495 lines
17 KiB
Markdown
495 lines
17 KiB
Markdown
## 目录结构
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```shell
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project_root/
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├── input/
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│ ├── receptors/
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│ │ ├── TrpE_entry_1.pdb
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│ │ └── TrpE_entry_1.pdbqt
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│ ├── ligands/
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│ │ ├── sdf/
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│ │ │ ├── ligand_001.sdf
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│ │ │ ├── ligand_002.sdf
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│ │ │ └── ...
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│ │ └── pdbqt/
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│ │ ├── ligand_001.pdbqt
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│ │ ├── ligand_002.pdbqt
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│ │ └── ...
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│ └── configs/
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│ ├── TrpE_entry_1.box.txt
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│ └── TrpE_entry_1.box.pdb
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├── results/
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│ ├── poses/
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│ │ ├── ligand_001_out.pdbqt
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│ │ ├── ligand_002_out.pdbqt
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│ │ └── ...
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│ └── scores/
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│ ├── docking_scores.csv
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│ └── summary_report.txt
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└── scripts/
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├── batch_prepare_ligands.sh
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├── batch_docking.sh
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└── analyze_results.py
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```
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## 受体准备 pdbqt 文件
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使用 alphafold 预测 pdb 文件 cif 文件。
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修复使用 moderller 同源建模，或者 pdbfixer，MOE，maestro 等
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这里使用 maestro 的 `Protein reparation Workflow` 模块
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然后导出 pdb 文件
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使用 meeko 准备受体文件 pdbqt 文件，详细可以[参考](https://meeko.readthedocs.io/en/release-doc/rec_overview.html)
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```shell
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micromamba run -n vina mk_prepare_receptor.py -i receptor/FgBar1_cut_proteinprep.pdb --write_pdbqt receptor/FgBar1_cut_proteinprep.pdbqt
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```
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选项组合用法
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### 举例1：用默认输出名生成 pdbqt 和 vina box 配置
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mk_prepare_receptor.py -i 1abc.pdb -o 1abc_clean --write_pdbqt --write_vina_box
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得到 1abc_clean_rigid.pdbqt, 1abc_clean.vina.txt
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### 举例2：为指定残基设置模板/柔性，并生成 box 配置
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```shell
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mk_prepare_receptor.py -i system.pdb \
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--output_basename system_prep \
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-f "A:42,B:23" \
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-n "A:5,7=CYX,B:17=HID" \
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--write_pdbqt --write_vina_box
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```
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### 举例3：自动包络某配体生成 box 配置
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```
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mk_prepare_receptor.py -i prot.pdb \
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--box_enveloping ligand.pdb \
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--padding 3.0 \
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--output_basename dock_ready \
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--write_pdbqt --write_vina_box
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```
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### 受体准备介绍
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```shell
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mk_prepare_receptor.py -i xxx.pdb -o my_receptor -p -j -v
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mk_prepare_receptor.py -i FgBar1_cut_proteinprep.pdb -o FgBar1_cut_proteinprep -p
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```
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这样会生成：
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my_receptor.pdbqt（对接用的受体文件）
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my_receptor.json（结构元数据，编程用得上）
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my_receptor.vina_box.txt（对接区域参数，给 vina 用）
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| 选项 | 作用 | 输出文件例子 |
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| ---- | -------------------- | ------------------ |
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| `-p` | 输出PDBQT文件（受体） | `xxx.pdbqt` |
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| `-j` | 输出JSON文件（元数据） | `xxx.json` |
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| `-v` | 输出vina box参数 | `xxx.vina_box.txt` |
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| `-g` | 输出GPF文件（老版AutoDock用） | `xxx.gpf` |
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## 小分子 3D 构象准备
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需要给小分子一个初始化的 3d 构象存放到`ligand/sdf`
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```shell
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python sdf2to3d.py --src_dir ./2d_sdf_dir --out_dir ./3d_sdf_dir --n_jobs 8
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```
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## 小分子格式转化
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使用 meeko 将 `ligand/sdf` 转为 `ligand/pdbqt`
