| Issue |
EPJ Web Conf.
Volume 173, 2018
Mathematical Modeling and Computational Physics 2017 (MMCP 2017)
|
|
|---|---|---|
| Article Number | 06010 | |
| Number of page(s) | 4 | |
| Section | Optimization and Simulation | |
| DOI | https://doi.org/10.1051/epjconf/201817306010 | |
| Published online | 14 February 2018 | |
https://doi.org/10.1051/epjconf/201817306010
Parallel Evolutionary Optimization Algorithms for Peptide-Protein Docking
1 Institute of System Analysis and Control, Dubna State University, Universitetskaya str. 19, Dubna 141980, Moscow Region, Russian Federation
2 Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Leninskie Gory 1, Bldg. 52, Moscow 119991, Russian Federation
* e-mail: svpoluyan@gmail.com
** e-mail: ershovnm@gmail.com
Published online: 14 February 2018
In this study we examine the possibility of using evolutionary optimization algorithms in protein-peptide docking. We present the main assumptions that reduce the docking problem to a continuous global optimization problem and provide a way of using evolutionary optimization algorithms. The Rosetta all-atom force field was used for structural representation and energy scoring. We describe the parallelization scheme and MPI/OpenMP realization of the considered algorithms. We demonstrate the efficiency and the performance for some algorithms which were applied to a set of benchmark tests.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.