PepGLAD
checkpoints
Conda環境可以使用env.yaml配置構建：
conda env create -f env.yaml使用CUDA版本11.7和Pytorch版本1.13.1對代碼進行了測試。
在運行代碼之前，不要忘記激活環境：
conda activate PepGLADPyrosetta用於計算生成肽的界面能量。如果您對此感興趣，請在此處按照此處進行安裝。
這些數據集僅用於基準模型。如果您只想使用訓練有素的權重來推斷案例，則無需下載這些數據集。
最初在本文中介紹的數據集在此URL上上傳到Zenodo。您可以按以下方式下載它們：
mkdir datasets # all datasets will be put into this directory
wget https://zenodo.org/records/13373108/files/train_valid.tar.gz ? download=1 -O ./datasets/train_valid.tar.gz # training/validation
wget https://zenodo.org/records/13373108/files/LNR.tar.gz ? download=1 -O ./datasets/LNR.tar.gz # test set
wget https://zenodo.org/records/13373108/files/ProtFrag.tar.gz ? download=1 -O ./datasets/ProtFrag.tar.gz # augmentation datasettar zxvf ./datasets/train_valid.tar.gz -C ./datasets
tar zxvf ./datasets/LNR.tar.gz -C ./datasets
tar zxvf ./datasets/ProtFrag.tar.gz -C ./datasetspython -m scripts.data_process.process --index ./datasets/train_valid/all.txt --out_dir ./datasets/train_valid/processed # train/validation set
python -m scripts.data_process.process --index ./datasets/LNR/test.txt --out_dir ./datasets/LNR/processed # test set
python -m scripts.data_process.process --index ./datasets/ProtFrag/all.txt --out_dir ./datasets/ProtFrag/processed # augmentation dataset需要如下生成火車/驗證拆分的處理數據的索引，這將導致數據datasets/train_valid/processed/train_index.txt和datasets/train_valid/processed/valid_index.txt ：
python -m scripts.data_process.split --train_index datasets/train_valid/train.txt --valid_index datasets/train_valid/valid.txt --processed_dir datasets/train_valid/processed/wget http://huanglab.phys.hust.edu.cn/pepbdb/db/download/pepbdb-20200318.tgz -O ./datasets/pepbdb.tgztar zxvf ./datasets/pepbdb.tgz -C ./datasets/pepbdbpython -m scripts.data_process.pepbdb --index ./datasets/pepbdb/peptidelist.txt --out_dir ./datasets/pepbdb/processed
python -m scripts.data_process.split --train_index ./datasets/pepbdb/train.txt --valid_index ./datasets/pepbdb/valid.txt --test_index ./datasets/pepbdb/test.txt --processed_dir datasets/pepbdb/processed/
mv ./datasets/pepbdb/processed/pdbs ./dataset/pepbdb # re-locate./checkpoint/codesign.ckpt codesign.ckpt./checkpoints/fixseq.ckpt fixseq.ckpt兩者都可以在發布頁面上下載。這些檢查站在Pepbench上進行了培訓。
以./assets/1ssc_A_B.pdb為例，其中鏈A是目標蛋白：
# obtain the binding site, which might also be manually crafted or from other ligands (e.g. small molecule, antibodies)
python -m api.detect_pocket --pdb assets/1ssc_A_B.pdb --target_chains A --ligand_chains B --out assets/1ssc_A_pocket.json
# sequence-structure codesign with length in [8, 15)
CUDA_VISIBLE_DEVICES=0 python -m api.run
--mode codesign
--pdb assets/1ssc_A_B.pdb
--pocket assets/1ssc_A_pocket.json
--out_dir ./output/codesign
--length_min 8
--length_max 15
--n_samples 10然後，將在文件夾中輸出10代./output/codesign 。
以./assets/1ssc_A_B.pdb為例，其中鏈A是目標蛋白：
# obtain the binding site, which might also be manually crafted or from other ligands (e.g. small molecule, antibodies)
python -m api.detect_pocket --pdb assets/1ssc_A_B.pdb --target_chains A --ligand_chains B --out assets/1ssc_A_pocket.json
# generate binding conformation
CUDA_VISIBLE_DEVICES=0 python -m api.run
--mode struct_pred
--pdb assets/1ssc_A_B.pdb
--pocket assets/1ssc_A_pocket.json
--out_dir ./output/struct_pred
--peptide_seq PYVPVHFDASV
--n_samples 10然後，將在./output/struct_pred夾中輸出10個構象。
每個任務都需要以下步驟，我們已將其集成到腳本中./scripts/run_exp_pipe.sh ：
另一方面，如果要評估現有檢查點，請按照以下說明（例如構象生成）：
# generate results on the test set and save to ./results/fixseq
python generate.py --config configs/pepbench/test_fixseq.yaml --ckpt checkpoints/fixseq.ckpt --gpu 0 --save_dir ./results/fixseq
# calculate metrics
python cal_metrics.py --results ./results/fixseq/results.jsonlpepbench上的代碼實驗：
GPU=0 bash scripts/run_exp_pipe.sh pepbench_codesign configs/pepbench/autoencoder/train_codesign.yaml configs/pepbench/ldm/train_codesign.yaml configs/pepbench/ldm/setup_latent_guidance.yaml configs/pepbench/test_codesign.yamlpepbench上的構象產生實驗：
GPU=0 bash scripts/run_exp_pipe.sh pepbench_fixseq configs/pepbench/autoencoder/train_fixseq.yaml configs/pepbench/ldm/train_fixseq.yaml configs/pepbench/ldm/setup_latent_guidance.yaml configs/pepbench/test_fixseq.yaml感謝您對我們的工作的興趣！
請隨時詢問有關算法，代碼以及運行它們遇到的問題的任何問題，以便我們可以使其更清晰，更好。您可以在GitHub回購中創建問題，也可以通過[email protected]與我們聯繫。
@article { kong2024full ,
title = { Full-atom peptide design with geometric latent diffusion } ,
author = { Kong, Xiangzhe and Huang, Wenbing and Liu, Yang } ,
journal = { arXiv preprint arXiv:2402.13555 } ,
year = { 2024 }
}
