PPAP
commit f4b264eDeep learning-powered protein-protein binding affinity prediction
Deep learning-powered protein-protein binding affinity prediction
Not available for new jobs
PPAP (Protein-Protein Affinity Predictor) estimates protein-protein binding affinity from an assembled PDB complex. It returns the model's −ΔG score in kcal/mol and a dissociation constant (Kd) calculated at 25 °C.
The model combines protein-interface graph features with per-residue embeddings from ESM2-3B. It predicts affinity for existing protein-protein interfaces; it does not dock partners or score protein-small-molecule binding.
Upload one PDB or ENT structure containing 2–4 protein chains in its first model. ProteinIQ applies the same structural assumptions used by PPAP before starting the GPU run:
MODEL is analyzed.Ligands, waters, ions, and nucleic-acid chains are not binding partners for this tool.
With Analyze all chain pairs enabled, PPAP evaluates every two-chain combination in the first model. A four-chain structure produces six pairs: A_B, A_C, A_D, B_C, B_D, and C_D.
Disable that setting to enter specific partners as comma-separated receptor_ligand groups:
A_B compares chain A with chain B.HL_Y treats chains H and L together as one partner and chain Y as the other.AB_CD, A_C requests two grouped comparisons.A chain cannot appear on both sides of the same comparison, and duplicate comparisons are rejected.
The results table contains one row per requested partner pair:
| Column | Meaning |
|---|---|
Chain Pair | The submitted receptor_ligand grouping. |
−ΔG (kcal/mol) | PPAP's positive negative-binding-free-energy score. Larger values indicate stronger predicted binding. |
Kd (25 °C) | Dissociation constant calculated by PPAP at 298.15 K. Lower values indicate tighter predicted binding. |
PPAP uses Kd = exp(−(−ΔG)/(RT)). This is why its displayed −ΔG values are positive, while conventional ΔG values for favorable binding are negative.
Downloads include the native output/result.xlsx, a chain-label-preserving CSV, and logs from both official PPAP entrypoints.
ProteinIQ runs the PPAP graph-preparation and prediction software from source commit f4b264e, with the official checkpoint pinned and checksum-verified. Graph preparation adds small random coordinate noise and provides no seed setting, so repeated runs can differ slightly.
Predictions are computational estimates rather than experimental measurements. Use them for ranking or hypothesis generation, and confirm quantitative conclusions with SPR, ITC, or another suitable affinity assay.
Turn an IC50 into pIC50 and an assay-independent Ki, with the equation and every substituted value shown.
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Predict biomolecular complex structures and binding affinities for proteins, ligands, DNA, and RNA.
Structure and binding prediction for proteins, DNA, RNA and ligands.
Plot net charge vs pH to visualize protein charge behavior and identify the isoelectric point.
Predict protein secondary structure from amino acid propensities for helices, sheets, and turns.