利用实验数据和灵活正向模型精化分子动力学系综
Refinement of molecular dynamics ensembles using experimental data and flexible forward models
AI总结：
提出融合最大熵、贝叶斯系综推断与正向模型优化的方法，在利用核酸磁共振数据重加权RNA动力学系综时同步校正Karplus模型误差，并通过交叉验证和正则化获得可迁移参数。
摘要：
本文提出了一种结合最大熵原理、系综贝叶斯推断方法和经验正向模型优化的新方法。这里我们聚焦于RNA系统的Karplus参数，这些参数将γ、β二面角以及糖环中的二面角与偶合质子之间相应的³J偶合信号联系起来。我们对一组RNA四聚体和六聚体进行了大量分子模拟，并与可用的核酸磁共振数据相结合。在新框架内，采样的结构动力学可以被重新加权以匹配实验数据，同时由正向模型不准确性引起的误差可以被同步校正，因此不会泄漏到重新加权的系综中。精心设计的交叉验证程序和正则化项使获得可迁移的Karplus参数成为可能。我们的方法确定了最优正则化强度和新的Karplus参数集，在模拟与实验的良好一致性和对原始系综的最小改变之间实现平衡。
英文摘要：
A novel method combining maximum entropy principle, the Bayesian-inference of ensembles approach, and the optimization of empirical forward models is presented. Here we focus on the Karplus parameters for RNA systems, which relate the dihedral angles of $γ$, $β$, and the dihedrals in the sugar ring to the corresponding $^3J$-coupling signal between coupling protons. Extensive molecular simulations are performed on a set of RNA tetramers and hexamers and combined with available nucleic-magnetic-resonance data. Within the new framework, the sampled structural dynamics can be reweighted to match experimental data while the error arising from inaccuracies in the forward models can be corrected simultaneously and consequently does not leak into the reweighted ensemble. Carefully crafted cross-validation procedure and regularization terms enable obtaining transferable Karplus parameters. Our approach identifies the optimal regularization strength and new sets of Karplus parameters balancing good agreement between simulations and experiments with minimal changes to the original ensemble.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
