Molecular and Cellular Biology, Stony Brook University, Stony Brook, New York 11794, United States.
Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States.
J Chem Theory Comput. 2022 Jun 14;18(6):3930-3947. doi: 10.1021/acs.jctc.2c00065. Epub 2022 May 3.
RNA is a key participant in many biological processes, but studies of RNA using computer simulations lag behind those of proteins, largely due to less-developed force fields and the slow dynamics of RNA. Generating converged RNA ensembles for force field development and other studies remains a challenge. In this study, we explore the ability of replica exchange molecular dynamics to obtain well-converged conformational ensembles for two RNA hairpin systems in an implicit solvent. Even for these small model systems, standard REMD remains computationally costly, but coupling to a pre-generated structure library using the reservoir REMD approach provides a dramatic acceleration of ensemble convergence for both model systems. Such precise ensembles could facilitate RNA force field development and validation and applications of simulation to more complex RNA systems. The advantages and remaining challenges of applying R-REMD to RNA are investigated in detail.
RNA 是许多生物过程的关键参与者，但使用计算机模拟研究 RNA 的进展落后于蛋白质，主要原因是力场的发展不够完善以及 RNA 的动态较慢。为了进行力场开发和其他研究，生成收敛的 RNA 集合仍然是一个挑战。在这项研究中，我们探索了 replica exchange 分子动力学在隐溶剂中获得两个 RNA 发夹系统良好收敛构象集合的能力。即使对于这些小型模型系统，标准 REMD 仍然计算成本高昂，但使用储库 REMD 方法与预先生成的结构库耦合，可以显著加速两个模型系统的集合收敛。这样精确的集合可以促进 RNA 力场的开发和验证，并将模拟应用于更复杂的 RNA 系统。详细研究了将 R-REMD 应用于 RNA 的优点和剩余挑战。
