Automatic Cavity Identification and Decomposition into Subpockets with CAVIAR

Motivation: The detection of small molecules binding sites in proteins is central to structure based drug design. Many tools were developed in the last 40 years, but only few of them are available today, open-source, and suitable for the analysis of large databases or for the integration in automatic workflows. In addition, no software can characterize subpockets solely with the information of the protein structure, a pivotal concept in fragment-based drug design.

Results: CAVIAR is a new open source tool for protein cavity identification and rationalization. Protein pockets are automatically detected based on the protein structure. It comprises a subcavity segmentation algorithm that decomposes binding sites into subpockets without requiring the presence of a ligand. The defined subpockets mimick the empirical definitions of subpockets in medicinal chemistry projects. A tool like CAVIAR may be valuable to support chemical biology, medicinal chemistry and ligand identification efforts. Our analysis of the entire PDB and the

PDBBind confirms that liganded cavities tend to be bigger, more hydrophobic and more complex than apo cavities. Moreover, in line with the paradigm of fragment-based drug design, the binding affinity scales relatively well with the number of subcavities filled by the ligand. Compounds binding to more than three of the subcavities identified by CAVIAR are mostly in the nanomolar or better range of affinities to their target.

Availability and implementation: Installation notes, user manual and support for CAVIAR are available at https://jr-marchand.github.io/caviar/. The CAVIAR GUI and CAVIAR command line tool are available on GitHub at https://github.com/jr-marchand/caviar and the package is hosted on Anaconda cloud at https://anaconda.org/jr-marchand/caviar under a MIT license. The GitHub

repository also hosts the validation datasets.