Self-Organizing Fuzzy Graphs for Structure-Based Comparison of Protein Pockets

Patterns of receptor-ligand interaction can be conserved in functionally equivalent proteins even in the absence of sequence homology Therefore, structural comparison of ligand-binding pockets and their pharmacophoric features allow for the characterization of so-called 'orphan proteins with known three-dimensional structure but unknown function and predict ligand promiscuity of binding pockets We present an algorithm for rapid pocket comparison (PoLiMorph), in which protein pockets are represented by self-organizing graphs that fill the volume of the cavity Vertices in these three-dimensional frameworks contain information about the local ligand-receptor interaction potential coded by fuzzy property labels For framework matching, we developed a fast heuristic based on the maximum dispersion problem, as an alternative to techniques utilizing clique detection or geometric hashing algorithms A sophisticated scoring function was applied that incorporates knowledge about property distributions and ligand-receptor interaction patterns In an all-against-all virtual screening experiment with 207 pocket frameworks extracted from a subset of PDBbind, PoLiMorph correctly assigned 81% of 69 distinct structural classes and demonstrated sustained ability to group pockets accommodating the same ligand chemotype We determined a score threshold that indicates true' pocket similarity with high reliability which not only supports structure based drug design but also allows for sequencein-dependent studies of the proteome