Self-Organizing Fuzzy Graphs for Structure-Based Comparison of Protein Pockets
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Date
2010-12-03Type
- Journal Article
Abstract
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 sequence-independent studies of the proteome. © 2010 American Chemical Society. Show more
Publication status
publishedExternal links
Journal / series
Journal of Proteome ResearchVolume
Pages / Article No.
Publisher
American Chemical SocietySubject
Orphan protein; Drug design; Machine learning; Pocketome; Graph matchingOrganisational unit
03852 - Schneider, Gisbert / Schneider, Gisbert
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