   U     0       1.923  -1.609  -0.583  0.00  0.00           H  
+HETATM   28 HO3'   U     0       4.094  -0.926  -0.234  0.00  0.00           H  
+HETATM   29  H2'   U     0       1.643  -1.301   1.688  0.00  0.00           H  
+HETATM   30 HO2'   U     0       3.865  -0.657   1.671  0.00  0.00           H  
+HETATM   31  H1'   U     0       1.392   1.700   1.423  0.00  0.00           H  
+HETATM   32  H3    U     0      -2.024  -1.762   3.528  0.00  0.00           H  
+HETATM   33  H5    U     0      -0.982   1.863   5.305  0.00  0.00           H  
+HETATM   34  H6    U     0       0.507   2.295   3.421  0.00  0.00           H  
+CONECT    1    2   22
+CONECT    2    1    3    4    5
+CONECT    3    2
+CONECT    4    2   23
+CONECT    5    2    6
+CONECT    6    5    7   24   25
+CONECT    7    6    8    9   26
+CONECT    8    7   13
+CONECT    9    7   10   11   27
+CONECT   10    9   28
+CONECT   11    9   12   13   29
+CONECT   12   11   30
+CONECT   13    8   11   14   31
+CONECT   14   13   15   21
+CONECT   15   14   16   17
+CONECT   16   15
+CONECT   17   15   18   32
+CONECT   18   17   19   20
+CONECT   19   18
+CONECT   20   18   21   33
+CONECT   21   14   20   34
+CONECT   22    1
+CONECT   23    4
+CONECT   24    6
+CONECT   25    6
+CONECT   26    7
+CONECT   27    9
+CONECT   28   10
+CONECT   29   11
+CONECT   30   12
+CONECT   31   13
+CONECT   32   17
+CONECT   33   20
+CONECT   34   21
+END

nucleotides/atom_mask_count.json

@@ -0,0 +1,200 @@
+{
+    "total": {
+        "A": {
+            "OP3": 768544,
+            "P": 768544,
+            "OP1": 768544,
+            "OP2": 768544,
+            "O5'": 768544,
+            "C5'": 768544,
+            "C4'": 768544,
+            "O4'": 768544,
+            "C3'": 768544,
+            "O3'": 768544,
+            "C2'": 768544,
+            "O2'": 768544,
+            "C1'": 768544,
+            "N9": 768544,
+            "C8": 768544,
+            "N7": 768544,
+            "C5": 768544,
+            "C6": 768544,
+            "N6": 768544,
+            "N1": 768544,
+            "C2": 768544,
+            "N3": 768544,
+            "C4": 768544
+        },
+        "C": {
+            "OP3": 809047,
+            "P": 809047,
+            "OP1": 809047,
+            "OP2": 809047,
+            "O5'": 809047,
+            "C5'": 809047,
+            "C4'": 809047,
+            "O4'": 809047,
+            "C3'": 809047,
+            "O3'": 809047,
+            "C2'": 809047,
+            "O2'": 809047,
+            "C1'": 809047,
+            "N1": 809047,
+            "C2": 809047,
+            "O2": 809047,
+            "N3": 809047,
+            "C4": 809047,
+            "N4": 809047,
+            "C5": 809047,
+            "C6": 809047
+        },
+        "G": {
+            "OP3": 1012275,
+            "P": 1012275,
+            "OP1": 1012275,
+            "OP2": 1012275,
+            "O5'": 1012275,
+            "C5'": 1012275,
+            "C4'": 1012275,
+            "O4'": 1012275,
+            "C3'": 1012275,
+            "O3'": 1012275,
+            "C2'": 1012275,
+            "O2'": 1012275,
+            "C1'": 1012275,
+            "N9": 1012275,
+            "C8": 1012275,
+            "N7": 1012275,
+            "C5": 1012275,
+            "C6": 1012275,
+            "O6": 1012275,
+            "N1": 1012275,
+            "C2": 1012275,
+            "N2": 1012275,
+            "N3": 1012275,
+            "C4": 1012275
+        },
+        "U": {
+            "OP3": 618673,
+            "P": 618673,
+            "OP1": 618673,
+            "OP2": 618673,
+            "O5'": 618673,
+            "C5'": 618673,
+            "C4'": 618673,
+            "O4'": 618673,
+            "C3'": 618673,
+            "O3'": 618673,
+            "C2'": 618673,
+            "O2'": 618673,
+            "C1'": 618673,
+            "N1": 618673,
+            "C2": 618673,
+            "O2": 618673,
+            "N3": 618673,
+            "C4": 618673,
+            "O4": 618673,
+            "C5": 618673,
+            "C6": 618673
+        }
+    },
+    "mask": {
+        "A": {
+            "OP3": 768350,
+            "P": 667,
+            "OP1": 7523,
+            "OP2": 7520,
+            "C5'": 6909,
+            "C4'": 6902,
+            "O4'": 6987,
+            "C3'": 6902,
+            "O3'": 6877,
+            "C2'": 7010,
+            "O2'": 7060,
+            "C1'": 7010,
+            "N9": 9257,
+            "C8": 9339,
+            "N7": 9300,
+            "C5": 9299,
+            "C6": 9301,
+            "N6": 9301,
+            "N1": 9300,
+            "C2": 9300,
+            "N3": 9303,
+            "C4": 9301,
+            "O5'": 6886
+        },
+        "C": {
+            "OP3": 808773,
+            "O2'": 7247,
+            "N1": 8884,
+            "C2": 8907,
+            "C4": 8908,
+            "C6": 8906,
+            "P": 675,
+            "OP1": 7697,
+            "OP2": 7691,
+            "O5'": 6973,
+            "C5'": 7114,
+            "C4'": 7174,
+            "O4'": 7192,
+            "C3'": 7112,
+            "O3'": 7106,
