SiteFerret: Beyond Simple Pocket Identification in Proteins.

ABSTRACT: We present a novel method for the automatic detection of pockets on protein molecular surfaces. The algorithm is based on an ad hoc hierarchical clustering of virtual probe spheres obtained from the geometrical primitives used by the NanoShaper software to build the solvent-excluded molecular surface. The final ranking of putative pockets is based on the Isolation Forest method, an unsupervised learning approach originally developed for anomaly detection. A detailed importance analysis of pocket features provides insight into which geometrical (clustering) and chemical (amino acidic composition) properties characterize a good binding site. The method also provides a segmentation of pockets into smaller subpockets. We prove that subpockets are a convenient representation to pinpoint the bin

SUBMITTER: Gagliardi L 

PROVIDER: S-EPMC10413863 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature