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drug-mmpbsa-gbsa

Compute MM-GBSA and MM-PBSA binding free-energy estimates from protein–ligand MD trajectories.

144|21|Updated Jan 8, 2026
Search Tags:#drug discovery#protein-ligand#molecular dynamics#mm-pbsa#mm-gbsa#implicit solvent#rescoring
One-click install
npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill drug-mmpbsa-gbsa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: drug-mmpbsa-gbsa
Source: https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/drug-mmpbsa-gbsa
Command: npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill drug-mmpbsa-gbsa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openmm, openmmforcefields, openff-toolkit, rdkit, MDAnalysis, parmed, AmberTools, cpptraj, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill computes relative binding free-energy estimates for protein–ligand complexes directly from an already-generated protein–ligand MD trajectory, using the single-trajectory MM-GBSA / MM-PBSA end-point method.

Core Features & Use Cases

  • Fast OpenMM GBn2 backend (MM-GBSA): rescoring via compute_mmgbsa.py with an OpenMM implicit solvent GBn2 model for high-throughput pose ranking.
  • AmberTools MMPBSA backend (MM-GBSA and/or MM-PBSA): rescoring via compute_mmpbsa.py using MMPBSA.py to support both GB (multiple igb choices) and PB in one run.
  • Trajectory-frame energy reporting: outputs per-frame energies and summary statistics, including caveats about correlated frames and the need for replicates.
  • Practical drug-discovery use case: re-ranking HTVS-docked hits after MD refinement as an orthogonal signal to geometry/stability metrics.

Quick Start

Run a GB-only rescoring from your solvated complex topology and production DCD with the ligand SDF by executing python .agents/skills/drug-mmpbsa-gbsa/scripts/compute_mmgbsa.py --topology md/system/complex_solvated.pdb --trajectory md/run/production.dcd --ligand_sdf md/ligand.sdf --ligand_resname UNL --skip_ns 0.5 --stride 5 --output_dir md/mmgbsa/.

Frequently Asked Questions about drug-mmpbsa-gbsa

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate MM-GBSA binding free energy from an existing protein-ligand MD trajectory?▼

Compute MM-GBSA binding free energy by running the OpenMM GBn2 backend on a solvated complex topology and production trajectory, which outputs per-frame dG estimates for relative ranking.

What is the difference between MM-PBSA and MM-GBSA for rescoring docked hits?▼

MM-GBSA uses the generalized Born implicit solvent model for fast rescoring, whereas MM-PBSA uses Poisson-Boltzmann equations via AmberTools, allowing both calculations simultaneously for orthogonal binding analysis.

Do I need AmberTools to run MM-PBSA calculations on protein-ligand complexes?▼

Yes, AmberTools is required for MM-PBSA calculations, as the backend leverages MMPBSA.py to support both Poisson-Boltzmann and multiple generalized Born (igb) options in a single run.

What input files are required to compute MM-GBSA binding free energy from molecular dynamics?▼

Computing MM-GBSA binding free energy requires a solvated complex topology PDB, a production trajectory DCD, and a ligand SDF file, with an optional cofactor SDF if present.

Can I control dielectric constants and frame sampling during MM-GBSA rescoring?▼

Yes, you can control dielectric constants and frame sampling during MM-GBSA rescoring by specifying user-controlled parameters such as skip_ns and stride to manage trajectory frame extraction.

What are the limitations of single-trajectory MM-GBSA for relative binding free energy estimation?▼

Single-trajectory MM-GBSA limitations include correlated frames in energy reporting and the necessity of running replicates to obtain statistically robust relative binding free-energy estimates.

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