+            "C2'": 7203,
+            "C1'": 7203,
+            "O2": 8906,
+            "N3": 8906,
+            "N4": 8908,
+            "C5": 8906
+        },
+        "G": {
+            "OP3": 1011609,
+            "O6": 11152,
+            "P": 1482,
+            "OP1": 10317,
+            "OP2": 10320,
+            "O5'": 8839,
+            "C5'": 8827,
+            "C4'": 8820,
+            "O4'": 8896,
+            "C3'": 8820,
+            "O3'": 8801,
+            "C2'": 8902,
+            "O2'": 8994,
+            "C1'": 8902,
+            "N9": 11076,
+            "C8": 11098,
+            "N7": 11098,
+            "C5": 11100,
+            "C6": 11100,
+            "N1": 11102,
+            "C2": 11101,
+            "N2": 11102,
+            "N3": 11100,
+            "C4": 11101
+        },
+        "U": {
+            "OP3": 618113,
+            "OP1": 7535,
+            "OP2": 7548,
+            "O5'": 6435,
+            "C5'": 6440,
+            "C4'": 6527,
+            "O4'": 6614,
+            "C3'": 6525,
+            "O3'": 6516,
+            "C2'": 6637,
+            "O2'": 6710,
+            "C1'": 6638,
+            "N1": 8856,
+            "C2": 8900,
+            "O2": 8914,
+            "N3": 8899,
+            "C4": 8902,
+            "O4": 8924,
+            "C5": 8900,
+            "C6": 8913,
+            "P": 1135
+        }
+    }
+}

nucleotides/load_nucleotide.py

@@ -0,0 +1,152 @@
+from rdkit import Chem
+# from torch_geometric.utils import from_rdmol
+from torch_geometric.utils.smiles import x_map, e_map
+from typing import Any, List
+import torch
+import torch.nn.functional as F
+from itertools import product
+
+
+def from_rdmol(mol: Any, use_atom_id=False) -> 'torch_geometric.data.Data':
+    r"""Converts a :class:`rdkit.Chem.Mol` instance to a
+    :class:`torch_geometric.data.Data` instance.
+
+    Args:
+        mol (rdkit.Chem.Mol): The :class:`rdkit` molecule.
+    """
+    from rdkit import Chem
+
+    from torch_geometric.data import Data
+
+    assert isinstance(mol, Chem.Mol)
+
+    xs: List[List[int]] = []
+    for i, atom in enumerate(mol.GetAtoms()):  # type: ignore
+        row: List[int] = []
+        row.append(x_map['atomic_num'].index(atom.GetAtomicNum()))
+        row.append(x_map['chirality'].index(str(atom.GetChiralTag())))
+        row.append(x_map['degree'].index(atom.GetTotalDegree()))
+        row.append(x_map['formal_charge'].index(atom.GetFormalCharge()))
+        row.append(x_map['num_hs'].index(atom.GetTotalNumHs()))
+        row.append(x_map['num_radical_electrons'].index(
+            atom.GetNumRadicalElectrons()))
+        row.append(x_map['hybridization'].index(str(atom.GetHybridization())))
+        row.append(x_map['is_aromatic'].index(atom.GetIsAromatic()))
+        row.append(x_map['is_in_ring'].index(atom.IsInRing()))
+        if use_atom_id:
+            row.append(i)
+        xs.append(row)
+
+    if use_atom_id:
+        x = torch.tensor(xs, dtype=torch.long).view(-1, 10)
+    else:
+        x = torch.tensor(xs, dtype=torch.long).view(-1, 9)
+    x_one_hot = []
+    x_one_hot.append(F.one_hot(x[:, 0], num_classes=len(x_map['atomic_num'])).float())
+    x_one_hot.append(F.one_hot(x[:, 1], num_classes=len(x_map['chirality'])).float())
+    x_one_hot.append(F.one_hot(x[:, 2], num_classes=len(x_map['degree'])).float())
+    x_one_hot.append(F.one_hot(x[:, 3], num_classes=len(x_map['formal_charge'])).float())
+    x_one_hot.append(F.one_hot(x[:, 4], num_classes=len(x_map['num_hs'])).float())
+    x_one_hot.append(F.one_hot(x[:, 5], num_classes=len(x_map['num_radical_electrons'])).float())
+    x_one_hot.append(F.one_hot(x[:, 6], num_classes=len(x_map['hybridization'])).float())
+    x_one_hot.append(F.one_hot(x[:, 7], num_classes=len(x_map['is_aromatic'])).float())
+    x_one_hot.append(F.one_hot(x[:, 8], num_classes=len(x_map['is_in_ring'])).float())
+    if use_atom_id:
+        x_one_hot.append(F.one_hot(x[:, 9], num_classes=24).float())
+    x = torch.cat(x_one_hot, dim=-1)
+
+    edge_indices, edge_attrs = [], []
+    for bond in mol.GetBonds():  # type: ignore
+        i = bond.GetBeginAtomIdx()
+        j = bond.GetEndAtomIdx()
+
+        e = []
+        e.append(e_map['bond_type'].index(str(bond.GetBondType())))
+        e.append(e_map['stereo'].index(str(bond.GetStereo())))
+        e.append(e_map['is_conjugated'].index(bond.GetIsConjugated()))
+
+        edge_indices += [[i, j], [j, i]]
+        edge_attrs += [e, e]
+
+    edge_index = torch.tensor(edge_indices)
+    edge_index = edge_index.t().to(torch.long).view(2, -1)
+    edge_attr = torch.tensor(edge_attrs, dtype=torch.long).view(-1, 3)
+    edge_attr_one_hot = []
+    edge_attr_one_hot.append(F.one_hot(edge_attr[:, 0], num_classes=len(e_map['bond_type'])).float())
+    edge_attr_one_hot.append(F.one_hot(edge_attr[:, 1], num_classes=len(e_map['stereo'])).float())
+    edge_attr_one_hot.append(F.one_hot(edge_attr[:, 2], num_classes=len(e_map['is_conjugated'])).float())
+    edge_attr = torch.cat(edge_attr_one_hot, dim=-1)
+
+    if edge_index.numel() > 0:  # Sort indices.
