Computation and Informatics in Biology and Medicine (CIBM) Seminar

Abstract: Computational structural prediction can assist in the study of proteins in the absence of experimentally determined structural data. This work focuses on an ab initio method that can effectively predict the structure of interacting transmembrane helices.  The method applies to transmembrane associations mediated by networks of weak hydrogen bonds that form between Cα donors on one helix and backbone carbonyl groups on the other helix (Cα–H∙∙∙O=C hydrogen bonds). Specific backbone arrangements are required to orient the helices so that the geometry is compatible with interhelical hydrogen bond formation.  Due to these restrictions the structural motifs are more recognizable and provide a number of constraints making their prediction at near atomic level amenable. Our method is based on a pre-computed analysis of the conformational space that permits the formation of Cα–H∙∙∙O=C-mediated interhelical interactions, which substantially limits the search space. The result is a high-throughput ab initio method that can predict the structure of known transmembrane homodimers with near atomic precision. The rapidity of the method allows us to perform genome wide searches for Cα–H∙∙∙O=C-mediated interhelical dimers. The method provides an opportunity for building, validating and analyzing a comprehensive atlas of transmembrane interactions for entire genomes.