Artificial Intelligence and Robotics | Molecular Genetics | Organic Chemistry
Journal of Molecular Graphics and Modelling
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.
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.
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