Communications in Information and Systems

Volume 21 (2021)

Number 1

A Bayes-inspired theory for optimally building an efficient coarse-grained folding force field

Pages: 65 – 83

DOI: https://dx.doi.org/10.4310/CIS.2021.v21.n1.a4

Authors

Travis Hurst (Department of Physics, University of Missouri, Columbia, Mo., U.S.A.)

Dong Zhang (Department of Physics, University of Missouri, Columbia, Mo., U.S.A.)

Yuanzhe Zhou (Department of Physics, University of Missouri, Columbia, Mo., U.S.A.)

Shi-Jie Chen (Departments of Physics and of Biochemistry, Institute for Data Science and Informatics, University of Missouri, Columbia, Mo., U.S.A.)

Abstract

Because of their potential utility in predicting conformational changes and assessing folding dynamics, coarse-grained (CG) RNA folding models are appealing for rapid characterization of RNA molecules. Previously, we reported the iterative simulated RNA reference state (IsRNA) method for parameterizing a CG force field for RNA folding, which consecutively updates the simulation force field to reflect marginal distributions of folding coordinates in the structure database and extract various energy terms. While the IsRNA model was validated by showing close agreement between the IsRNA-simulated and experimentally observed distributions, here, we expand our theoretical understanding of the model and, in doing so, improve the parameterization process to optimize the subset of included folding coordinates, which leads to accelerated simulations. Using statistical mechanical theory, we analyze the underlying, Bayesian concept that drives parameterization of the energy function, providing a general method for developing predictive, knowledge-based, polymer force fields on the basis of limited data. Furthermore, we propose an optimal parameterization procedure, based on the principal of maximum entropy.

The research of Travis Hurst was supported by NSF Graduate Research Fellowship Program under Grant 1443129.

The research of Shi-Jie Chen was supported by NIH Grants R35-GM134919 and R01-GM117059.

Received 2 November 2020