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```shell
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micromamba run -n vina ./scripts/batch_prepare_ligands.sh ligands/sdf ligands/pdbqt/ batch_prepare_ligands.log 128
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```
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## 小分子批量提交对接
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分割小分子文件将 ligand 目录里面的 pdbqt 文件夹拆分 n 个子文件夹(pdbqt1,pdbqt2,pdbqt3...pdbqtn)
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```shell
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micromamba run -n vina python vina_split_and_submit.py <split_number_n>
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```
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执行完成后会自动使用 dsub 命令将对接任务提交给华为多瑙调度系统
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需要注意有时候提交执行速度过快可能有批次遗漏，可以在合并时候检查
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## 对接结果合并
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在对接完成之后会在 `result` 文件夹里面创建 n 个对接结果文件夹（poses1，poses2，poses3...posesn）
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每个文件夹中都有对应的`*_out.pdbqt`文件与`*_converted.sdf`文件，调用
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```shell
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micromamba run -n vina python vina_merge_and_check.py --n_splits <split_number_n> --out_dir ./result --output_prefix poses --poses_dir ./result/poses_all
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```
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会将所有的n 个对接结果文件夹中`*_converted.sdf`文件存放到 `./result/poses_all` 目录，同时会检测是否有提交时候过快导致遗漏某个批次没有对接，需要注意查看。
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## 分析对接结果
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在`*_converted.sdf`文件中存在`20`个对接构象，取决于`scripts/batch_docking.sh` 中 `NUM_MODES` 设置多少数目，默认设置为 20。
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其中每个 sdf 构象存在下面的`<meeko>`字段 用于获取对接打分等属性用于后续筛选分子。
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```
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> <meeko> (20)
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{"is_sidechain": [false], "free_energy": -6.38, "intermolecular_energy": -15.695, "internal_energy": -2.912}
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```
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## batch 模式对接
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vina=1.2.7可以使用batch 模式进行批量对接。
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```shell
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mkdir -p results/poses
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vina --receptor input/receptors/TrpE_entry_1.pdbqt \
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--batch input/ligands/test \
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--config ./configs/TrpE_entry_1.box.txt \
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--dir results/poses \
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--exhaustiveness=32
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# 使用脚本对接
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./scripts/batch_docking.sh ./receptors/TrpE_entry_1.pdbqt ./config/TrpE_entry_1.box.txt ligands/test output test.log /share/home/lyzeng24/rdkit_script/vina/vina
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```
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## 环境安装
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```shell
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conda install -c conda-forge vina meeko rdkit joblib rich ipython parallel openpyxl pandas mordred -y
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```
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## 准备小分子pdbqt
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```shell
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# 单个配体准备
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mk_prepare_ligand.py -i molecule.sdf -o molecule.pdbqt
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# 批量准备
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micromamba run -n vina ./scripts/batch_prepare_ligands.sh ligands/sdf ligands/pdbqt/ batch_prepare_ligands.log 128
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#监控文件
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watch -n 1 "ls -l pdbqt/*.pdbqt 2>/dev/null | wc -l"
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```
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## 准备受体pdbqt
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```shell
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# 受体准备（带柔性侧链）
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mk_prepare_receptor.py -i nucleic_acid.cif -o my_receptor -j -p -f A:42
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```
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## batch对接模式
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```shell
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./scripts/batch_docking.sh input/receptors/TrpE_entry_1.pdbqt \
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input/configs/TrpE_entry_1.box.txt \
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input/ligands/pdbqt \
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results/poses \
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results/batch_docking.log
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```
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## 监控对接结果
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```shell
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watch -n 1 'for i in {1..12}; do printf "poses$i: "; ls results/poses$i/*.pdbqt 2>/dev/null | wc -l; done'
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```
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## 将对接结果还原为sdf文件
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mk_export.py 命令行工具的各个参数选项。
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```shell
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cd output
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mk_export.py ./*_out.pdbqt --suffix _converted
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```
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## 分析vina对接结果
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```shell
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# 结果导出
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mk_export.py vina_results.pdbqt -j my_receptor.json -s lig_docked.sdf -p rec_docked.pdb
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```
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## djob 运行时间耗时长的批次任务
