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import torch
import re
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import h5py
import argparse
from omegaconf import OmegaConf
from esm.tokenization.sequence_tokenizer import EsmSequenceTokenizer
from Bio.Seq import Seq
device = torch.device("cuda:0")
num_replicates = 10
campaign_number = 2 # change this according to the campaign we are interested in
dataset_size = 96 # change this according to the dataset size we are interested in
sequence_tokenizer = EsmSequenceTokenizer()
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')
args = parser.parse_args()
data = args.target
data_root_path = "/scratch/groups/rotskoff/sebastian/era/protein_era/data"
print(data)
for i in range(num_replicates):
cfg_filename = f"./config.yaml"
cfg = OmegaConf.load(cfg_filename)
sampling_temperature=1
OmegaConf.update(cfg, "train.lightning_model_args.sampling_temperature", sampling_temperature)
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"]
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))
# mask_indices = [i-1 for i in mask_indices] #convert to 0-based indexing
fitness_scores = []
# Load from base_model_{dataset_size}
trpb_base = torch.load(f"./{data}/base_model_{dataset_size}/trpb_post_rd_{campaign_number-1}_{i}.pt")
all_unmasked_sequences_decoded_base = trpb_base["all_unmasked_sequences_decoded"]
all_unmasked_sequences_base = trpb_base["all_unmasked_sequences"]
all_masked_sequences_base = trpb_base["all_masked_sequences"]
all_unmasked_sequences_base = all_unmasked_sequences_base.reshape(-1, all_unmasked_sequences_base.shape[-1])
all_logps_base = trpb_base["all_logps"]
for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_base, all_unmasked_sequences_base):
index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
try:
index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
muts = ''.join(mutations)
except:
mutations = [index_residue_0, index_residue_1, index_residue_2]
muts = ''.join(mutations)
df_filtered = df[df["AAs"] == muts]
if len(df_filtered) == 0:
if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
print(f"Invalid sequence {muts}")
fitness_score = -2
else:
print(f"Invalid sequence {muts}")
fitness_score = -2
else:
fitness_score = df_filtered["fitness"].values[0]
fitness_scores.append(fitness_score)
# Load from aligned_0_{dataset_size}
trpb_aligned = torch.load(f"./{data}/aligned_{campaign_number-2}_{dataset_size}_{i}/trpb_post_rd_{campaign_number-1}_{i}.pt")
all_unmasked_sequences_decoded_aligned_0 = trpb_aligned["all_unmasked_sequences_decoded"]
all_unmasked_sequences_aligned_0 = trpb_aligned["all_unmasked_sequences"]
all_masked_sequences_aligned_0 = trpb_aligned["all_masked_sequences"]
all_unmasked_sequences_aligned_0 = all_unmasked_sequences_aligned_0.reshape(-1, all_unmasked_sequences_aligned_0.shape[-1])
all_logps_aligned_0 = trpb_aligned["all_logps"]
for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_aligned_0, all_unmasked_sequences_aligned_0):
index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
try:
index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
muts = ''.join(mutations)
except:
mutations = [index_residue_0, index_residue_1, index_residue_2]
muts = ''.join(mutations)
df_filtered = df[df["AAs"] == muts]
if len(df_filtered) == 0:
if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
print(f"Invalid sequence {muts}")
fitness_score = -2
else:
print(f"Invalid sequence {muts}")
fitness_score = -2
else:
fitness_score = df_filtered["fitness"].values[0]
fitness_scores.append(fitness_score)
# Load from aligned_1_{dataset_size}
trpb_aligned_1 = torch.load(f"./{data}/aligned_{campaign_number-1}_{dataset_size}_{i}/trpb_{i}.pt")
all_unmasked_sequences_decoded_aligned_1 = trpb_aligned_1["all_unmasked_sequences_decoded"]
all_unmasked_sequences_aligned_1 = trpb_aligned_1["all_unmasked_sequences"]
all_masked_sequences_aligned_1 = trpb_aligned_1["all_masked_sequences"]
all_unmasked_sequences_aligned_1 = all_unmasked_sequences_aligned_1.reshape(-1, all_unmasked_sequences_aligned_1.shape[-1])
all_logps_aligned_1 = trpb_aligned_1["all_logps"]
for unmasked_sequence_decoded, unmasked_sequence in zip(all_unmasked_sequences_decoded_aligned_1, all_unmasked_sequences_aligned_1):
index_residue_0 = unmasked_sequence_decoded[mask_indices[0]-1]
index_residue_1 = unmasked_sequence_decoded[mask_indices[1]-1]
