Abstract
Cryo-electron microscopy is a technique that is capable of producing high quality three-dimensional density maps of proteins. The identification of secondary structures from within these proteins is important to help understand the protein’s overall structure and function. One of the more commonly found secondary structures is the \(\beta \) barrel. In previous papers, we presented a novel approach utilizing a genetic algorithm and ray tracing to identify and isolate \(\beta \) barrels from the density maps. However, one key limitation of that approach was the computational cost of ray tracing portion. In this paper, we applied parallel processing and graphical processing units (GPU) to increase the performance of the ray tracing. We tested this method on both experimental and simulated cryo-EM density maps. The results suggest that we were successful in speeding up our method significantly using parallelization and graphical processing units.
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Acknowledgments
This work was supported by the Graduate Research Award from the Computing and Software Systems division of University of Washington Bothell and the startup fund 74-0525. We would like to thank Dr. Kelvin Sung for his assistance on this paper.
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Ng, A., Odesile, A., Si, D. (2018). GPU Accelerated Ray Tracing for the Beta-Barrel Detection from Three-Dimensional Cryo-EM Maps. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_20
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