+        perm = (edge_index[0] * x.size(0) + edge_index[1]).argsort()
+        edge_index, edge_attr = edge_index[:, perm], edge_attr[perm]
+
+    return Data(x=x, edge_index=edge_index, edge_attr=edge_attr)
+
+def load_standard_nucleotide(path, remove_hs):
+    mol = Chem.MolFromPDBFile(path, removeHs=remove_hs)
+    with open(path, 'r') as f:
+        lines = f.readlines()
+    mapping = {}
+    for line in lines:
+        line = line.strip()
+        if line.startswith('HETATM'):
+            line = line.split()
+            idx = int(line[1])
+            atom_name = line[2]
+            atom_type = line[-1]
+            if remove_hs and atom_type == 'H':
+                continue
+            mapping[atom_name] = (idx-1, atom_type)
+            assert idx == len(mapping) # this is to ensure no Hydrogen are deleted in the middle of lines
+    
+    reverse_mapping = {v[0]: v[1] for k, v in mapping.items()}
+    ## sanity check
+    for atom in mol.GetAtoms():
+        idx = atom.GetIdx()
+        atom_name = atom.GetSymbol()
+        assert reverse_mapping[idx] == atom_name
+    return mol, mapping
+
+
+def get_full_edge(x, edge_index, edge_attr):
+    num_atom = x.shape[0]
+    indices = list(product(range(num_atom), range(num_atom)))
+    indices.sort(key=lambda x: (x[0], x[1]))
+    full_edge_index = torch.LongTensor(indices).t() # [2, N_edge]
+    full_edge_attr = torch.zeros((num_atom, num_atom, edge_attr.size(1)), dtype=edge_attr.dtype) # [num_atom, num_atom, edge_nf]
+    full_edge_attr[edge_index[0], edge_index[1]] = edge_attr
+    full_edge_attr = torch.cat([full_edge_attr, torch.eye(num_atom, dtype=full_edge_attr.dtype).unsqueeze(-1)], dim=-1) # [num_atom, num_atom, edge_nf + 1], add self-loop edge_attr
+    full_edge_attr = full_edge_attr.reshape(-1, full_edge_attr.size(-1)) # [N_edge, edge_nf + 1]
+    return full_edge_index, full_edge_attr
+
+
+def get_standard_nucleotide(remove_hs=False, use_atom_id=False):
+    nucleotides = {}
+    for n in ['A', 'G', 'C', 'U']:
+        mol, mapping = load_standard_nucleotide(f'/home/hui007/rna/DiffRNA/nucleotides/{n}.pdb', remove_hs)
+        data = from_rdmol(mol, use_atom_id)
+        full_edge_index, full_edge_attr = get_full_edge(data.x, data.edge_index, data.edge_attr)
+        data['edge_index'] = full_edge_index
+        data['edge_attr'] = full_edge_attr
+        nucleotides[n] = (mol, data, mapping)
+    return nucleotides
+
+
+if __name__ == "__main__":
+    # from pymol import cmd
+    # for nucleotide in ["A", "C", "G", "U"]:
+    #     cmd.load(f"{nucleotide}.cif", "molecule")
+    #     cmd.save(f"{nucleotide}.pdb", "molecule")
+    #     cmd.delete("all")
+    #     mol, mapping = load_standard_nucleotide(f"{nucleotide}.pdb", remove_hs=False)
+    #     data = from_rdmol(mol)
+    #     mapping = {k: v for k, v in mapping.items()}
+    out = get_standard_nucleotide(remove_hs=True, use_atom_id=False)
+    for v in out.values():
+        v = v[2]
+        # Sort mapping by index and print
+        sorted_items = sorted(v.items(), key=lambda x: x[1][0])
+        for atom_name, (idx, atom_type) in sorted_items:
+            print(f"{atom_name}: {idx} ({atom_type})")
+        print()  # Add a blank line between nucleotides

plot_loss.py

@@ -0,0 +1,82 @@
+import re
+import matplotlib.pyplot as plt
+
+log_file = '/home/hui007/rna/first_stage/log/train_20250811_2359_totalbatchsize256/train_20250811_2359.log'  # 替换为你的log文件名
+steps = []
+contrast_losses = []
+denoising_losses = []
+avg_losses = []
+
+with open(log_file, 'r') as f:
+    for line in f:
+        match = re.search(
+            r'Step: (\d+), avg_contrast_loss: ([-\d.]+), avg_denoising_loss: ([-\d.]+), Avg Loss: ([-\d.]+)',
+            line
+        )
+        if match:
+            step = int(match.group(1))
+            contrast_loss = float(match.group(2))
+            denoising_loss = float(match.group(3))
+            avg_loss = float(match.group(4))
+
+            steps.append(step)
+            contrast_losses.append(contrast_loss)
+            denoising_losses.append(denoising_loss)
+            avg_losses.append(avg_loss)
+
+# 画图
+plt.figure(figsize=(8, 5))
+plt.plot(steps, contrast_losses, label='Avg Contrast Loss')
+plt.plot(steps, denoising_losses, label='Avg Denoising Loss')
+plt.plot(steps, avg_losses, label='Avg Loss')
+
+plt.xlabel('Step')
+plt.ylabel('Loss')
+plt.title('Loss over Training Steps')
+plt.grid(True)
+plt.legend()
+plt.tight_layout()
+
+# 保存为 PNG 文件
+plt.savefig('/home/hui007/rna/first_stage/figures/loss_vs_step_v3.png', dpi=300)
+plt.close()
+