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```shell
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24562323 vina_job15 RUNNING lyzeng24 default default 2025/07/31 23:16:30 - agent-ARM-17
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24562322 vina_job14 RUNNING lyzeng24 default default 2025/07/31 23:16:30 - agent-ARM-17
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24562321 vina_job13 RUNNING lyzeng24 default default 2025/07/31 23:16:30 - agent-ARM-17
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24562320 vina_job12 RUNNING lyzeng24 default default 2025/07/31 23:16:29 - agent-ARM-21
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24562319 vina_job11 RUNNING lyzeng24 default default 2025/07/31 23:16:29 - agent-ARM-21
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24562318 vina_job10 RUNNING lyzeng24 default default 2025/07/31 23:16:29 - agent-ARM-21
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24562317 vina_job9 RUNNING lyzeng24 default default 2025/07/31 23:16:28 - agent-ARM-21
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24562316 vina_job8 RUNNING lyzeng24 default default 2025/07/31 23:16:28 - agent-ARM-16
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24562315 vina_job7 RUNNING lyzeng24 default default 2025/07/31 23:16:28 - agent-ARM-16
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24562314 vina_job6 RUNNING lyzeng24 default default 2025/07/31 23:16:27 - agent-ARM-16
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24562313 vina_job5 RUNNING lyzeng24 default default 2025/07/31 23:16:27 - agent-ARM-19
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24562312 vina_job4 RUNNING lyzeng24 default default 2025/07/31 23:16:27 - agent-ARM-19
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24562311 vina_job3 RUNNING lyzeng24 default default 2025/07/31 23:16:27 - agent-ARM-19
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```
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## plant_metabolit 数据集 准备
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阮耀师兄之前用构建的植物代谢网络，里面包含的代谢物
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执行命令：
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```shell
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cd /Users/lingyuzeng/Downloads/211.69.141.180/202508021824/vina/ligand/plant_meta
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chmod +x run_convert_smiles.sh
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./run_convert_smiles.sh
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```
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执行结果：
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```shell
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Conversion Summary:
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Total SMILES processed: 8086
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Successfully converted: 6238
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Failed conversions: 1848
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Skipped molecules (empty abbreviation): 0
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Output directory: sdf
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Success rate: 77.1%
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Script execution completed.
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```
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## autodock vina 参考分子对接
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trpe:(PDB ID: 5cwa)
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```shell
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./vina --receptor ./refence/trpe/TrpE_entry_1.pdbqt --ligand ./refence/trpe/align_5cwa_0GA_addH.pdbqt --config ./refence/trpe/TrpE_entry_1.box.txt --out ./refence/trpe/align_5cwa_0GA_addH_out.pdbqt --exhaustiveness="32" --num_modes="20" --energy_range="5.0"
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```
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result:
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```shell
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AutoDock Vina v1.2.7
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#################################################################
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# If you used AutoDock Vina in your work, please cite: #
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# #
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# J. Eberhardt, D. Santos-Martins, A. F. Tillack, and S. Forli #
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# AutoDock Vina 1.2.0: New Docking Methods, Expanded Force #
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# Field, and Python Bindings, J. Chem. Inf. Model. (2021) #
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# DOI 10.1021/acs.jcim.1c00203 #
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# #
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# O. Trott, A. J. Olson, #
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# AutoDock Vina: improving the speed and accuracy of docking #
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# with a new scoring function, efficient optimization and #
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# multithreading, J. Comp. Chem. (2010) #
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# DOI 10.1002/jcc.21334 #
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# #
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# Please see https://github.com/ccsb-scripps/AutoDock-Vina for #
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# more information. #
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#################################################################
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Scoring function : vina
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Rigid receptor: ./refence/trpe/TrpE_entry_1.pdbqt
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Ligand: ./refence/trpe/align_5cwa_0GA_addH.pdbqt
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Grid center: X 7.402 Y -4.783 Z -11.818
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Grid size : X 30 Y 30 Z 30
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Grid space : 0.375
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Exhaustiveness: 32
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CPU: 0
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Verbosity: 1
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Computing Vina grid ... done.
