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Article
Statistical potentials for RNA-protein interactions optimized by CMA-ES
Journal of Molecular Graphics and Modelling
Publication Date
10-7-2021
Document Type
Article
DOI
10.1016/j.jmgm.2021.108044
Abstract
Characterizing RNA-protein interactions remains an important endeavor, complicated by the difficulty in obtaining the relevant structures. Evaluating model structures via statistical potentials is in principle straight-forward and effective. However, given the relatively small size of the existing learning set of RNA-protein complexes optimization of such potentials continues to be problematic. Notably, interaction-based statistical potentials have problems in addressing large RNA-protein complexes. In this study, we adopted a novel strategy with covariance matrix adaptation (CMA-ES) to calculate statistical potentials, successfully identifying native docking poses.
Keywords
- RNA-Protein interaction,
- Statistical potential,
- CMA-ES optimization,
- Machine learning
Creative Commons License
Creative Commons Attribution-Noncommercial-No Derivative Works 4.0
Citation Information
Takayuki Kimura, Nobuaki Yasuo, Masakazu Sekijima and Brooke Lustig. "Statistical potentials for RNA-protein interactions optimized by CMA-ES" Journal of Molecular Graphics and Modelling Vol. 110 (2021) Available at: http://works.bepress.com/brooke_lustig/25/
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