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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('--alignment_round', type=int, required=False, default=1, help='Alignment round as an integer')
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
alignment_round = args.alignment_round
version_number = args.version_number
num_samples = args.num_samples
replicate = args.replicate
cfg_filename = f"{target}/lightning_logs/{version_number}/config.yaml"
network_filename = f"{target}/lightning_logs/{version_number}/checkpoints/best_model.ckpt"
save_folder_name = f"{target}/aligned_{alignment_round}_{num_samples}_{replicate}"
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)
past_generations =[f"{target}/base_model_{num_samples}"]
for i in range(alignment_round):
past_generations.append(f"{target}/aligned_{i}_{num_samples}_{replicate}")
previous_unmasked_sequences_decoded = []
for round in past_generations:
trpb = torch.load(f"{round}/trpb_{replicate}.pt")
previous_unmasked_sequences_decoded.extend(trpb['all_unmasked_sequences_decoded'])
assert len(previous_unmasked_sequences_decoded) == len(set(previous_unmasked_sequences_decoded)), "There are duplicate sequences in previous_unmasked_sequences_decoded"
print("All elements in previous_unmasked_sequences_decoded are unique.")
data = target # "GB1", "ParD2", "TEV", "TrpB3F", "TrpB3I", "TrpB4"
data_root_path = "/global/cfs/cdirs/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 or seq in previous_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") |