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蛋白质语言模型 ESM (Evolutionary Scale Modeling) 是一种利用深度学习技术来预测蛋白质结构和功能的方法。ESM 通过在大规模的蛋白质序列数据库上，训练一个自回归的神经网络，学习蛋白质的进化规律和序列-结构-功能的关系。ESM 可以根据给定的蛋白质序列，生成其对应的隐向量，表示其结构和功能的特征，还可以利用隐向量进行多种下游任务，如结构预测、功能注释、相互作用分析等。ESM 是一种强大而通用的蛋白质语言模型，为蛋白质科学提供了新的视角和工具。
ESM (Evolutionary Scale Modeling)，即进化尺度模型，包括 ESM-2、ESMFold、ESM-MSA-1b、ESM-1v、ESM-IF1(反向折叠)，即
ESM-2，2022.8，SOTA 通用目的蛋白质语言模型 v2 版，其中ESM-1v是 v1 版本。ESMFold，2022.11，端到端的单序列 3D 结构预测ESM-MSA-1b，2021.6，MSA Transformer 语言模型ESM-IF1，2022.4，反向折叠模型
具体参考：ESM GitHub
1. 配置 Docker 环境
配置 TORCH_HOME 与 BOS 环境，即:
vim ~/.bashrcexport TORCH_HOME=[your folder]/torch_home/
alias bos='bcecmd/bcecmd --conf-path bcecmd/bceconf/ bos'
建议配置 TORCH_HOME，固定 PyTorch 模型缓存地址，即
torch_home/hub/checkpoints中。
在 Docker Image 中，导入 ESM 环境：
conda create -n esmfold --clone miniconda3/envs/esmfold
需要安装的 Torch 相关包：
pip install -q torch-scatter -f https://data.pyg.org/whl/torch-1.12.1+cu113.html
pip install -q torch-sparse -f https://data.pyg.org/whl/torch-1.12.1+cu113.html
pip install -q torch-cluster -f https://data.pyg.org/whl/torch-1.12.1+cu113.html
pip install -q torch-spline-conv -f https://data.pyg.org/whl/torch-1.12.1+cu113.html
pip install -q torch-geometric
导出 Docker 环境：
# 提交 Tag
docker ps -a
docker commit [container id] esmfold:v1.0# 准备远程 Tag
docker tag esmfold:v1.0 [your ip]/esmfold:v1.0# 推送至远程
docker push [your ip]/esmfold:v1.0
# 从远程拉取
# docker pull [your ip]/esmfold:v1.0
2. 批量推理 ESM2 模型
配置 ESM 推理脚本：
set -xe
PROJECT_DIR="$(cd "$(dirname $0)" && pwd)/.."source activate esmfold
export PATH="/usr/local/cuda-11.6/bin:$PATH"
export LD_LIBRARY_PATH="/usr/local/cuda-11.6/lib64:$LD_LIBRARY_PATH"
export TORCH_HOME=[your folder]/torch_home/echo "${PROJECT_DIR}"python "${PROJECT_DIR}/scripts/extract.py" esm2_t36_3B_UR50D \"${PROJECT_DIR}/mydata/all-1.fasta" \[your folder]/esm2_3B_feat/ \--toks_per_batch 1536 \--repr_layers -1 \--include per_tok contacts \--truncation_seq_length 1536 \--num_workers 8
测试 A100 显卡 80G，最大支持 1536 序列长度。
优化 scripts/extract.py 脚本，输出结果是序列 MD5 编码的特征，避免序列过长或名字重复：
- 增加
num_workers，提升推理速度。 - 替换
label为蛋白质序列。 - 增加断点处理，避免重复搜索
即
# ...
data_loader = DataLoader(dataset, collate_fn=alphabet.get_batch_converter(args.truncation_seq_length),batch_sampler=batches, num_workers=args.num_workers,
)
# ...
# result = {"label": label}
result = {"label": strs[i]} # label 修改成序列
# ...
for i, label in enumerate(labels):args.output_file = args.output_dir / f"{label}.pt"if os.path.isfile(args.output_file):warnings.warn(f"The feat has processed. {args.output_file}")continue
