  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 SES probe spheres obtained from the geometrical primitives generated
by the NanoShaper software. 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 on which geometrical (clustering) and chemical
(residues) properties characterize a good binding site. The method also
provides a segmentation of pockets into smaller subpockets. We prove that
subpockets are a reliable representation that pinpoint the binding site with
greater precision. Site Ferret is outstanding in its versatility, accurately
predicting a wide range of binding sites, from small molecules to peptides and
difficult shallow sites.