+# log_file = '/home/hui007/rna/first_stage/second_stage.log'  # 替换为你的log文件名
+# steps = []
+# avg_nce_losses = []
+# avg_mlm_losses = []
+# avg_losses = []
+
+# with open(log_file, 'r') as f:
+#     for line in f:
+#         match = re.search(
+#             r'Step: (\d+), avg_nce_loss: ([-\d.]+), avg_mlm_loss: ([-\d.]+), Avg Loss: ([-\d.]+)',
+#             line
+#         )
+#         if match:
+#             step = int(match.group(1))
+#             avg_nce_loss = float(match.group(2))
+#             avg_mlm_loss = float(match.group(3))
+#             avg_loss = float(match.group(4))
+
+#             steps.append(step)
+#             avg_nce_losses.append(avg_nce_loss)
+#             avg_mlm_losses.append(avg_mlm_loss)
+#             avg_losses.append(avg_loss)
+
+# # 画图
+# plt.figure(figsize=(8, 5))
+# plt.plot(steps, avg_nce_losses, label='Avg Contrast Loss')
+# plt.plot(steps, avg_mlm_losses, label='Avg MLM Loss')
+# plt.plot(steps, avg_losses, label='Avg Loss')
+
+# plt.xlabel('Step')
+# plt.ylabel('Loss')
+# plt.title('Loss over Training Steps')
+# plt.grid(True)
+# plt.legend()
+# plt.tight_layout()
+
+# # 保存为 PNG 文件
+# plt.savefig('/home/hui007/rna/first_stage/figures/loss_vs_step_2nd.png', dpi=300)
+# plt.close()

rna_dataset.py

@@ -0,0 +1,474 @@
+import os
+import random
+import math
+import torch
+import json
+from tqdm import tqdm
+import numpy as np
+from torch.utils.data import Dataset, DataLoader
+
+import torch.nn.functional as F
+
+from torch_geometric.data import Data
+import pickle
+from itertools import product
+from nucleotides.load_nucleotide import get_standard_nucleotide
+from scipy.spatial.transform import Rotation
+import copy
+from torch_geometric.data import Batch
+
+N1 = 0
+N9 = 0
+N_dis_list = []
+
+def continuous_cropping(data, rna_max_len):
+    rna_len = data.x.size(0)
+    start_idx = random.randint(0, rna_len - rna_max_len)
+    end_idx = start_idx + rna_max_len
+    data.x = data.x[start_idx:end_idx]
+    if hasattr(data, 'pos'):
+        data.pos = data.pos[start_idx:end_idx]
+        # add struc_emb [0403 by TIANRUI]
+        data.struc_emb = data.struc_emb[start_idx:end_idx]
+        # add protenix_emb [1406 by YIMING]
+        if hasattr(data, 'protenix_emb'):
+            data.protenix_emb = data.protenix_emb[start_idx:end_idx]
+    if hasattr(data, 'bead_pos'):
+        data.bead_pos = data.bead_pos[start_idx:end_idx]
+    edge_mask = (data.edge_index >= start_idx) & (data.edge_index < end_idx)
+    edge_mask = edge_mask.all(dim=0)
+    data.edge_index = data.edge_index[:, edge_mask] - start_idx
+    data.edge_attr = data.edge_attr[edge_mask]
+    data.pos_mask = data.pos_mask[start_idx:end_idx]
+    data.res_id = data.res_id[start_idx:end_idx]
+    data.residue_list = data.residue_list[start_idx:end_idx]
+    return data
+
+def spatial_cropping(data, rna_max_len):
+    rna_len = data.x.size(0)
+    if hasattr(data, 'pos') and data.pos.shape[1] == 9 and data.pos.dim() == 2:
+        c_pos = data.pos[:, 3:6] # shape = [num_res, 3]
+    elif hasattr(data, 'pos') and data.pos.dim() == 3 and data.pos.shape[1] == 3:
+        c_pos = data.pos[:, 0]
+    elif hasattr(data, 'pos') and data.pos.dim() == 3:
+        c_pos = data.pos[:, 6] # c4' is the 7th in pos
+    elif hasattr(data, 'bead_pos'):
+        c_pos = data.bead_pos[:, 3:6] # shape = [num_res, 3]
+    rand_center = c_pos[random.randint(0, rna_len - 1)].view(1, 3)
+    dist = torch.linalg.vector_norm(c_pos - rand_center, dim=1) # shape = [num_res]
+    topk = torch.topk(dist, rna_max_len, largest=False).indices
+    topk = topk.sort().values
+
+    ## update the node features
+    data.x = data.x[topk]
+    if hasattr(data, 'pos'):
+        data.pos = data.pos[topk]
+        # add struc_emb [0403 by TIANRUI]
+        data.struc_emb = data.struc_emb[topk]
+        # add protenix_emb [1406 by YIMING]
+        if hasattr(data, 'protenix_emb'):
+            data.protenix_emb = data.protenix_emb[topk]
+    if hasattr(data, 'bead_pos'):
+        data.bead_pos = data.bead_pos[topk]
+    data.pos_mask = data.pos_mask[topk]
+    data.res_id = data.res_id[topk]
+    data.residue_list = [data.residue_list[i] for i in topk.tolist()]
+
+    ## create a mapping from the original edge index to the new edge index
+    edge_mapping = torch.ones(rna_len, dtype=torch.long) * -1
+    edge_mapping[topk] = torch.arange(rna_max_len, dtype=torch.long)
+
+    ## update the edge index
+    new_edge_index = edge_mapping[data.edge_index.long()]
+    edge_mask = (new_edge_index >= 0).all(dim=0) # shape = [num_edges]
