drug-protein-prep
learningmatter-mit/AtomisticSkills/.agents/skills/drug-protein-prep/SKILL.md
Prepare macromolecular receptor structures (PDB/mmCIF or RCSB PDB ID) for docking or simulation by fixing common structure issues and adding hydrogens.
Skill172 starsChanged 4 months ago
What's in it
- protein-prep
- Goal
- Instructions
- 1. Prepare a receptor to PDB (Cleanup + Hydrogens)
- 2. Convert to PDBQT (for AutoDock Vina)
- 3. Keep cofactors/metal ions
- 4. Use a biological assembly (recommended when oligomerization matters)
- 5. Prepare from a local structure file
- 6. Validate the output (strongly recommended)
- Examples
- Full Workflow: HIV-1 Protease
- Constraints
---
name: drug-protein-prep
description: Prepare macromolecular receptor structures (PDB/mmCIF or RCSB PDB ID) for docking or simulation by fixing common structure issues and adding hydrogens.
category: [drug-discovery]
---
# protein-prep
## Goal
To prepare protein (and optionally nucleic acid) receptor structures for molecular docking (e.g., AutoDock Vina) by:
1) retrieving coordinates from RCSB PDB (optional),
2) fixing common structural issues (missing atoms, nonstandard residues),
3) adding hydrogens at a target pH.
> **Note**: This skill handles structure cleanup and protonation. To convert the result to **PDBQT** for docking, use the `mcp_drugdisc_convert_to_pdbqt` tool.
## Instructions
### 1. Prepare a receptor to PDB (Cleanup + Hydrogens)
This script manages missing atoms, nonstandard residues, and protonation.
```bash
# Env: drugdisc-agent
python .agents/skills/drug-protein-prep/scripts/prepare_protein.py \
--pdb_id 1iep \
--chains A \
--ph 7.0 \
--heterogens none \
--missing_residues ignore \
--output_dir protein_prep/
```
### 2. Convert to PDBQT (for AutoDock Vina)
Use the MCP tool to convert the prepared PDB to PDBQT format.
```bash
mcp_drugdisc_convert_to_pdbqt(
input_data="protein_prep/1IEP_prepared.pdb",
output_path="protein_prep/1IEP.pdbqt",
input_type="pdb"
)
```
### 3. Keep cofactors/metal ions
```bash
# Env: drugdisc-agent
python .agents/skills/drug-protein-prep/scripts/prepare_protein.py \
--pdb_id 1iep \
--chains A \
--heterogens non-water \
--delete_resname SO4 GOL \
--output_dir protein_prep_keep_cofactors/
```
### 4. Use a biological assembly (recommended when oligomerization matters)
```bash
# Env: drugdisc-agent
python .agents/skills/drug-protein-prep/scripts/prepare_protein.py \
--pdb_id 1iep \
--assembly 1 \
--chains A \
--output_dir protein_prep_assembly1/
```
### 5. Prepare from a local structure file
```bash
# Env: drugdisc-agent
python .agents/skills/drug-protein-prep/scripts/prepare_protein.py \
--pdb_file receptor.pdb \
--heterogens none \
--output_dir protein_prep_local/
```
### 6. Validate the output (strongly recommended)
After preparation:
* Inspect the JSON summary for **missing residues**, **nonstandard residue replacements**, and **atoms added**.
* Visually inspect the binding site and check for:
* correct oligomeric state,
* retained/removed cofactors and metal ions,
* sensible protonation (especially histidines),
* alternate locations resolved appropriately.
If protonation is critical, consider a hydrogen optimization / pKa-aware tool (e.g., Reduce/Reduce2, PROPKA/PDB2PQR/H++), then regenerate PDBQT from the protonated receptor.
## Examples
### Full Workflow: HIV-1 Protease
1. Prepare the structure:
```bash
# Env: drugdisc-agent
python .agents/skills/drug-protein-prep/scripts/prepare_protein.py \
--pdb_id 1hsg \
--chains A B \
--heterogens none \
--ph 7.0 \
--output_dir hiv_prep/
```
2. Convert to PDBQT:
```bash
mcp_drugdisc_convert_to_pdbqt(
input_data="hiv_prep/1HSG_prepared.pdb",
output_path="hiv_prep/1HSG.pdbqt",
input_type="pdb"
)
```
## Constraints
* **Environment**: Requires `drugdisc-agent`.
* **Core dependencies**: `pdbfixer`, `openmm`.
* **Protonation**: Default pH-based hydrogen addition is a baseline.
* **Missing residues**: By default, missing residues are ignored to avoid introducing uncertain loop models.
* **PDBQT**: PDBQT conversion is delegated to the `mcp_drugdisc_convert_to_pdbqt` tool (which uses Meeko).
---
**Author:** Matthew Cox
**Contact:** [GitHub @mcox3406](https://github.com/mcox3406)
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