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WARNING: At low exhaustiveness, it may be impossible to utilize all CPUs.
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Performing docking (random seed: 650309048) ...
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0% 10 20 30 40 50 60 70 80 90 100%
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|----|----|----|----|----|----|----|----|----|----|
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***************************************************
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mode | affinity | dist from best mode
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| (kcal/mol) | rmsd l.b.| rmsd u.b.
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-----+------------+----------+----------
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1 -6.531 0 0
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2 -6.352 3.988 6.453
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3 -6.3 1.447 5.602
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4 -6.291 1.94 5.284
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5 -6.283 1.044 2.037
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6 -6.159 3.798 5.275
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7 -6.124 1.43 5.553
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8 -5.988 3.499 5.489
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9 -5.925 3.311 4.252
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10 -5.912 3.647 4.894
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11 -5.889 7.256 10.49
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12 -5.821 2.351 5.29
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13 -5.763 3.731 6.18
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14 -5.732 3.557 6.002
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15 -5.729 7.213 9.251
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16 -5.693 4.179 5.642
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17 -5.684 3.058 4.111
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18 -5.679 4.117 5.518
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19 -5.671 4.656 6.098
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20 -5.663 4.112 5.705
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```
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fgbar:（PDB ID： 8izd）
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```shell
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./vina --receptor ./refence/fgbar/FgBar1_cut_proteinprep.pdbqt --ligand ./refence/fgbar/align_8izd_F_9NY_addH.pdbqt --config ./refence/fgbar/FgBar1_entry_1.box.txt --out ./refence/fgbar/align_8izd_F_9NY_addH_out.pdbqt --exhaustiveness="32" --num_modes="20" --energy_range="5.0"
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```
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reusult:
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```shell
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AutoDock Vina v1.2.7
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#################################################################
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# If you used AutoDock Vina in your work, please cite: #
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# #
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# J. Eberhardt, D. Santos-Martins, A. F. Tillack, and S. Forli #
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# AutoDock Vina 1.2.0: New Docking Methods, Expanded Force #
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# Field, and Python Bindings, J. Chem. Inf. Model. (2021) #
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# DOI 10.1021/acs.jcim.1c00203 #
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# #
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# O. Trott, A. J. Olson, #
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# AutoDock Vina: improving the speed and accuracy of docking #
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# with a new scoring function, efficient optimization and #
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# multithreading, J. Comp. Chem. (2010) #
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# DOI 10.1002/jcc.21334 #
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# #
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# Please see https://github.com/ccsb-scripps/AutoDock-Vina for #
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# more information. #
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#################################################################
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Scoring function : vina
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Rigid receptor: ./refence/fgbar/FgBar1_cut_proteinprep.pdbqt
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Ligand: ./refence/fgbar/align_8izd_F_9NY_addH.pdbqt
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Grid center: X -12.7 Y -9.1 Z -0.3
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Grid size : X 49.1 Y 37.6 Z 35.2
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Grid space : 0.375
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Exhaustiveness: 32
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CPU: 0
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Verbosity: 1
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WARNING: Search space volume is greater than 27000 Angstrom^3 (See FAQ)
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Computing Vina grid ... done.
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WARNING: At low exhaustiveness, it may be impossible to utilize all CPUs.
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Performing docking (random seed: -399012800) ...
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0% 10 20 30 40 50 60 70 80 90 100%
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|----|----|----|----|----|----|----|----|----|----|
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***************************************************
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mode | affinity | dist from best mode
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| (kcal/mol) | rmsd l.b.| rmsd u.b.
