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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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PrankWeb: a web server for ligand binding site prediction and visualization.

Lukas Jendele1, Radoslav Krivak1, Petr Skoda1

  • 1Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Czech Republic.

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Summary
This summary is machine-generated.

PrankWeb offers an accessible online platform for P2Rank, a machine learning method that predicts ligand binding sites on protein surfaces. This tool aids researchers in identifying potential drug targets by analyzing protein ligandability and sequence conservation.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Drug discovery

Background:

  • Predicting ligand binding sites is crucial for understanding protein function and designing drugs.
  • Existing methods often require templates or complex setups, limiting accessibility.
  • Machine learning approaches offer a promising avenue for template-free binding site prediction.

Purpose of the Study:

  • To introduce PrankWeb, a user-friendly web interface for the P2Rank ligand binding site prediction method.
  • To provide researchers with an accessible tool for predicting and visualizing potential ligand binding sites.
  • To enable high-throughput analysis via a REST API for direct integration into computational workflows.

Main Methods:

  • P2Rank utilizes a template-free machine learning approach to predict local chemical neighborhood ligandability on solvent-accessible protein surfaces.
  • Ligandability scores are calculated for points on the protein surface and clustered to identify binding sites.
  • PrankWeb integrates sequence conservation analysis to enhance prediction and visualization.

Main Results:

  • PrankWeb provides an intuitive web interface for P2Rank predictions.
  • Users can visualize predicted binding sites directly within the web application using an integrated sequence-structure view.
  • Results can be exported as PyMOL scripts for offline analysis and visualization.
  • A REST API allows for direct server-side access, facilitating high-throughput predictions.

Conclusions:

  • PrankWeb democratizes access to advanced ligand binding site prediction using the P2Rank method.
  • The platform supports both interactive visualization and automated, high-throughput analyses.
  • PrankWeb serves as a valuable resource for researchers in structural biology, drug discovery, and computational chemistry.