Docking — Protein-Ligand Docking & Virtual Screening

AutoDock Vina 1.2 · Gnina · pdbfixer · ProLIF · fpocket. For structure-based drug design: binding mode prediction, virtual screening, and lead optimization by docking.

When to Use This Skill

• Predicting how a small molecule binds to a protein (binding mode / pose)
• Virtual screening: ranking a library of compounds by predicted binding affinity
• Validating a pharmacophore hypothesis in 3D structural context
• Ensemble docking to account for protein flexibility
• Re-scoring docking poses with physics-based (MM-GB/SA) or CNN-based scoring
• Fragment-based screening (→ see fbdd skill for growing/linking)

Decision Tree — Docking vs. Other Methods

Input: protein structure 3D?
  NO  → ligand-based methods (pharmacophore, QSAR, similarity search)
  YES → docking

Compound set size?
  > 10 000   → VS pipeline (references/virtual-screening.md)
  10–10 000  → standard docking batch (references/vina-gnina.md)
  < 10       → manual docking + careful pose analysis

Goal: binding mode accuracy vs. ranking accuracy?
  Binding mode → high exhaustiveness, Gnina CNN rescoring
  Ranking      → standard Vina + clustering + MM-GB/SA rescore

Protein structure source?
  X-ray / CryoEM  → direct prep (references/protein-prep.md)
  Homology model  → validate first (→ homology-modeling skill)
  AlphaFold       → check pLDDT > 80 in pocket region before docking

Quick Start

import subprocess
from pathlib import Path

# 1. Prepare receptor (pdbfixer + obabel → PDBQT)
# See references/protein-prep.md for full workflow

# 2. Prepare ligand
import subprocess
subprocess.run([
    "obabel", "ligand.sdf", "-O", "ligand.pdbqt",
    "--gen3d", "-h"
], check=True)

# 3. Run Vina
result = subprocess.run([
    "vina",
    "--receptor", "receptor.pdbqt",
    "--ligand",   "ligand.pdbqt",
    "--center_x", "10.5",
    "--center_y", "-2.3",
    "--center_z", "14.1",
    "--size_x",   "20",
    "--size_y",   "20",
    "--size_z",   "20",
    "--exhaustiveness", "16",
    "--num_modes", "9",
    "--out", "docked.pdbqt"
], capture_output=True, text=True, check=True)

# 4. Parse best score
for line in result.stdout.splitlines():
    if line.strip().startswith("1 "):
        print("Best score:", line.split()[1], "kcal/mol")
        break

Router — What to Read

Task	Reference
Clean PDB, add H, assign protonation, define docking box	references/protein-prep.md
AutoDock Vina / Gnina docking, parameters, scoring	references/vina-gnina.md
High-throughput VS pipeline, metrics (BEDROC, EF), filtering	references/virtual-screening.md
Extract interactions (H-bonds, hydrophobic, π), ProLIF, clustering	references/pose-analysis.md
Ensemble docking, protein flexibility, MD snapshots	references/ensemble-docking.md

Key Tools

Tool	Install	Role
vina	conda install -c conda-forge autodock-vina	Docking engine (empirical scoring)
gnina	prebuilt binary or Docker	CNN scoring function
pdbfixer	conda install -c conda-forge pdbfixer	PDB cleaning, H addition, missing residues
openbabel	conda install -c conda-forge openbabel	Format conversion → PDBQT
prolif	pip install prolif	Protein-Ligand Interaction Fingerprints
fpocket	conda install -c conda-forge fpocket	Pocket detection / box definition
propka	pip install propka	pKa prediction for protonation
meeko	pip install meeko	Ligand PDBQT prep (better than obabel for Vina)

Scoring Function Reference

Method	Score type	Precision	Speed	Use case
Vina empirical	ΔG (kcal/mol)	★★★	★★★★★	VS, binding mode
Gnina CNN	unitless affinity	★★★★	★★★★	Rescoring, pose selection
MM-GB/SA	ΔG (kcal/mol)	★★★★	★★	Lead opt rescoring
FEP/TI	ΔΔG (kcal/mol)	★★★★★	★	Precise relative ranking

Installation

# Vina + OpenBabel (required)
conda install -c conda-forge autodock-vina openbabel

# pdbfixer + propka (receptor prep)
conda install -c conda-forge pdbfixer
pip install propka

# ProLIF (pose analysis)
pip install prolif

# meeko (better ligand PDBQT prep)
pip install meeko

# fpocket (pocket detection)
conda install -c conda-forge fpocket

# Gnina (CNN rescoring) — prebuilt binary
wget https://github.com/gnina/gnina/releases/latest/download/gnina
chmod +x gnina && mv gnina ~/.local/bin/

# Verify Vina
vina --version   # AutoDock Vina 1.2.x

Related Skills

• force-fields → MM-GB/SA rescoring after docking
• mdanalysis → generate conformational ensemble for ensemble docking
• homology-modeling → build receptor when no crystal structure available
• pharmacophore → pharmacophore-constrained docking, pose validation
• free-energy → FEP/TI for accurate ΔΔG after docking hit identification
• py3Dmol → 3D visualization of poses inline
• Scripts: chem_filter.py --lipinski → pre-filter library before VS
• Scripts: chem_3d.py → generate 3D conformers for ligand prep