A Computer Vision Approach to Align and Compare Protein Cavities: Application to Fragment-Based Drug Design

Identifying local similarities in binding sites from distant proteins is a major hurdle to rational drug design. We herewith present a novel method, borrowed from computer vision, adapted to mine fragment subpockets and compare them to whole ligand-binding sites. Pockets are represented by pharmacophore-annotated point clouds mimicking ideal ligands or fragments. Point cloud registration is used to find the transformation enabling an optimal overlap of points sharing similar topological and pharmacophoric neighborhoods. The method (ProCare) was calibrated on a large set of druggable cavities and applied to the comparison of fragment subpockets to entire cavities. A collection of 33,953 subpockets annotated with their bound fragments was screened for local similarity to cavities from recently described protein X-ray structures. ProCare was able to detect local similarities between remote pockets and transfer the corresponding fragments to the query cavity space, thereby proposing a first step to fragment-based design approaches targeting orphan cavities.