+    data.edge_index = new_edge_index[:, edge_mask]
+    data.edge_attr = data.edge_attr[edge_mask]
+    return data
+
+def cropping(data, rna_max_len, spatial_crop_ratio=0.5):
+    if random.random() < spatial_crop_ratio:
+        data = spatial_cropping(data, rna_max_len)
+    else:
+        data = continuous_cropping(data, rna_max_len)
+    return data
+
+def random_rotate_pos(data, seed=None):
+    """
+    对 data.pos 进行统一随机旋转增强，返回新 Data
+    - data.pos: [N, R, 3]，N 个节点，每个节点 R 个点
+    """
+    if seed is not None:
+        rng = np.random.default_rng(seed)
+    else:
+        rng = np.random.default_rng()
+
+    # 生成一个随机旋转矩阵
+    rot = Rotation.random(random_state=rng)
+    
+    # 提取 pos 并旋转
+    pos = data.pos.view(-1, 3)  # [N*R, 3]
+    rotated_pos = rot.apply(pos)             # [N*R, 3]
+    rotated_pos = torch.from_numpy(rotated_pos).to(data.pos.dtype)
+
+    data.pos = rotated_pos.view_as(data.pos)  # reshape back to [N, R, 3]
+    return data
+
+def random_translation_pos(data, translation_scale=0.1):
+    dtype = data.pos.dtype
+    trans_aug = translation_scale * torch.randn(3, dtype=dtype)
+    data.pos = data.pos + trans_aug.view(1, 1, 3)
+    return data
+
+def aug(data):
+    # random_num = random.random()
+    # if random_num < 1 / 3:
+    data = random_rotate_pos(data)
+    # elif random_num > 2 / 3:
+    data = random_translation_pos(data)
+    return data
+
+class RNADataset(Dataset):
+    def __init__(self, processed_path, rna_max_len=50, spatial_crop_ratio=0.5, disable_ss=False, mode='train', new_aa=True, new_res=False, fix_N_bug=False, struc_emb_path = '/home/hui007/rna/DiffRNA/data/RNAData20250323/struc_emb.pt', use_protenix_emb=True, protenix_emb_path='/home/hui007/protenix_0612/Protenix/protenix_embedding'):
+        super(RNADataset, self).__init__()
+        self.new_aa = new_aa
+        self.new_res = new_res
+        self.rna_max_len = rna_max_len
+        self.residue_types = {'A':0, 'G':1, 'U':2, 'C':3}
+        self.fix_N_bug = fix_N_bug
+        self.use_protenix_emb = use_protenix_emb
+        self.protenix_emb_path = protenix_emb_path
+        if not os.path.exists(processed_path):
+            raise FileNotFoundError(f'processed_path = `{processed_path}` not found! please preprocess it first!')
+        
+        ## load data
+        assert processed_path.endswith('.npz')
+        np_data = np.load(processed_path)
+        self.cluster_mapping = pickle.loads(np_data['clstr'])
+        # print(len(self.cluster_mapping), len(self.cluster_mapping[0]), len(self.cluster_mapping[0][0]), self.cluster_mapping[0][0])
+        self.data_list = pickle.loads(np_data['data_list'])
+        self.ss_list = {}
+        for ss_id in np_data.keys():
+            if ss_id.startswith('ss'):
+                new_id = int(ss_id.split('_')[1])
+                self.ss_list[new_id] = np_data[ss_id]
+
+        self.rna_class_idx = {'[unclassed]': 0, 'solo': 1, 'protein-RNA': 2, 'DNA-RNA': 3}
+        self.rnaclass = dict()
+        
+        # add struc_emb [0403 by TIANRUI]
+        self.struc_emb = torch.load(struc_emb_path)
+
+        self.mode = mode
+        self.disable_ss = disable_ss
+        self.pos_std = 20.3689
+        
+        self.spatial_crop_ratio = spatial_crop_ratio
+        self.aa_mapping = get_standard_nucleotide(True, False)
+
+        self.total_conf_num = len(self.data_list)
+        assert self.total_conf_num == sum([len(idx_list) for cluster in self.cluster_mapping for idx_list in cluster])
+
+        if self.mode == 'train':
+            self.num_seq = sum([len(cluster) for cluster in self.cluster_mapping])
+            self.seq_list = [inner for outer in self.cluster_mapping for inner in outer]
+
+    def __len__(self):
+        if self.mode == 'train':
+            return self.num_seq
+        elif self.mode in {'valid', 'test'}:
+            return self.total_conf_num
+        else:
+            raise ValueError(f'Unsupported mode: {self.mode}')
+
+    def len(self):
+        return self.__len__()
+
+    def get_idx_data(self, idx: int):
+        data = self.data_list[idx]
+        full_id = data['full_id']
+        
+        ## construct fully connected graph based on secondary structure
+        num_res = len(data['data'])
+        ss = self.ss_list[data['ss_id']]
+        ss = torch.from_numpy(ss).to(torch.float)
+        assert ss.shape[0] == num_res
+        indices = np.mgrid[0:num_res, 0:num_res].reshape(2, -1)
+        full_edge_index = torch.from_numpy(indices) # [2, N_edge]
+        full_edge_attr = ss.reshape(-1, ss.size(-1)) # [N_edge, dim]
+
+        rna_data = data['data']
