Publication Date
10-7-2021
Document Type
Article
Publication Title
Journal of Molecular Graphics and Modelling
Volume
110
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
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Department
Chemistry
Recommended Citation
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 (2021). https://doi.org/10.1016/j.jmgm.2021.108044
Included in
Artificial Intelligence and Robotics Commons, Molecular Genetics Commons, Organic Chemistry Commons
Comments
This is the Version of Record and can also be read online here.