File size: 12,525 Bytes
44a25da |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 |
import os
import torch
import numpy as np
import pandas as pd
import h5py
import re
from omegaconf import OmegaConf
import h5py
import lightning as L
from pera.nn import BidirectionalModel, sample_components_from_bidirectional_transformer, sample_perturbations, sample_embedding_perturbations
from esm.tokenization.sequence_tokenizer import EsmSequenceTokenizer
from Bio.Seq import Seq
from Bio.PDB import PDBList, PDBParser, is_aa
device = torch.device("cuda:0")
# Optional: map 3-letter residue names to 1-letter codes
three_to_one = {
'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D',
'CYS': 'C', 'GLN': 'Q', 'GLU': 'E', 'GLY': 'G',
'HIS': 'H', 'ILE': 'I', 'LEU': 'L', 'LYS': 'K',
'MET': 'M', 'PHE': 'F', 'PRO': 'P', 'SER': 'S',
'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V',
'SEC': 'U', 'PYL': 'O', 'ASX': 'B', 'GLX': 'Z',
'XLE': 'J', 'UNK': 'X'
}
def get_backbone_coords_from_local_pdb(pdb_path, chain_id='A', sequence_length=None, target="data", device=device):
"""
Load backbone coordinates and residue types from a local PDB file.
Returns:
coords_tensor: torch.Tensor of shape (1, N, 3, 3)
residue_types: List of one-letter residue codes
"""
parser = PDBParser(QUIET=True)
structure = parser.get_structure("local_structure", pdb_path)
coords = []
residue_types = []
model = structure[0]
if chain_id not in model:
raise ValueError(f"Chain {chain_id} not found in {pdb_path}")
chain = model[chain_id]
for residue in chain:
if sequence_length is not None and len(coords) >= sequence_length:
break
if not is_aa(residue):
continue
try:
n = residue['N'].get_coord()
ca = residue['CA'].get_coord()
c = residue['C'].get_coord()
coords.append([n, ca, c])
resname = residue.get_resname().upper()
residue_types.append(three_to_one.get(resname, 'X')) # default to 'X' if unknown
except KeyError:
continue
if not coords:
raise ValueError("No residues with complete backbone atoms found.")
# Add infinity-padding before and after
pad = [[float('inf')]*3, [float('inf')]*3, [float('inf')]*3]
coords.insert(0, pad)
coords.append(pad)
if target == "ParD2":
coords = [pad, pad] + coords + [pad, pad]
elif target == "ParD3":
coords = [pad]*2 + coords + [pad]*6
elif target == "TrpB4":
coords = [pad] + coords
coords_tensor = torch.tensor(coords, device=device).unsqueeze(0) # (1, N, 3, 3)
return coords_tensor, residue_types
sequence_tokenizer = EsmSequenceTokenizer()
import argparse
# set up parser
parser = parser = argparse.ArgumentParser(description="Calculating the log-likelihood of a sequence")
parser.add_argument('--target', type=str, required=True, help='Dataset as a string')
parser.add_argument('--num_samples', type=int, required=False, default=384, help='Number of samples to process (default: 100000)')
parser.add_argument('--version_number', type=str, required=False, default=1, help='Version number as a string')
parser.add_argument('--replicate', type=int, required=False, default=1, help='Replicate number as an integer')
args = parser.parse_args()
target = args.target
num_samples = args.num_samples
replicate = args.replicate
cfg_filename = "./config.yaml"
network_filename = "/global/cfs/cdirs/m4235/sebastian/models/esm3clm/esm3_clm.pt"
save_folder_name = f"{target}/base_model_{num_samples}"
cfg = OmegaConf.load(cfg_filename)
sampling_temperature=1
OmegaConf.update(cfg, "train.lightning_model_args.sampling_temperature", sampling_temperature)
esm_model = BidirectionalModel(cfg["nn"]["model"],
cfg["nn"]["model_args"],
**cfg["train"]["lightning_model_args"]).to(device)
esm_model.load_model_from_ckpt(network_filename)
esm_model.eval()
print("")
mask_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["mask"]
bos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["bos"]
eos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["eos"]
pad_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["pad"]
os.makedirs(save_folder_name, exist_ok=True)
data = target # "GB1", "ParD2", "TEV", "TrpB3F", "TrpB3I", "TrpB4"
data_root_path = "/global/cfs/projectdirs/m4235/sebastian/data"
sequence_tokenizer = EsmSequenceTokenizer()
if data.startswith("TrpB"):
df = pd.read_csv(f"{data_root_path}/TrpB/scale2max/{data}.csv")
with open(f"{data_root_path}/TrpB/TrpB.fasta", "r") as file:
parent_sequence_decoded = file.readlines()[1].strip()
elif data == "DHFR":
df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
nucleotide_seq = file.readlines()[1].strip()
nucleotide_seq = Seq(nucleotide_seq)
parent_sequence_decoded = str(nucleotide_seq.translate()) # Translate to amino acid sequence
else:
df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
parent_sequence_decoded = file.readlines()[1].strip()
if data != "GB1":
muts = df["muts"].iloc[0]
else:
muts = df["muts"].iloc[100000]
numbers = re.findall(r'\d+', muts)
mask_indices = list(map(int, numbers))
num_masks_per_sequence = num_samples // 4
num_to_generate_per_mask = 4
parent_sequence = torch.tensor(sequence_tokenizer.encode(parent_sequence_decoded,
add_special_tokens=True), device=device).unsqueeze(0).long()
sequence_length = parent_sequence.shape[1]
all_masked_sequences = []
all_unmasked_sequences_decoded = []
all_unmasked_sequences = []
all_logps = []
while len(all_unmasked_sequences_decoded) < num_samples:
print(len(all_unmasked_sequences_decoded))
masked_sequences = parent_sequence.clone().repeat(num_to_generate_per_mask, 1)
masked_sequences[:, mask_indices] = mask_token_sequence
sequence_id = torch.ones((num_to_generate_per_mask, sequence_length), device=device).long() * 1
structure_tokens = torch.ones((num_to_generate_per_mask, sequence_length), device=device).long() * 4096
structure_tokens[:, 0] = 4098
structure_tokens[:, -1] = 4097
coords, residue_types = get_backbone_coords_from_local_pdb(f"{data_root_path}/{data}/{data}.pdb", chain_id='A', sequence_length=sequence_length-2, target=data) if not data.startswith("TrpB") else get_backbone_coords_from_local_pdb(f"{data_root_path}/TrpB/TrpB.pdb", chain_id='A', sequence_length=sequence_length-2, target=data)
# parent sequence sanity check
coords_trimmed = coords[:, 1:-1] # shape: (1, N-2, 3, 3)
# Step 2: Determine mask of non-padding residues (i.e., not all coords are inf)
valid_mask = ~(torch.isinf(coords_trimmed).view(-1, 9).any(dim=1)) # shape: (N-2,)
residues_to_compare = [r for r, valid in zip(list(parent_sequence_decoded), valid_mask) if valid]
if residue_types != residues_to_compare:
print("Residue mismatch detected!")
