title: Backbone dependency further improves side chain prediction efficiency in the Energy-based Conformer Library (bEBL).
abstract: Side chain optimization is an integral component of many protein modeling applications. In these applications, the conformational freedom of the side chains is often explored using libraries of discrete, frequently occurring conformations. Because side chain optimization can pose a computationally intensive combinatorial problem, the nature of these conformer libraries is important for ensuring efficiency and accuracy in side chain prediction. We have previously developed an innovative method to create a conformer library with enhanced performance. The Energy-based Library (EBL) was obtained by analyzing the energetic interactions between conformers and a large number of natural protein environments from crystal structures. This process guided the selection of conformers with the highest propensity to fit into spaces that should accommodate a side chain. Because the method requires a large crystallographic data-set, the EBL was created in a backbone-independent fashion. However, it is well established that side chain conformation is strongly dependent on the local backbone geometry, and that backbone-dependent libraries are more efficient in side chain optimization. Here we present the backbone-dependent EBL (bEBL), whose conformers are independently sorted for each populated region of Ramachandran space. The resulting library closely mirrors the local backbone-dependent distribution of side chain conformation. Compared to the EBL, we demonstrate that the bEBL uses fewer conformers to produce similar side chain prediction outcomes, thus further improving performance with respect to the already efficient backbone-independent version of the library.
authors.affiliation: Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706.
authors.forename: Sabareesh
authors.initials: S
authors.lastname: Subramaniam
authors.forename: Alessandro
authors.initials: A
authors.lastname: Senes
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D018360:Crystallography, X-Ray; D030562:Databases, Protein; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins
keywords: conformer; protein prediction; rotamer; side chain library; side chain optimization