Publication Date

10-7-2021

Document Type

Article

Department

Chemistry

Disciplines

Artificial Intelligence and Robotics | Molecular Genetics | Organic Chemistry

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

Comments

This is the Version of Record and can also be read online here.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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