for i, (ref, pdb) in enumerate(zip(residues_to_compare, residue_types)):
if ref != pdb:
print(f"Position {i}: expected {ref}, got {pdb}")
else:
print("Residues match.")
print(coords.shape)
assert coords.shape[1] == sequence_length, f"Coords length {coords.shape[1]} does not match sequence length {sequence_length}"
# Repeat the coords tensor to match the batch size (num_to_generate_per_mask)
coords = coords.repeat(num_to_generate_per_mask, 1, 1, 1) # Shape becomes (num_to_generate_per_mask, sequence_length, 3, 3)
average_plddt = torch.ones((num_to_generate_per_mask), device=device)
per_res_plddt = torch.zeros((num_to_generate_per_mask, sequence_length), device=device)
ss8_tokens = torch.zeros((num_to_generate_per_mask, sequence_length), device=device).long()
sasa_tokens = torch.zeros((num_to_generate_per_mask, sequence_length), device=device).long()
function_tokens = torch.zeros((num_to_generate_per_mask, sequence_length, 8), device=device).long()
residue_annotation_tokens = torch.zeros((num_to_generate_per_mask, sequence_length, 16), device=device).long()
with torch.no_grad():
unmasked_sequences = sample_components_from_bidirectional_transformer(transformer_model=esm_model,
masked_sequence_tokens=masked_sequences,
structure_tokens=structure_tokens,
average_plddt=average_plddt,
per_res_plddt=per_res_plddt,
ss8_tokens=ss8_tokens,
sasa_tokens=sasa_tokens,
function_tokens=function_tokens,
residue_annotation_tokens=residue_annotation_tokens,
bb_coords=coords,
sequence_id=sequence_id,
mask_token_sequence=mask_token_sequence,
bos_token_sequence=bos_token_sequence,
eos_token_sequence=eos_token_sequence,
pad_token_sequence=pad_token_sequence,
inference_batch_size=1)
masked_indices = (masked_sequences == mask_token_sequence).float()
logits = esm_model.nn(sequence_tokens=masked_sequences,
structure_tokens=structure_tokens,
average_plddt=average_plddt,
per_res_plddt=per_res_plddt,
ss8_tokens=ss8_tokens,
sasa_tokens=sasa_tokens,
function_tokens=function_tokens,
residue_annotation_tokens=residue_annotation_tokens,
sequence_id=sequence_id,
bb_coords=coords)["sequence_logits"].detach()
logps = torch.nn.functional.log_softmax(logits/sampling_temperature, dim=-1)
logps = torch.gather(logps, dim=-1, index=unmasked_sequences.unsqueeze(-1)).squeeze(-1)
logps = (logps * masked_indices).sum(-1).detach()
decoded_seqs = [sequence.replace(" ", "") for sequence in sequence_tokenizer.batch_decode(unmasked_sequences[:, 1:-1])]
for seq, logp, masked_seq, unmasked_seq in zip(decoded_seqs, logps, masked_sequences, unmasked_sequences):
if seq in all_unmasked_sequences_decoded:
continue
else:
all_unmasked_sequences_decoded.append(seq)
all_logps.append(logp)
all_masked_sequences.append(masked_seq)
all_unmasked_sequences.append(unmasked_seq)
all_unmasked_sequences_decoded = all_unmasked_sequences_decoded[:num_samples]
all_masked_sequences = all_masked_sequences[:num_samples]
all_unmasked_sequences = all_unmasked_sequences[:num_samples]
all_logps = all_logps[:num_samples]
all_masked_sequences = torch.stack(all_masked_sequences, dim=0)
all_unmasked_sequences = torch.stack(all_unmasked_sequences, dim=0)
all_logps = torch.stack(all_logps, dim=0)
to_save = {"parent_sequence": parent_sequence,
"all_masked_sequences": all_masked_sequences,
"all_unmasked_sequences": all_unmasked_sequences,
"all_unmasked_sequences_decoded": all_unmasked_sequences_decoded,
"all_logps": all_logps}
torch.save(to_save, f"{save_folder_name}/trpb_{replicate}.pt") |