Calculate binding free energies with MM/PBSA and MM/GBSA Learn more
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Configure inputs to begin
Set options on the left, then click “Submit job”.
Calculate binding free energies with MM/PBSA and MM/GBSA Learn more
Configure inputs to begin
Set options on the left, then click “Submit job”.
Calculate binding free energies with MM/PBSA and MM/GBSA Learn more
Configure inputs to begin
Set options on the left, then click “Submit job”.
gmx_MMPBSA calculates binding free energies from molecular dynamics trajectories using MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) and MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) methods. Developed by Valdés-Tresanco and colleagues, it bridges GROMACS simulations with AMBER's MMPBSA.py engine, enabling end-state free energy calculations for protein-ligand, protein-protein, and protein-nucleic acid complexes.
These methods estimate binding affinity by calculating the free energy difference between bound and unbound states. The calculation combines molecular mechanics energies (van der Waals, electrostatic) with implicit solvation models, offering a balance between the speed of empirical scoring functions and the accuracy of rigorous alchemical methods.
Binding free energy () is computed from snapshots extracted from an MD trajectory:
Each term comprises several energy components:
| Component | Description |
|---|---|
| Van der Waals interactions (Lennard-Jones potential) | |
| Coulombic electrostatic interactions | |
| Polar solvation energy (PB or GB model) | |
| Nonpolar solvation, proportional to solvent-accessible surface area | |
| Conformational entropy (optional, computationally expensive) |
MM/PBSA solves the Poisson-Boltzmann equation for electrostatic solvation, providing higher accuracy but at greater computational cost. MM/GBSA uses analytical Generalized Born approximations, running faster while maintaining reasonable accuracy for ranking compounds.
| Model | Description |
|---|---|
GB-HCT (igb=1) | Hawkins-Cramer-Truhlar model, fast but less accurate |
GB-OBC1 (igb=2) | Onufriev-Bashford-Case model, variant 1 |
GB-OBC2 (igb=5) | OBC variant 2, recommended for most applications |
GB-Neck (igb=7) | Improved treatment of interstitial regions |
GB-Neck2 (igb=8) | Further refinements to neck region calculations |
The OBC2 model (igb=5) provides a good balance of accuracy and speed for protein-ligand systems.
ProteinIQ runs gmx_MMPBSA 1.6.5 with a tested GROMACS 2025.4 and AmberTools 23 environment, eliminating the need to configure these dependencies locally.
| Input | Description |
|---|---|
MD Trajectory file | One or more GROMACS trajectories (.xtc, .trr) or multi-model PDB files. gmx_MMPBSA concatenates the submitted trajectories in order. |
Complex structure file | Bound complex structure (.tpr recommended, .pdb supported) passed to gmx_MMPBSA as -cs. |
Complex topology file | Optional GROMACS topology (.top) passed to gmx_MMPBSA as -cp. |
Reference structure | Optional but recommended PDB used for reliable chain assignment, passed as -cr. |
Ligand MOL2 file | Required for a protein-small-molecule complex when neither a complex nor ligand topology supplies the ligand parameters. It must be an Antechamber MOL2 file. |
Complex index file | Required GROMACS index file (.ndx). Set the receptor and ligand group IDs in the settings. |
Input parameter file | Optional gmx_MMPBSA input file. Overrides settings with custom parameters. |
| Setting | Description |
|---|---|
Calculation method | MM/GBSA (faster, suitable for screening), MM/PBSA (more accurate), or Both for comparison |
Energy decomposition | When enabled, calculates per-residue energy contributions to identify key binding site residues |
| Setting | Description |
|---|---|
Start frame | First frame to analyze (1-indexed) |
End frame | Last frame to analyze. Use 0 to keep the gmx_MMPBSA source default |
Frame interval | Analyze every nth frame. Set to 2 or higher to reduce computation for long trajectories |
| Setting | Description |
|---|---|
Salt concentration | Ionic strength in molar. The default keeps the gmx_MMPBSA source value (0.0 M) |
GB model | Generalized Born variant for MM/GBSA. GB-OBC2 (igb=5) recommended |
| Setting | Description |
|---|---|
Receptor group | Receptor group ID from the complex index file, passed as the first -cg value |
Ligand group | Ligand group ID from the complex index file, passed as the second -cg value |
Results include a separate total binding free energy and component breakdown for every calculation method requested:
| Column | Description |
|---|---|
DELTA TOTAL | Net binding free energy (kcal/mol). More negative indicates stronger binding. |
VDWAALS | Van der Waals contribution |
EEL | Electrostatic energy (gas phase) |
EGB or EPB | Polar solvation (GB or PB method) |
ESURF or ENPOLAR | Nonpolar solvation (surface area term) |
When energy decomposition is enabled, per-residue contributions identify which amino acids drive binding.
