---
title: P2Rank quickstart
description: >-
  Step-by-step guide to predicting binding sites on a protein structure with
  P2Rank on the platform.
tags:
  - p2rank
  - quickstart
---

## Quick binding site prediction with .bloom* platform

P2Rank uses random forest machine learning to predict ligand binding sites from protein 3D structure. This guide walks through predicting pockets on a protein, from entering a PDB ID to interpreting druggability scores.

## Step-by-step pocket prediction workflow

### 1. Access the P2Rank tool

Navigate to **P2Rank** in the .bloom* tool selector, or ask Ivy directly:

- "Run P2Rank on 6LU7 to find binding pockets"
- "Predict binding sites on my ESMFold structure"

### 2. Prepare your protein structure

**Option A: PDB ID lookup (fastest)**
- Enter a PDB accession code (e.g., `6LU7` for SARS-CoV-2 main protease, `1M17` for EGFR kinase)
- .bloom* fetches the structure from RCSB PDB automatically
- All chains in the structure are analyzed together — there's no chain-filter parameter

**Option B: Structure upload**
- Upload a PDB file from your computer
- .bloom* validates the structure and displays chain information
- Supports standard PDB format with ATOM records

**Option C: From previous jobs**
- Select a structure from a completed ESMFold prediction
- Import from AlphaFold database fetches or PDB search results
- .bloom* handles format extraction automatically

### 3. Configure prediction parameters

**Basic settings (recommended for first run):**
- **Model**: default (general-purpose, best for experimental structures)
- **Visualization**: Enable PyMOL script generation
- Keep all other parameters at defaults

**Advanced settings (via gear icon):**
- **Model variant**: default or alphafold
- **Max pockets**: Maximum number of pockets to report (1-50, default 10)
- **Visualization**: Enable/disable PyMOL script generation

### 4. Submit and monitor the job

- Click **Predict Binding Sites** to start the job
- .bloom* shows real-time progress through structure parsing, feature extraction, and classification
- Typical completion: 10-30 seconds per protein
- Results appear automatically on the canvas

### 5. Review and analyze results

**Pocket summary:**
- Ranked list of predicted pockets with probability scores
- Druggability classification for each pocket (druggable, intermediate, difficult, undruggable)
- Residue lists showing which amino acids line each pocket

**3D visualization:**
- Predicted pockets highlighted on the protein surface in the Mol* viewer
- Color-coded by pocket rank (top pocket in green, secondary pockets in yellow/orange)
- Toggle pocket visibility to compare different sites

**PyMOL script (if enabled):**
- Downloadable PyMOL script for offline visualization
- Pockets rendered as surface representations with custom coloring
- Pocket-lining residues shown as sticks

**Download options:**
- **CSV**: Pocket table with scores, druggability, residues, and centers
- **PDB**: Structure annotated with pocket occupancy in B-factor column
- **PyMOL script**: Interactive visualization for offline analysis
- **JSON**: Full structured results with all pocket metadata

## Common pocket prediction scenarios

### Pre-docking site identification
1. Enter the PDB ID of your target protein
2. Run P2Rank with the default model
3. Identify the top-scoring pocket as the primary docking target
4. Feed the pocket information into DiffDock for targeted docking

### Novel target assessment
1. Predict the structure of a novel protein with ESMFold
2. Run P2Rank with the alphafold model variant on the predicted structure
3. Assess whether druggable pockets exist on the predicted structure
4. Use druggability scores to decide whether to pursue the target

### Multi-site analysis
1. Run P2Rank on a protein with multiple known binding sites
2. Compare druggability scores across all predicted pockets
3. Identify the most druggable site for small molecule targeting
4. Look for allosteric sites beyond the primary active site

### Wild-type vs. mutant comparison
1. Run P2Rank on the wild-type structure
2. Run P2Rank on the mutant structure (or model the mutation)
3. Compare pocket rankings and druggability scores
4. Identify how mutations affect binding site properties

## Quality control checklist

### Before submission
- [ ] PDB structure is complete with no missing heavy atoms in the binding region
- [ ] Structure resolution is reasonable (<3.0 angstroms for experimental structures)
- [ ] Appropriate model variant chosen (default for experimental, alphafold for predicted structures)

### After prediction
- [ ] Top pocket corresponds to known binding site (if available for validation)
- [ ] Pocket scores are reasonable (>0.5 for high-confidence pockets)
- [ ] Pocket-lining residues make biological sense
- [ ] Druggability classification aligns with known drug target properties

## Next steps after pocket prediction

**Molecular docking:**
- Run DiffDock to dock small molecules into the top-scoring pocket
- Use pocket residue information to set up targeted docking grids
- Compare binding modes across multiple predicted pockets

**Target assessment:**
- Feed druggability scores into Target Passport for comprehensive target evaluation
- Compare pocket properties across species orthologs for selectivity analysis
- Assess whether the target is amenable to small molecule intervention

**Structure-based design:**
- Use pocket geometry and residue composition to guide pharmacophore design
- Identify key interactions (hydrogen bond donors/acceptors, hydrophobic regions) within the pocket
- Use pocket shape to filter virtual screening results for shape complementarity

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