+        x = []
+        residue_list = []
+        pos = []
+        pos_mask = []
+        # add struc_emb [0403 by TIANRUI]
+        struc_emb_list = []
+        for _, res, atom_coords in rna_data:
+            x.append(self.residue_types[res])
+            residue_list.append(res)
+            atom_list = atom_coords['atom_list']
+            coord_list = atom_coords['coord_list']
+
+            if self.new_aa:
+                atom_mapping = self.aa_mapping[res][-1]
+                aa_pos = torch.zeros((24, 3), dtype=torch.float)
+                aa_mask = torch.ones((24,), dtype=torch.bool)
+                for i, atom in enumerate(atom_list):
+                    idx = atom_mapping.get(atom, None)
+                    if idx is None:
+                        continue
+                    idx = idx[0]
+                    aa_pos[idx] = torch.from_numpy(coord_list[i])
+                    aa_mask[idx] = False
+                pos.append(aa_pos)
+                pos_mask.append(aa_mask)
+                # add struc_emb [0403 by TIANRUI]
+                struc_emb = self.struc_emb[res]
+                struc_emb_list.append(struc_emb)
+
+            else:
+                if self.new_res:
+                    atom_mapping = {'P': 0, 'C4\'': 1, 'N1': 2}
+                    bead_pos = torch.zeros((3, 3), dtype=torch.float)
+                    bead_pos_mask = torch.ones((3,), dtype=torch.bool)
+                    for i, atom in enumerate(atom_list):
+                        idx = atom_mapping.get(atom, None)
+                        if idx is None:
+                            continue
+                        bead_pos[idx] = torch.from_numpy(coord_list[i])
+                        bead_pos_mask[idx] = False
+                    pos.append(bead_pos)
+                    pos_mask.append(bead_pos_mask)
+                else:
+                    bead_idx = np.asarray([-1, -1, -1])
+                    for i, atom in enumerate(atom_list):
+                        if atom == 'P':
+                            bead_idx[0] = i
+                        elif atom == 'C4\'':
+                            bead_idx[1] = i
+                        else: 
+                            if self.fix_N_bug:
+                                if atom == 'N9' and (res == 'A' or res == 'G'):
+                                    bead_idx[2] = i
+                                elif atom == 'N1' and (res == 'C' or res == 'U'):
+                                    bead_idx[2] = i
+                            else:
+                                if atom == 'N1' or atom == 'N9':
+                                    bead_idx[2] = i
+                    if (bead_idx == -1).any():
+                        pos.append(torch.zeros((9,), dtype=torch.float))
+                        pos_mask.append(True)
+                    else:
+                        bead_pos = coord_list[bead_idx].reshape(-1) # shape = [9]
+                        pos.append(torch.from_numpy(bead_pos))
+                        pos_mask.append(False)
+
+        x = torch.LongTensor(x)
+        x = F.one_hot(x, num_classes=4).to(torch.float)
+        rna_class = self.rnaclass.get(full_id.split('-')[0], '[unclassed]')
+        rna_class = self.rna_class_idx[rna_class]
+        
+        if self.new_aa:
+            pos = torch.stack(pos, dim=0) # shape = [num_res, res_len, 3]
+            pos_mask = torch.stack(pos_mask, dim=0) # shape = [num_res, res_len]
+            data = Data(x=x, pos=pos, pos_mask=pos_mask, rna_seq_id=full_id, edge_index=full_edge_index, edge_attr=full_edge_attr, res_id=torch.arange(num_res), residue_list=residue_list)
+            data['class'] = rna_class
+            # add struc_emb [0403 by TIANRUI]
+            struc_emb = torch.stack(struc_emb_list, dim=0)
+            data['struc_emb'] = struc_emb
+            # add protenix embedding [1206 by YIMING]
+            if self.use_protenix_emb:
+                protenix_emb = os.path.join(self.protenix_emb_path, f'{full_id}.pt')
+                if os.path.exists(protenix_emb):
+                    data['protenix_emb'] = torch.load(protenix_emb, map_location='cpu')
+                else:
+                    data['protenix_emb'] = torch.zeros(x.shape[0], 384, device='cpu')
+        else:
+            if self.new_res:
+                pos_mask = torch.stack(pos_mask, dim=0)
+            else:
+                pos_mask = torch.BoolTensor(pos_mask)
+            pos = torch.stack(pos, dim=0)
+            data = Data(x=x, pos=pos, pos_mask=pos_mask, rna_seq_id=full_id, edge_index=full_edge_index, edge_attr=full_edge_attr, res_id=torch.arange(num_res), residue_list=residue_list)