The Files tab includes the source result report, per-frame energy CSV, decomposition files when requested, bounded execution logs, the effective input file, and COMPACT_MMXSA_RESULTS.mmxsa for analysis with the gmx_MMPBSA Python API or desktop analyzer. Completed jobs stream these artifacts directly to persistent storage. Callback-free runs use a bounded inline fallback and fail explicitly instead of discarding an oversized result.
When you provide a custom input parameter file, ProteinIQ reports the calculation methods detected from the source output rather than claiming that the separate form defaults were used.
| (kcal/mol) | Interpretation |
|---|---|
| < −15 | Very strong binding |
| −10 to −15 | Strong binding (nanomolar range) |
| −5 to −10 | Moderate binding |
| > −5 | Weak binding |
These values should be interpreted as relative rankings rather than absolute affinities. MM/PBSA and MM/GBSA excel at comparing similar ligands binding to the same target, but absolute values carry significant uncertainty.
The single-trajectory approach used here assumes identical conformations for complex, receptor, and ligand. This reduces noise but misses conformational reorganization effects. Results depend heavily on the quality and length of the MD simulation—short simulations with poor sampling produce unreliable energies.
Entropy contributions are not calculated by default due to computational cost and large uncertainties. For charged ligands, electrostatic and polar solvation terms often exhibit significant cancellation, amplifying small errors. Results should guide compound prioritization rather than predict absolute binding constants.
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AutoDock Vina predicts protein-ligand binding modes with Vina, Vinardo, or AutoDock4 scoring and returns ranked poses with energy estimates.
gmx_MMPBSA calculates binding free energies from molecular dynamics trajectories using MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) and MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) methods. Developed by Valdés-Tresanco and colleagues, it bridges GROMACS simulations with AMBER's MMPBSA.py engine, enabling end-state free energy calculations for protein-ligand, protein-protein, and protein-nucleic acid complexes.
These methods estimate binding affinity by calculating the free energy difference between bound and unbound states. The calculation combines molecular mechanics energies (van der Waals, electrostatic) with implicit solvation models, offering a balance between the speed of empirical scoring functions and the accuracy of rigorous alchemical methods.
Binding free energy () is computed from snapshots extracted from an MD trajectory:
Each term comprises several energy components:
| Component | Description |
|---|---|
| Van der Waals interactions (Lennard-Jones potential) | |
| Coulombic electrostatic interactions | |
| Polar solvation energy (PB or GB model) | |
| Nonpolar solvation, proportional to solvent-accessible surface area | |
| Conformational entropy (optional, computationally expensive) |
MM/PBSA solves the Poisson-Boltzmann equation for electrostatic solvation, providing higher accuracy but at greater computational cost. MM/GBSA uses analytical Generalized Born approximations, running faster while maintaining reasonable accuracy for ranking compounds.
| Model | Description |
|---|---|
GB-HCT (igb=1) | Hawkins-Cramer-Truhlar model, fast but less accurate |
GB-OBC1 (igb=2) | Onufriev-Bashford-Case model, variant 1 |
GB-OBC2 (igb=5) | OBC variant 2, recommended for most applications |
GB-Neck (igb=7) | Improved treatment of interstitial regions |
GB-Neck2 (igb=8) | Further refinements to neck region calculations |
The OBC2 model (igb=5) provides a good balance of accuracy and speed for protein-ligand systems.
ProteinIQ runs gmx_MMPBSA 1.6.5 with a tested GROMACS 2025.4 and AmberTools 23 environment, eliminating the need to configure these dependencies locally.
