P2Rank

P2Rank tells you where a drug could bind — it detects and ranks the druggable pockets on a protein structure so you know which site to target. It's the first step in the docking arc (pocket → pose → rescore): the entry step before you dock a ligand with DiffDock. See what you can achieve for the full arc.

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What P2Rank does

P2Rank is a machine learning-based tool for predicting ligand binding sites (pockets) on protein structures. It uses a random forest classifier trained on structural features extracted from protein surface points to identify regions likely to bind small molecules. Unlike geometry-only methods such as fpocket, P2Rank learns from known protein-ligand complexes. Its authors report 72.0% top-n and 78.3% top-(n+2) success on COACH420, and 68.6% / 74.0% on HOLO4K, counting a pocket correct when its centre is within 4 Å of any ligand atom (DCA). Both sets are holo structures, where the pocket is already open in the coordinates — see how P2Rank results compare to the bench for what that does and does not tell you.

Citations

Krivak R, Hoksza D. P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. Journal of Cheminformatics. 2018;10:39. doi: 10.1186/s13321-018-0285-8

Krivak R, Hoksza D. Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features. Journal of Cheminformatics. 2015;7:12. doi: 10.1186/s13321-015-0059-5

Key achievements

• Machine learning advantage: In its original benchmarks P2Rank outperformed Fpocket, SiteHound, MetaPocket 2.0 and DeepSite (Krivák & Hoksza, 2018)
• No template dependence: Does not require homologous structures or known binding sites; works directly from 3D coordinates
• Speed: Predicts all pockets on a protein in seconds, enabling high-throughput screening (6.8 s for a single protein cold, under 1 s per protein when amortised across a dataset)
• Druggability assessment: The platform adds a heuristic druggability class to each pocket, computed from the P2Rank score and the pocket's residue composition (not a P2Rank output)

Core capabilities

• Pocket detection: Identify binding pockets from protein 3D structure using learned structural features
• Pocket ranking: Rank detected pockets by probability score (likelihood of being a true binding site)
• Druggability scoring: Classify each pocket as druggable, intermediate, difficult, or undruggable
• Residue identification: Report which amino acid residues line each predicted pocket
• PyMOL visualization: Generate PyMOL scripts for interactive pocket exploration

Technical approach

P2Rank operates in three stages:

1. Surface point generation: The solvent-accessible surface of the protein is sampled with evenly distributed points (Connolly surface representation)
2. Feature extraction: For each surface point, structural features are computed including:
   • Amino acid composition in the local neighborhood
   • Hydrophobicity and charge distribution
   • Curvature and depth from the convex hull
   • Accessible surface area and secondary structure context
3. Classification and clustering: The random forest classifies each point as binding/non-binding, and nearby positive points are clustered into pockets

Available model variants

When to use P2Rank

Primary use cases

Pre-docking site identification

• Blind docking guidance: Before running DiffDock, identify the most likely binding pockets to focus docking
• Multi-site analysis: For proteins with multiple potential binding sites, rank pockets by druggability to prioritize docking targets
• Allosteric site discovery: Find binding pockets beyond the known active site for allosteric drug design

Target assessment and druggability

• Target validation: Assess whether a novel target has druggable binding pockets before committing to a drug discovery campaign
• Pocket comparison: Compare pocket properties across homologous proteins or between wild-type and mutant structures
• Selectivity analysis: Identify differences in binding pockets between on-target and off-target proteins

Structural genomics

• High-throughput screening: Rapidly assess druggability of hundreds of protein structures
• Target prioritization: Rank novel targets by the quality and druggability of their predicted binding pockets
• PDB mining: Screen the PDB for structures with druggable pockets matching specific criteria

Webapp integration and workflow

Input methods in .bloom*

PDB ID lookup (fastest):

• Enter a PDB accession code (e.g., 6LU7, 1M17)
• .bloom* fetches the structure from RCSB PDB automatically
• All chains in the structure are analyzed together — there's no chain-filter parameter

Structure upload:

• Upload a PDB file from local storage or previous jobs
• Supports standard PDB format with ATOM records

From previous jobs:

• Import structures from ESMFold predictions
• Chain from AlphaFold database fetches
• Use structures from PDB search results

Integration with .bloom* ecosystem

Upstream data sources

Structure sources:

• ESMFold: Predicted structures for novel proteins
• AlphaFold DB: Pre-computed structure predictions
• PDB: Experimental crystal and cryo-EM structures

Downstream applications

Docking:

• DiffDock: Target docking to specific predicted pockets
• Automatic chaining: ask for structure → pockets → docking in one request

Target analysis:

• Target Passport: Pocket druggability data feeds into target assessment
• Comparison tools: Compare pockets across protein variants

Best paired with

• ESMFold: Predict the structure first, then identify binding pockets with P2Rank (use the alphafold model variant)
• DiffDock: Identify binding sites, then dock small molecules to the top-scoring pocket
• PDB2PQR: Prepare electrostatic surface for further binding site characterization
• Target Passport: Feed pocket druggability data into comprehensive target assessment

Scope and limitations

Supported inputs

Structure requirements

• Format: PDB format with ATOM records
• Resolution: Best results with experimental structures at <3.0 angstrom resolution
• Completeness: All heavy atoms in the protein should be present; missing atoms reduce prediction accuracy
• Single chain or multi-chain: Both supported; the full structure as submitted is analyzed (no chain-filter parameter)

Structure sources

• RCSB PDB: Experimental crystal and cryo-EM structures
• ESMFold: Predicted structures (use the alphafold model variant)
• AlphaFold DB: Pre-computed structure predictions

Prediction limitations

Accuracy considerations

• Experimental structures: Best performance on high-resolution crystal structures
• Predicted structures: Lower accuracy on ESMFold/AlphaFold models, especially in low-confidence regions; use the alphafold model variant
• Disordered regions: Cannot predict pockets in intrinsically disordered regions
• Ligand-induced pockets: Cryptic binding sites that only form upon ligand binding may not be detected

Druggability caveats

• Druggability classification is based on pocket properties (size, shape, hydrophobicity) and is approximate
• Small pockets may be classified as undruggable even if they bind fragment-sized molecules
• PPI (protein-protein interaction) sites are typically classified as difficult but may still be druggable with specialized compounds

Performance characteristics

Speed

• Typical runtime: 10-30 seconds per protein structure
• Scaling: Roughly linear with protein size
• Batch processing: Multiple structures processed sequentially

Deterministic behavior

• Same structure and model always produce identical pocket predictions
• Results are stable across platform updates (model weights are fixed)

Chat usage

Example prompts

• "Run P2Rank on 1M17 to identify binding pockets"
• "Find binding sites on my ESMFold structure"
• "Predict druggable pockets on the EGFR kinase structure"
• "Compare binding pockets between wild-type and mutant structures"
• "Which pocket on 6LU7 is most druggable?"

Workflow chaining

• Feeds into: DiffDock (targeted docking at predicted pockets), Target Passport
• Accepts from: ESMFold (predicted structures), PDB Search (experimental structures), AlphaFold DB

FAQ

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