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GPU-Based Point Cloud Superpositioning for Structural Comparisons of Protein Binding Sites | IEEE Journals & Magazine | IEEE Xplore

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 s...Show More

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.
Published in: IEEE/ACM Transactions on Computational Biology and Bioinformatics ( Volume: 15, Issue: 3, 01 May-June 2018)
Page(s): 740 - 752
Date of Publication: 07 November 2016

ISSN Information:

PubMed ID: 27845672

Funding Agency:


References

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