+            data['class'] = rna_class
+        return data
+
+    def __getitem__(self, idx):
+        if self.mode == 'train':
+            assert 0 <= idx < self.num_seq
+            data_id = random.choices(self.seq_list[idx])[0]
+            data = self.get_idx_data(data_id)
+        elif self.mode in {'valid', 'test'}:
+            data = self.get_idx_data(idx)
+        else:
+            raise ValueError(f'Unsupported mode: {self.mode}')
+        
+        if self.mode == 'test':
+            if self.new_aa:
+                ## center the data
+                pos_unmask = ~data.pos_mask # shape = [num_res, res_len]
+                centroid = data.pos.reshape(-1, 3).sum(dim=0, keepdim=True) / torch.clamp(pos_unmask.sum(), min=1) # shape = [1, 3]
+                data.pos = (data.pos - centroid.unsqueeze(0)) * pos_unmask.unsqueeze(-1)
+            else:
+                if self.new_res:
+                    ## center the data
+                    pos_unmask = ~data.pos_mask # shape = [num_res, 3]
+                    centroid = data.pos.reshape(-1, 3).sum(dim=0, keepdim=True) / torch.clamp(pos_unmask.sum(), min=1) # shape = [1, 3]
+                    data.pos = (data.pos - centroid.view(1, 1, 3)) * pos_unmask.view(-1, 3, 1)
+                else:
+                    ## center the data
+                    pos_unmask = ~data.pos_mask # shape = [num_res]
+                    centroid = data.pos.reshape(-1, 3).sum(dim=0, keepdim=True) / torch.clamp((pos_unmask.sum() * 3), min=1) # shape = [1, 3]
+                    data.pos = (data.pos - centroid.repeat(1, 3)) * pos_unmask.view(-1, 1)
+            return data
+        
+        data_copy = copy.deepcopy(data)
+        rna_len = data.x.size(0)
+        if rna_len > self.rna_max_len:
+            data = cropping(data, self.rna_max_len, self.spatial_crop_ratio)
+            data_copy = cropping(data_copy, self.rna_max_len, self.spatial_crop_ratio)
+        
+        if self.new_aa:
+            ## center the data
+            pos_unmask = ~data.pos_mask # shape = [num_res, res_len]
+            centroid = data.pos.reshape(-1, 3).sum(dim=0, keepdim=True) / torch.clamp(pos_unmask.sum(), min=1) # shape = [1, 3]
+            data.pos = (data.pos - centroid.unsqueeze(0)) * pos_unmask.unsqueeze(-1) / self.pos_std
+
+            pop_unmask_copy = ~data_copy.pos_mask
+            centroid_copy = data_copy.pos.reshape(-1, 3).sum(dim=0, keepdim=True) / torch.clamp(pop_unmask_copy.sum(), min=1)
+            data_copy.pos = (data_copy.pos - centroid_copy.unsqueeze(0)) * pop_unmask_copy.unsqueeze(-1) / self.pos_std
+        
+        else:
+            if self.new_res:
+                ## center the data
+                pos_unmask = ~data.pos_mask # shape = [num_res, 3]
+                centroid = data.pos.reshape(-1, 3).sum(dim=0, keepdim=True) / torch.clamp(pos_unmask.sum(), min=1) # shape = [1, 3]
+                data.pos = (data.pos - centroid.view(1, 1, 3)) * pos_unmask.view(-1, 3, 1) / self.pos_std
+            else:
+                ## center the data
+                pos_unmask = ~data.pos_mask # shape = [num_res]
+                centroid = data.pos.reshape(-1, 3).sum(dim=0, keepdim=True) / torch.clamp((pos_unmask.sum() * 3), min=1) # shape = [1, 3]
+                data.pos = (data.pos - centroid.repeat(1, 3)) * pos_unmask.view(-1, 1) / self.pos_std
+        
+        assert not torch.isnan(data.pos).any()
+        
+        data = aug(data)
+        data_copy = aug(data_copy)
+
+        return data, data_copy
+
+class RNACollater:
+    def __call__(self, batch):
+        # batch: List of tuples (g1, g2)
+        g1_list, g2_list = zip(*batch)
+
+        g1_batch = Batch.from_data_list(g1_list)
+        g2_batch = Batch.from_data_list(g2_list)
+
+        # batch['max_seqlen'] = int((batch['ptr'][1:] - batch['ptr'][:-1]).max())
+        g1_batch['edge_attr'] = g1_batch['edge_attr'].to(torch.float)
+        g1_batch['edge_index'] = g1_batch['edge_index'].to(torch.long)
+        g1_batch['class'] = torch.LongTensor(g1_batch['class'])
+        # if hasattr(batch, 'res_id'):
+        g1_batch['seq_pos'] = g1_batch.pop('res_id').to(torch.long)
+        
+        if hasattr(g1_batch, 'bead_pos'):
+            g1_batch['gt_pos'] = g1_batch.pop('bead_pos')
+        elif hasattr(g1_batch, 'pos'):
+            g1_batch['gt_pos'] = g1_batch.pop('pos')
+
+        # if self.new_res:
+        #     batch['gt_pos'] = batch['gt_pos'].view(-1, 9)
+        #     batch['pos_mask'] = batch.pos_mask.any(dim=-1)