| Input | Description |
|---|---|
MD Trajectory file | One or more GROMACS trajectories (.xtc, .trr) or multi-model PDB files. gmx_MMPBSA concatenates the submitted trajectories in order. |
Complex structure file | Bound complex structure (.tpr recommended, .pdb supported) passed to gmx_MMPBSA as -cs. |
Complex topology file | Optional GROMACS topology (.top) passed to gmx_MMPBSA as -cp. |
Reference structure | Optional but recommended PDB used for reliable chain assignment, passed as -cr. |
Ligand MOL2 file | Required for a protein-small-molecule complex when neither a complex nor ligand topology supplies the ligand parameters. It must be an Antechamber MOL2 file. |
Complex index file | Required GROMACS index file (.ndx). Set the receptor and ligand group IDs in the settings. |
Input parameter file | Optional gmx_MMPBSA input file. Overrides settings with custom parameters. |
| Setting | Description |
|---|---|
Calculation method | MM/GBSA (faster, suitable for screening), MM/PBSA (more accurate), or Both for comparison |
Energy decomposition | When enabled, calculates per-residue energy contributions to identify key binding site residues |
| Setting | Description |
|---|---|
Start frame | First frame to analyze (1-indexed) |
End frame | Last frame to analyze. Use 0 to keep the gmx_MMPBSA source default |
Frame interval | Analyze every nth frame. Set to 2 or higher to reduce computation for long trajectories |
| Setting | Description |
|---|---|
Salt concentration | Ionic strength in molar. The default keeps the gmx_MMPBSA source value (0.0 M) |
GB model | Generalized Born variant for MM/GBSA. GB-OBC2 (igb=5) recommended |
| Setting | Description |
|---|---|
Receptor group | Receptor group ID from the complex index file, passed as the first -cg value |
Ligand group | Ligand group ID from the complex index file, passed as the second -cg value |
Results include a separate total binding free energy and component breakdown for every calculation method requested:
| Column | Description |
|---|---|
DELTA TOTAL | Net binding free energy (kcal/mol). More negative indicates stronger binding. |
VDWAALS | Van der Waals contribution |
EEL | Electrostatic energy (gas phase) |
EGB or EPB | Polar solvation (GB or PB method) |
ESURF or ENPOLAR | Nonpolar solvation (surface area term) |
When energy decomposition is enabled, per-residue contributions identify which amino acids drive binding.
The Files tab includes the source result report, per-frame energy CSV, decomposition files when requested, bounded execution logs, the effective input file, and COMPACT_MMXSA_RESULTS.mmxsa for analysis with the gmx_MMPBSA Python API or desktop analyzer. Completed jobs stream these artifacts directly to persistent storage. Callback-free runs use a bounded inline fallback and fail explicitly instead of discarding an oversized result.
When you provide a custom input parameter file, ProteinIQ reports the calculation methods detected from the source output rather than claiming that the separate form defaults were used.
| (kcal/mol) | Interpretation |
|---|---|
| < −15 | Very strong binding |
| −10 to −15 | Strong binding (nanomolar range) |
| −5 to −10 | Moderate binding |
| > −5 | Weak binding |
These values should be interpreted as relative rankings rather than absolute affinities. MM/PBSA and MM/GBSA excel at comparing similar ligands binding to the same target, but absolute values carry significant uncertainty.
The single-trajectory approach used here assumes identical conformations for complex, receptor, and ligand. This reduces noise but misses conformational reorganization effects. Results depend heavily on the quality and length of the MD simulation—short simulations with poor sampling produce unreliable energies.
Entropy contributions are not calculated by default due to computational cost and large uncertainties. For charged ligands, electrostatic and polar solvation terms often exhibit significant cancellation, amplifying small errors. Results should guide compound prioritization rather than predict absolute binding constants.
Calculate absolute hydration free energies (AHFE) for neutral small molecules with OpenFE and GPU-accelerated OpenMM simulations.
Predict molecular energies, atomic forces, atomic and spin charges, dipole vectors, stress tensors, and Hessians from coordinate-bearing molecular structures with AIMNet2 neural network potentials.
Run GPU-accelerated molecular dynamics simulations using OpenMM. Simulate protein and protein-ligand complex dynamics with industry-standard force fields (AMBER, CHARMM) and OpenFF ligand parameterization.
GPU-accelerated molecular docking using the AutoDock4 force field. Up to 56x faster than serial AutoDock via CUDA parallelization of the Lamarckian Genetic Algorithm.
Analyze molecular dynamics trajectories using a ProteinIQ tool pinned to MDAnalysis 2.9.0. Calculate RMSD, residue-aggregated RMSF, radius of gyration, distance tracking, and additional trajectory observables from standard topology and trajectory files.
ORB v3 is a universal interatomic potential (machine learning force field) that predicts energies, forces, and stress tensors for atomic systems. Supports both molecular and materials structures with geometry optimization using conservative and direct model variants.
Open-source molecular docking platform using physics-based scoring functions. CPU-optimized algorithms achieve sub-angstrom accuracy (0.014A RMSD) without GPU requirements.
Analyze noncovalent interactions in protein-ligand complex structures with PLIP, including hydrogen bonds, hydrophobic contacts, pi interactions, salt bridges, water bridges, halogen bonds, and metal complexes.
SMINA is a fork of AutoDock Vina with enhanced scoring functions, custom scoring support, and 10-20x faster minimization. Ideal for scoring function development, pose refinement, and high-performance docking workflows.
AutoDock Vina predicts protein-ligand binding modes with Vina, Vinardo, or AutoDock4 scoring and returns ranked poses with energy estimates.