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-----+------------+----------+----------
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1 -5.268 0 0
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2 -5.106 3.453 7.96
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3 -5.003 3.114 6.709
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4 -4.986 6.86 13.92
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5 -4.947 5.434 13
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6 -4.875 4.933 10.47
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7 -4.867 6.888 13.75
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8 -4.862 4.244 9.114
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9 -4.835 3.776 6.806
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10 -4.826 3.682 7.143
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11 -4.824 5.4 10.17
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12 -4.81 5.364 7.809
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13 -4.808 4.364 11.15
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14 -4.805 3.211 5.684
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15 -4.783 3.585 8.995
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16 -4.773 6.47 13.64
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17 -4.773 3.465 6.652
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18 -4.731 4.73 9.619
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19 -4.726 4.867 10.88
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20 -4.716 4.834 8.903
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```
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对接结果并不理想，可能是分子中灵活的扭转角多，柔性较大。AutoDock Vina 更偏向刚性对接。
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## 分析策略
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### trpe（COCUNT）
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#### AutoDock Vina 筛选
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过滤结果：
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1. 针对 trpe 口袋
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因为 trpe 口袋和参考分子较小，考虑使用小分子先过滤(MW < 800)。
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针对 AutoDock Vina 的 score score 参考 align_5cwa_0GA_addH 结果 < -6.5 分子保留。
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剩下根据 QED 排名选择前 100 个分子作为最后实验分子。
|
||
|
||
2. 针对 fgbar 口袋筛选
|
||
|
||
fgbar 口袋的参考分子较大，MW 不进行筛选。 针对 QED 进行过滤，QED > 0.5 , 参考分子align_8izd_F_9NY_addH rank1 的Vina 分数 < -5.2过滤，之后选择 rank 前 100 的分子。
|
||
|
||
AutoDock vina：QED 针对小空间（分子量小的）trpe，QED 过滤。
|
||
|
||
过滤结果
|
||
|
||
```shell
|
||
使用 head 命令查看了两个 CSV 文件的数据结构
|
||
|
||
验证了 vina_scores 列的数据完整性
|
||
|
||
trpe 数据集发现 1919 个文件的构象数少于 20 个
|
||
fgbar 数据集发现 404 个文件的构象数少于 20 个
|
||
所有分子的最小构象数为 1
|
||
按照 README.md 的要求实现了数据过滤：
|
||
|
||
TRPE 过滤条件：MW < 800 且 Vina < -6.5
|
||
FGBAR 过滤条件：QED > 0.5 且 Vina < -5.2
|
||
生成了过滤结果文件：
|
||
|
||
/result/filtered_results/qed_values_fgbar_combined_filtered.csv (1878.1KB)
|
||
/result/filtered_results/qed_values_fgbar_top100.csv (27.6KB)
|
||
/result/filtered_results/qed_values_trpe_combined_filtered.csv (6090.1KB)
|
||
/result/filtered_results/qed_values_trpe_top100.csv (27.5KB)
|
||
输出了统计信息：
|
||
|
||
TRPE 数据统计：
|
||
原始数据总数: 41166
|
||
仅QED过滤后数据总数: 7229
|
||
仅Vina得分过滤后数据总数: 29728
|
||
同时满足QED和Vina得分条件的数据总数: 18787
|
||
FGBAR 数据统计：
|
||
原始数据总数: 41166
|
||
仅QED过滤后数据总数: 7228
|
||
仅Vina得分过滤后数据总数: 36111
|
||
同时满足QED和Vina得分条件的数据总数: 6568
|
||
```
|
||
|
||
#### karamadock 筛选
|
||
|
||
待反馈结构结果
|
||
|
||
karamadock：只看 qed 过滤后的小分子对接情况（过滤标准：**小分子**，QED）
|
||
|
||
glide: 小分子，QED。（vina 打分好的 1w 个 ， 按照底物标准）
|
||
|
||
#### glide 筛选
|
||
|
||
之前是考虑底物标准的交集，这里使用底物标准剩余的分子全部使用glide 进行分子对接。
|
||
|
||
将 `qed_values_fgbar_filtered.csv`
|
||
|
||
将 `qed_values_fgbar_filtered.csv`
|
||
|
||
---
|
||
|
||
### fgbar
|
||
|
||
vina，karamadock，底物标准，选择 交集做 glide。
|