+
+        pos_unmask = ~g1_batch['pos_mask'].unsqueeze(-1)
+        g1_batch['gt_pos'] = g1_batch['gt_pos'] * pos_unmask
+
+        g2_batch['edge_attr'] = g2_batch['edge_attr'].to(torch.float)
+        g2_batch['edge_index'] = g2_batch['edge_index'].to(torch.long)
+        g2_batch['class'] = torch.LongTensor(g2_batch['class'])
+        g2_batch['seq_pos'] = g2_batch.pop('res_id').to(torch.long)
+        if hasattr(g2_batch, 'bead_pos'):
+            g2_batch['gt_pos'] = g2_batch.pop('bead_pos')
+        elif hasattr(g2_batch, 'pos'):
+            g2_batch['gt_pos'] = g2_batch.pop('pos')
+        pos_unmask = ~g2_batch['pos_mask'].unsqueeze(-1)
+        g2_batch['gt_pos'] = g2_batch['gt_pos'] * pos_unmask
+
+        return g1_batch, g2_batch
+
+class RNACollater_v2:
+    def add_noise(self, batch):
+        noise = torch.randn(batch.gt_pos.shape) # shape = [\sum N_i, 9]
+        batch['noise'] = noise
+        
+        pos_t = batch.gt_pos + 0.1 * noise # shape = [\sum N_i, 9]
+        
+        batch['pos'] = pos_t
+        return batch
+
+    def __call__(self, batch):
+        # batch: List of tuples (g1, g2)
+        g1_list, g2_list = zip(*batch)
+
+        g1_batch = Batch.from_data_list(g1_list)
+        g2_batch = Batch.from_data_list(g2_list)
+
+        # batch['max_seqlen'] = int((batch['ptr'][1:] - batch['ptr'][:-1]).max())
+        g1_batch['edge_attr'] = g1_batch['edge_attr'].to(torch.float)
+        g1_batch['edge_index'] = g1_batch['edge_index'].to(torch.long)
+        g1_batch['class'] = torch.LongTensor(g1_batch['class'])
+        # if hasattr(batch, 'res_id'):
+        g1_batch['seq_pos'] = g1_batch.pop('res_id').to(torch.long)
+        
+        if hasattr(g1_batch, 'bead_pos'):
+            g1_batch['gt_pos'] = g1_batch.pop('bead_pos')
+        elif hasattr(g1_batch, 'pos'):
+            g1_batch['gt_pos'] = g1_batch.pop('pos')
+
+        g1_batch = self.add_noise(g1_batch)
+
+        # if self.new_res:
+        #     batch['gt_pos'] = batch['gt_pos'].view(-1, 9)
+        #     batch['pos_mask'] = batch.pos_mask.any(dim=-1)
+
+        pos_unmask = ~g1_batch['pos_mask'].unsqueeze(-1)
+        g1_batch['gt_pos'] = g1_batch['gt_pos'] * pos_unmask
+        g1_batch['pos'] = g1_batch['pos'] * pos_unmask
+        g1_batch['noise'] = g1_batch['noise'] * pos_unmask
+
+        g2_batch['edge_attr'] = g2_batch['edge_attr'].to(torch.float)
+        g2_batch['edge_index'] = g2_batch['edge_index'].to(torch.long)
+        g2_batch['class'] = torch.LongTensor(g2_batch['class'])
+        g2_batch['seq_pos'] = g2_batch.pop('res_id').to(torch.long)
+        if hasattr(g2_batch, 'bead_pos'):
+            g2_batch['gt_pos'] = g2_batch.pop('bead_pos')
+        elif hasattr(g2_batch, 'pos'):
+            g2_batch['gt_pos'] = g2_batch.pop('pos')
+
+        g2_batch = self.add_noise(g2_batch)
+
+        pos_unmask = ~g2_batch['pos_mask'].unsqueeze(-1)
+        g2_batch['gt_pos'] = g2_batch['gt_pos'] * pos_unmask
+        g2_batch['pos'] = g2_batch['pos'] * pos_unmask
+        g2_batch['noise'] = g2_batch['noise'] * pos_unmask
+
+        return g1_batch, g2_batch
+    
+if __name__ == '__main__':
+    dataset = RNADataset('/home/hui007/rna/rna_repr/zhiyuan/train_data_final.npz')
+    for i in range(10):
+        print(dataset[i])
+    loader = DataLoader(
+            dataset,
+            batch_size=4,
+            shuffle=False,
+            num_workers=0,
+            drop_last=True,
+            collate_fn=RNACollater()
+        )
+    for g1, g2 in loader:
+        print(g1)
+        print(g2)
+        break
+    

scale_protenix_emb.py

@@ -0,0 +1,32 @@
+import os
+import torch
+import numpy as np
+
+src_dir ='/home/hui007/Protenix/protenix_1d_embeddings'
+dst_dir = '/home/hui007/rna/first_stage/scaled_tiny_protenix_emb_1d'
+
+embedding_list = []
+file_list = []
+
+# 1. 读取所有embedding
+for fname in os.listdir(src_dir):
+    if fname.endswith('.pt'):
+        fpath = os.path.join(src_dir, fname)
+        emb = torch.load(fpath)
+        # 假设emb为[N, 384]的Tensor
+        embedding_list.append(emb)
+        file_list.append(fname)
+
+# 拼接所有 embedding（样本维度拼接）
+concat_emb = torch.cat(embedding_list, dim=0)  # shape: [total_N, 384]
+
+# 计算全局 mean 和 std（全体元素参与）
+global_mean = concat_emb.mean()
+global_std = concat_emb.std(unbiased=False)  # unbiased=False 更稳定
+
+# 避免除以 0
+global_std = global_std.clamp(min=1e-6)
+
+for emb, fname in zip(embedding_list, file_list):
+    standardized_emb = (emb - global_mean) / global_std
+    torch.save(standardized_emb, os.path.join(dst_dir, fname))