Accelerating Drug Discovery: P2Rank Binding Site Prediction Now Available on Vecura

What is P2Rank?

P2Rank is a high-performance, machine-learning-based command-line tool designed for the rapid and accurate prediction of ligand-binding sites on protein structures. Unlike traditional methods that rely solely on geometric analysis, P2Rank is trained on real protein–ligand structures to prioritize pockets based on their actual ligandability. It operates efficiently on standard CPUs, making it accessible for researchers who do not have access to heavy GPU infrastructure.

It helps users identify potential binding sites on protein structures, providing essential data for drug discovery pipelines. It is especially useful for structural biologists and medicinal chemists who need to quickly determine where small molecules might bind to a target protein, whether using experimental crystal structures, AlphaFold-predicted models, or cryo-EM data.

What can users do with P2Rank on Vecura?

With P2Rank on Vecura, users can:

• Predict ligand-binding pockets directly from PDB, mmCIF, or BinaryCIF protein structure files.
• Choose from specialized model profiles, such as the alphafold profile for AI-predicted structures, to ensure high-accuracy results.
• Leverage PRANK rescoring to combine Fpocket’s geometric cavity detection with P2Rank’s machine-learning-based prioritization.
• Generate visualization scripts for PyMOL and ChimeraX to intuitively inspect predicted binding sites in 3D.

What the output means

The output provides a comprehensive report including a ranked table of predicted binding pockets with calibrated probability scores, 3D centroid coordinates for docking, and per-residue ligandability annotations.

This output should be used to support scientific decision making. It does not replace experimental validation.

Why this matters

In drug discovery, identifying the "druggability" of a protein target is a critical first step. Traditional pocket finders often identify many geometric cavities that are not biologically relevant, wasting time and computational resources on non-binding sites. By using a machine-learning model trained on diverse experimental data, P2Rank filters out noise and prioritizes pockets that have the highest potential for small-molecule interaction.

Furthermore, the integration of P2Rank into automated workflows allows for high-throughput screening of protein structures. Its ability to handle both experimental structures and AlphaFold models seamlessly empowers researchers to explore a much broader chemical and structural space early in the drug design process.

• Developed by: Research group led by Radek Křenek (rdk)
• Source: GitHub Repository
• Reference: Krivák, R., & Hoksza, D. (2018). P2Rank: machine learning based tool for ligand binding site prediction. Journal of Cheminformatics, 10(1), 39.