PSP - 蛋白质与蛋白质的扩散对接 DiffDock-PP 算法

本文主要是介绍PSP - 蛋白质与蛋白质的扩散对接 DiffDock-PP 算法，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

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DiffDock-PP

DiffDock-PP is a new approach to rigid-body protein-protein docking that is based on a diffusion generative model that learns to translate and rotate unbound protein structures into their bound conformations, and a confidence model that learns to rank different poses generated by the score model and select the best one.

DiffDock-PP 是一种新的刚体蛋白质-蛋白质对接方法，基于扩散生成模型，该模型学习将未结合的蛋白质结构翻译和旋转为其结合构象，基于置信模型，该模型学习对评分模型生成的不同姿势，进行排序并选择最佳姿势。

Paper：DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models
Github：https://github.com/ketatam/DiffDock-PP

算法整体思路与 DiffDock 接近，Paper：DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Conda 环境配置：

conda create -n diffdock_pp python=3.10.8
conda activate diffdock_ppconda install pytorch=1.13.0 pytorch-cuda=11.6 -c pytorch -c nvidia

验证 PyTorch 是否安装成功：

import torch
print(torch.__version__)  # 1.13.0
print(torch.cuda.is_available())  # True

安装其他包：

pip install --no-cache-dir  torch-scatter==2.0.9 torch-sparse==0.6.15 torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.13.0+cu116.htmlpip install numpy dill tqdm pyyaml pandas biopandas scikit-learn biopython e3nn wandb tensorboard tensorboardX matplotlib

下载数据，具体数据路径 datasets/DIPS/pairs_pruned，即：

bypy downfile /psp_data/diffdock_pp/DIPS.zip DIPS.zip

具体使用位于 src/inference.sh，参考 dips_esm_inference.yaml：

 data:dataset: dipsdata_file: datasets/DIPS/data_file_100_test.csvdata_path: datasets/DIPS/pairs_pruned
...

其中，数据文件 data_file_100_test.csv，即：

path,split
eb/1ebo.pdb2_1.dill,test
dm/3dmp.pdb3_2.dill,test
kq/1kq1.pdb1_1.dill,test
b2/2b24.pdb1_8.dill,test
cf/3cf0.pdb1_6.dill,test
...

还需要下载 ESM 的 650M 模型，位于 torchhub/checkpoints，否则下载很慢，即：

esm2_t33_650M_UR50D-contact-regression.pt
esm2_t33_650M_UR50D.pt

其中数据类型是 dill 类型，PDB 转换成 dill 类型，参考 https://github.com/octavian-ganea/equidock_public#dips-data

运行推理脚本：

sh src/inference.sh

其中，在运行时，在 src/geom_utils/so3.py 中，需要预处理 npy ，耗时较长，即：

.so3_cdf_vals2.npy
.so3_exp_score_norms2.npy
.so3_omegas_array2.npy
.so3_score_norms2.npy

运行日志：

SCORE_MODEL_PATH: checkpoints/large_model_dips/fold_0/
CONFIDENCE_MODEL_PATH: checkpoints/large_model_dips/fold_0/
SAVE_PATH: ckpts/test_large_model_dips
09:50:16 Starting Inference
data loading: 100%|█| 100/100 [00:00<00:00, 561486
09:50:18 Computing ESM embeddings
Using cache found in torchhub/facebookresearch_esm_main
ESM: 100%|██████████| 4/4 [00:10<00:00,  2.70s/it]
ESM: 100%|██████████| 4/4 [00:06<00:00,  1.61s/it]
09:50:50 finished tokenizing residues with ESM
09:50:50 finished tokenizing all inputs
09:50:50 100 entries loaded
09:50:50 finished loading raw data
09:50:50 running inference
09:50:50 finished creating data splits
miniconda3/envs/diffdock_pp/lib/python3.10/site-packages/torch/jit/_check.py:181: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.warnings.warn("The TorchScript type system doesn't support "
09:50:52 loaded model with kwargs: 
checkpoint checkpoints/large_model_dips/fold_0/model_best_338669_140_31.084_30.347.pth
09:50:52 loaded checkpoint from checkpoints/large_model_dips/fold_0/model_best_338669_140_31.084_30.347.pth
09:50:53 loaded model with kwargs: 
checkpoint checkpoints/confidence_model_dips/fold_0/model_best_0_6_0.241_0.887.pth
09:50:53 loaded checkpoint from checkpoints/confidence_model_dips/fold_0/model_best_0_6_0.241_0.887.pth
09:50:53 finished loading model
args.temp_sampling: 2.4390%|                                                                                                       | 0/100 [00:00<?, ?it/s]09:53:42 Completed 0 out of 40 steps
09:53:44 Completed 1 out of 40 steps
09:53:46 Completed 2 out of 40 steps
09:53:48 Completed 3 out of 40 steps
...

预测结果，1B26 Receptor 和 Ligand，即：

NVCC Bug，参考 GitHub - Failed building wheel for torch-cluster:

      In file included from csrc/cuda/graclus_cuda.cu:3:0:diffdock_pp/lib/python3.10/site-packages/torch/include/ATen/cuda/CUDAContext.h:10:10: fatal error: cusolverDn.h: No such file or directory#include <cusolverDn.h>^~~~~~~~~~~~~~compilation terminated.error: command 'diffdock_pp/bin/nvcc' failed with exit code 1[end of output]note: This error originates from a subprocess, and is likely not a problem with pip.ERROR: Failed building wheel for torch-clusterRunning setup.py clean for torch-cluster
Failed to build torch-cluster
ERROR: Could not build wheels for torch-cluster, which is required to install pyproject.toml-based projects