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CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities.

Jean-Rémy Marchand1, Bernard Pirard2, Peter Ertl2

  • 1Novartis Institutes for Biomedical Research, Fabrikstrasse 16, 4056, Basel, Switzerland. jean-remy.marchand@novartis.com.

Journal of Computer-Aided Molecular Design
|May 29, 2021
PubMed
Summary
This summary is machine-generated.

CAVIAR is a new open-source tool that generates descriptors for protein binding sites. These descriptors aid in machine learning for predicting ligandability and understanding binding site function.

Keywords:
Binding pocketCavity descriptorsFragment-based drug designLigandabilitySubcavitiesSubpocket

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Area of Science:

  • Structural Biology
  • Computational Chemistry
  • Bioinformatics

Background:

  • Accurate protein binding site description is crucial for determining similarity and applying machine learning to functional predictions.
  • Existing methods may lack comprehensive descriptor generation for diverse structural inputs.

Purpose of the Study:

  • Introduce CAVIAR, an open-source tool for generating novel descriptors for protein binding sites.
  • Demonstrate the utility of CAVIAR descriptors in machine learning tasks, specifically predicting binding site ligandability.
  • Enable automatic identification and characterization of subcavities within binding sites.

Main Methods:

  • Utilized protein structures in PDB and mmCIF formats, along with molecular dynamics simulation trajectory frames as input.
  • Developed CAVIAR to generate descriptors capturing geometric and chemical properties of binding sites.
  • Implemented algorithms for automatic subcavity and subpocket assignment.

Main Results:

  • CAVIAR descriptors were successfully applied to predict binding site ligandability using machine learning.
  • The tool automatically assigns subcavities, mimicking medicinal chemistry empirical definitions.
  • Experimental binding affinity correlated with the number of occupied subcavities, with >3 subcavities yielding nanomolar affinities.

Conclusions:

  • CAVIAR provides a robust method for generating binding site descriptors applicable to machine learning.
  • The tool facilitates protein engineering and hit identification by enhancing binding site analysis.
  • Open-source availability on GitHub and Anaconda cloud promotes widespread adoption and further research.