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

Abstract

The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.

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Data availability

All data are publicly available on GitHub.

Code availability

Installation notes, user manual and support for CAVIAR are available at https://jr-marchand.github.io/caviar/. The GitHub repository hosts the CAVIAR source code and validation sets at https://github.com/jr-marchand/caviar. A conda package is hosted on Anaconda cloud at https://anaconda.org/jr-marchand/caviar. Source code and data available under a MIT license.

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Acknowledgements

The authors thank Imtiaz Hossein, Michael Schaefer and Richard Lewis for insightful discussions. J.-R.M. thanks the ProDy development team and generally all contributors to open source codes for their crucial work.

Funding

This work was supported by the postdoctoral office of the Novartis Institutes for Biomedical Research.

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The study was designed by all authors. JRM wrote the software and performed the analysis. JRM and FS analyzed the results. The manuscript was written by JRM and FS. All authors have given approval to the final version of the manuscript.

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Correspondence to Jean-Rémy Marchand or Finton Sirockin.

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Marchand, JR., Pirard, B., Ertl, P. et al. CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities. J Comput Aided Mol Des 35, 737–750 (2021). https://doi.org/10.1007/s10822-021-00390-w

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Keywords

  • Binding pocket
  • Cavity descriptors
  • Subcavities
  • Subpocket
  • Ligandability
  • Fragment-based drug design