# ...
注意不能使用
num_workers否则程序无法运行。
日志：
python workspace/esm-by-chenlong/run/../scripts/extract.py esm2_t36_3B_UR50D workspace/esm-by-chenlong/mydata/all-1.fasta pdb_dataset/esm2_6b_feat/ --toks_per_batch 1536 --repr_layers -1 --include per_tok contacts --truncation_seq_length 1536 --num_workers 32
Transferred model to GPU
Read /nfs_beijing_ai/chenlong/workspace/esm-by-chenlong/run/../mydata/all-1.fasta with 27115 sequences
Processing 1 of 6668 batches (66 sequences)
Processing 2 of 6668 batches (61 sequences)
Processing 3 of 6668 batches (56 sequences)
Processing 4 of 6668 batches (52 sequences)
Processing 5 of 6668 batches (51 sequences)
注意序列尺寸 2048 导致显存溢出。
3. 准备 ESM2 输入 FASTA 数据
将 FASTA 文件夹中的全部 FASTA 文件组成1个文件，并且序列描述，转换成 Hash 编码，避免相同序列的特征重复生成特征，即：
seq_encoder：Hash 编码函数，同时也用于查找。load_feat：读取 feature 特征，支持显示数据和绘制图像。merge_fasta_folder：合并 FASTA 文件夹。
即：
#!/usr/bin/env python
# -- coding: utf-8 --
"""
Copyright (c) 2022. All rights reserved.
Created by C. L. Wang on 2023/9/13
"""
import argparse
import os
import sys
import warnings
from pathlib import Pathfrom tqdm import tqdmp = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if p not in sys.path:sys.path.append(p)from myutils.project_utils import traverse_dir_files_for_large, read_file, write_list_to_fileclass Esm2FastaGenerator(object):"""ESM2 工具类"""def __init__(self):pass@staticmethoddef seq_encoder(sequence):"""将 seq 使用 hash 编码，避免重复生成"""import hashlibreturn hashlib.md5(sequence.encode(encoding="utf-8")).hexdigest()@staticmethoddef load_feat(path, is_print=False):"""加载 ESM 特征文件，以及打印特征"""import torchfrom torch import Tensorrep = torch.load(path)if is_print:print(f"[Info] rep: {rep.keys()}")for key in rep.keys():val = rep[key]if isinstance(val, str):print(f"[Info] {key}: {val}")elif isinstance(val, dict):for sub_key in val.keys():print(f"[Info] {key}: {sub_key}: {val[sub_key].shape}")elif isinstance(val, Tensor):print(f"[Info] {key}: {val.shape}")else:print(f"[Info] {key}: {val}")# 绘制接触矩阵import matplotlib.pyplot as pltcontacts_map = rep["contacts"]plt.matshow(contacts_map)plt.title("contacts_map")save_name = "contacts_map.png"plt.savefig(save_name, bbox_inches='tight', format='png')plt.show()return rep@classmethoddef merge_fasta_folder(cls, folder_path, output_path):"""合并 fasta 文件，用于 esm 推理"""print(f"[Info] folder_path: {folder_path}")print(f"[Info] output_path: {output_path}")assert os.path.isdir(folder_path)path_list = traverse_dir_files_for_large(folder_path, ext="fasta")print(f"[Info] fasta: {len(path_list)}")seq_set = set()for path in tqdm(path_list, "[Info] fasta"):data_lines = read_file(path)n = len(data_lines)for i in range(1, n, 2):seq = data_lines[i]if seq:seq_set.add(seq)seq_list = list(seq_set)print(f"[Info] seq unique: {len(seq_list)}")# create_empty_file(output_path)seq_lines = []header_set = set()for seq in tqdm(seq_list, "[Info] seq"):header = cls.seq_encoder(seq)header_set.add(header)seq_lines.append(f">{header}")seq_lines.append(seq)assert len(seq_lines) // 2 == len(header_set)write_list_to_file(output_path, seq_lines)print(f"[Info] over! {output_path}")def main():parser = argparse.ArgumentParser()parser.add_argument("-f","--folder-path",type=Path,required=True,)parser.add_argument("-o","--output-path",type=Path,required=True)args = parser.parse_args()folder_path = str(args.folder_path)output_path = str(args.output_path)if os.path.isfile(output_path):warnings.warn(f"The output file exists, append lines to it! {output_path}")# from root_dir import DATA_DIR# folder_path = os.path.join(DATA_DIR, "fasta")# output_path = os.path.join(DATA_DIR, "all.fasta")Esm2FastaGenerator.merge_fasta_folder(folder_path, output_path)def main2():from root_dir import DATA_DIRfeat_path = os.path.join(DATA_DIR, "fffd26f4307d76eec938ac9c2c93a698.pt")Esm2FastaGenerator.load_feat(feat_path, is_print=True)if __name__ == '__main__':main()# main2()