index_residue_2 = unmasked_sequence_decoded[mask_indices[2]-1]
try:
index_residue_3 = unmasked_sequence_decoded[mask_indices[3]-1]
mutations = [index_residue_0, index_residue_1, index_residue_2, index_residue_3]
muts = ''.join(mutations)
except:
mutations = [index_residue_0, index_residue_1, index_residue_2]
muts = ''.join(mutations)
df_filtered = df[df["AAs"] == muts]
if len(df_filtered) == 0:
if torch.any((unmasked_sequence[1:-1] > 23) | (unmasked_sequence[1:-1] < 4)):
print(f"Invalid sequence {muts}")
fitness_score = -2
else:
print(f"Invalid sequence {muts}")
fitness_score = -2
else:
fitness_score = df_filtered["fitness"].values[0]
fitness_scores.append(fitness_score)
# Concatenate the sequences and logps from all models
all_unmasked_sequences = torch.cat((all_unmasked_sequences_base, all_unmasked_sequences_aligned_0, all_unmasked_sequences_aligned_1),dim=0)#
all_masked_sequences = torch.cat((all_masked_sequences_base, all_masked_sequences_aligned_0, all_masked_sequences_aligned_1),dim=0)#
print(all_logps_base.shape, all_logps_aligned_0.shape, all_logps_aligned_1.shape)#
all_logps = torch.cat((all_logps_base, all_logps_aligned_0, all_logps_aligned_1),dim=0)#
all_fitness_scores = fitness_scores
# Check for duplicates in all_unmasked_sequences
unique_sequences, counts = torch.unique(all_unmasked_sequences, dim=0, return_counts=True)
num_duplicates = torch.sum(counts > 1).item()
print(f"Number of duplicate sequences: {num_duplicates}")
all_fitness_scores = np.array(all_fitness_scores)
all_fitness_scores = np.where(all_fitness_scores > 0, -np.log(all_fitness_scores), 10)
sampling_temperature = 1 # hard-coding a sampling temperature of 1 for mixed-temperature alignment
sequence_length = all_unmasked_sequences.shape[1]
sequence_id = torch.ones((all_unmasked_sequences.shape[0], sequence_length), device=device).long() * 1
structure_tokens = torch.ones((1, sequence_length), device=device).long() * 4096
structure_tokens[:, 0] = 4098
structure_tokens[:, -1] = 4097
coords = torch.inf * torch.ones((1, sequence_length, 3, 3), device=device)
average_plddt = torch.ones((1), device=device)
per_res_plddt = torch.zeros((1, sequence_length), device=device)
ss8_tokens = torch.zeros((1, sequence_length), device=device).long()
sasa_tokens = torch.zeros((1, sequence_length), device=device).long()
function_tokens = torch.zeros((1, sequence_length, 8), device=device).long()
residue_annotation_tokens = torch.zeros((1, sequence_length, 16), device=device).long()
with h5py.File(f"./{data}/alignment_dataset_{campaign_number}_{dataset_size}_from_ESM3_{i}.hdf5", "w") as f:
masked_sequence_tokens = f.create_dataset("masked_sequence_tokens", data=all_masked_sequences.cpu().numpy())
unmasked_sequence_tokens = f.create_dataset("unmasked_sequence_tokens", data=all_unmasked_sequences.cpu().numpy())
sequence_id = f.create_dataset("sequence_id", data=sequence_id.cpu().numpy())
structure_tokens = f.create_dataset("structural_tokens", data=structure_tokens.cpu().numpy())
coords = f.create_dataset("bb_coords", data=coords.cpu().numpy())
average_plddt = f.create_dataset("average_plddt", data=average_plddt.cpu().numpy())
per_res_plddt = f.create_dataset("per_res_plddt", data=per_res_plddt.cpu().numpy())
ss8_tokens = f.create_dataset("ss8_tokens", data=ss8_tokens.cpu().numpy())
sasa_tokens = f.create_dataset("sasa_tokens", data=sasa_tokens.cpu().numpy())
function_tokens = f.create_dataset("function_tokens", data=function_tokens.cpu().numpy())
residue_annotation_tokens = f.create_dataset("residue_annotation_tokens", data=residue_annotation_tokens.cpu().numpy())
ref_logps = f.create_dataset("ref_logps", data=all_logps.cpu().numpy())
energies = f.create_dataset("energies", data=all_fitness_scores)
f.attrs["num_prompts"] = 1
f.attrs["num_examples_per_prompt"] = masked_sequence_tokens.shape[0]
f.attrs["fixed_bb_coords"] = True
f.attrs["fixed_average_plddt"] = True
f.attrs["fixed_per_res_plddt"] = True
f.attrs["fixed_ss8_tokens"] = True
f.attrs["fixed_sasa_tokens"] = True
f.attrs["fixed_function_tokens"] = True
f.attrs["fixed_residue_annotation_tokens"] = True
f.attrs["fixed_structural_tokens"] = True
f.attrs["sampling_temperature"] = sampling_temperature
# with h5py.File(f"./{data}/alignment_dataset_{campaign_number}_{dataset_size}_from_ESM3.hdf5", "r") as f:
# for key in f.keys():
# print(key, f[key].shape) |