P2Rank: A Machine Learning-Based Tool for Protein-Ligand Binding Site Prediction

P2Rank is a machine learning-based tool for predicting protein-ligand binding sites, designed to address the issues of time-consuming, high-cost, or external resource-dependent traditional methods in drug discovery and molecular biology research. By sampling points on the protein's solvent-accessible surface and using machine learning for scoring and clustering, it achieves high prediction success rates without relying on external complex feature calculation software or known template databases, providing an efficient and accurate solution for this field.

Protein-ligand interactions are the foundation of life activities, but predicting binding sites has long been a challenge in computational biology. Traditional methods rely on complex physicochemical feature calculations or known structure alignments, which have issues like high computational costs or limitations from the coverage of known structures. P2Rank's design goal is to break through these limitations and provide a fast and accurate prediction solution.

The core strategies of P2Rank include:

1. Solvent-Accessible Surface (SAS) Point Sampling: Systematically sample points on the protein's solvent-accessible surface (SAS) as scoring targets;
2. Machine Learning Scoring Model: Trained on known complex structures, it outputs ligand-binding propensity scores without relying on external feature calculations;
3. Clustering and Site Identification: Form potential binding sites by clustering high-scoring points, providing detailed information such as center coordinates and scores.

P2Rank's version updates continuously expand its features:

• Version 2.5: Prediction speed increased by approximately two times, supports ChimeraX visualization and improved fpocket re-scoring;
• Version 2.4: Added mmCIF format support, adapted to AlphaFold models and NMR/cryo-EM structures (no dependency on B-factor features);
• Version 2.4.2: Supports BinaryCIF format, fpocket re-scoring, and Zstandard compression.

P2Rank is a standalone command-line tool that supports multi-format input:

• Basic Command: prank predict -f protein.pdb (supports PDB, mmCIF, and other formats as well as compressed files);
• Batch Processing: Parallel processing via dataset description file (.ds): prank predict -threads 8 dataset.ds;
• AlphaFold Adaptation: Use -c alphafold configuration to optimize predictions;
• Output Files: Include predicted sites (_predictions.csv), residue scores (_residues.csv), visualization scripts, and surface point data.

P2Rank has a wide range of application scenarios:

• Drug discovery: Quickly identify potential drug targets;
• Functional annotation: Assist in predicting protein functional sites;
• Structural biology: Provide guidance for experimental design;
• AlphaFold adaptation: Effectively handle massive predicted structures, filling gaps in the field.

Through concise and effective algorithm design, P2Rank achieves high accuracy and fast prediction, adapting to the trend of predicted structures brought by AlphaFold. With the advancement of deep learning and structure prediction technologies, it will play a more important role in drug discovery and functional research. Its open-source nature and active updates provide a foundation for community improvements.