title: GPU-Based Point Cloud Superpositioning for Structural Comparisons of Protein Binding Sites.
abstract: In this paper, we present a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning. The performance of the GPU-based parallel evolution strategy is compared to a previously proposed CPU-based sequential approach for labeled point cloud superpositioning, indicating that the GPU-based parallel evolution strategy leads to qualitatively better results and significantly shorter runtimes, with speed improvements of up to a factor of 1,500 for large populations. Binary classification tests based on the ATP, NADH, and FAD protein subsets of CavBase, a database containing putative binding sites, show average classification rate improvements from about 92 percent (CPU) to 96 percent (GPU). Further experiments indicate that the proposed GPU-based labeled point cloud superpositioning approach can be superior to traditional protein comparison approaches based on sequence alignments.
authors.forename: Matthias
authors.initials: M
authors.lastname: Leinweber
authors.forename: Thomas
authors.initials: T
authors.lastname: Fober
authors.forename: Bernd
authors.initials: B
authors.lastname: Freisleben
mesh_terms: D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
chemical_list: D011506:Proteins