输出的序列 ESM2 特征包括：
label序列描述representations序列表征 LxHmean_representations均值化表征 Hbos_representations起始 Token 表征 Hcontacts序列接触表征 LxL
例如 序列长度是 65，ESM2 650M 的 Embeddings 是 1280，ESM2 3B 是 2560，即：
[Info] rep: dict_keys(['label', 'representations', 'contacts'])
[Info] label: MAKDSKAPVVEIFDERDGCTSAGSTGKASDAGEKGLLVKVSMQKVGYNAIMAKSVAASYMNK
[Info] representations: 36: torch.Size([62, 2560])
[Info] contacts: torch.Size([62, 62])
其中 序列长度 235 的 ESM2 3B 特征，约是 2.6M，序列长度 65 的 ESM2 650M 特征，约是 361 KB。
4. 测试 ESM2 推理脚本
推理脚本：
#!/usr/bin/env python
# -- coding: utf-8 --
"""
Copyright (c) 2022. All rights reserved.
Created by C. L. Wang on 2023/9/11
"""
import math
import os
import sys
import timeimport torchimport esmp = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if p not in sys.path:sys.path.append(p)from myutils.project_utils import time_elapsedclass Esm2Infer(object):"""推理 ESM2 特征"""def __init__(self):print("[Info] 加载模型开始! ")s_time = time.time()self.model, self.alphabet = esm.pretrained.esm2_t33_650M_UR50D()print(f"[Info] vocab: {self.alphabet.to_dict()}")self.batch_converter = self.alphabet.get_batch_converter()self.model.eval() # disables dropout for deterministic resultsprint(f"[Info] 加载模型完成! 耗时: {time_elapsed(s_time, time.time())}")def predict(self, data_list):"""数据示例:data_list = [("protein1", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),("protein2", "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),("protein2 with mask", "KALTARQQEVFDLIRD<mask>ISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),("protein3", "K A <mask> I S Q"),]"""print(f"[Info] data_list: {len(data_list)}")batch_labels, batch_strs, batch_tokens = self.batch_converter(data_list)print(f"[Info] batch_labels: {batch_labels}")print(f"[Info] batch_tokens: {batch_tokens}")batch_lens = (batch_tokens != self.alphabet.padding_idx).sum(1)print(f"[Info] batch_lens: {batch_lens}") # 有效维数# Extract per-residue representations (on CPU)with torch.no_grad():results = self.model(batch_tokens, repr_layers=[33], return_contacts=True)token_representations = results["representations"][33]# Generate per-sequence representations via averaging# NOTE: token 0 is always a beginning-of-sequence token, so the first residue is token 1.sequence_representations = []for i, tokens_len in enumerate(batch_lens):feat = token_representations[i, 1: tokens_len - 1]# embeddings = feat.mean(0)# print(f"[Info] idx: {i}, feat: {feat.shape}, embeddings: {embeddings.shape}")# sequence_representations.append(embeddings)sequence_representations.append(feat)return sequence_representationsdef main():data_list = [("protein1", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),("protein2", "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),("protein2 with mask", "KALTARQQEVFDLIRD<mask>ISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),("protein3", "K A <mask> I S Q"),]ei = Esm2Infer()ei.predict(data_list)if __name__ == '__main__':main()
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