The capability of current force fields to reproduce RNA structural dynamics is limited. Several methods have been developed to take advantage of experimental data in order to enforce agreement with experiments. Here, we extend an existing framework which allows arbitrarily chosen force-field correction terms to be fitted by quantification of the discrepancy between observables back-calculated from simulation and corresponding experiments. We apply a robust regularization protocol to avoid overfitting and additionally introduce and compare a number of different regularization strategies, namely, L1, L2, Kish size, relative Kish size, and relative entropy penalties. The training set includes a GACC tetramer as well as more challenging systems, namely, gcGAGAgc and gcUUCGgc RNA tetraloops. Specific intramolecular hydrogen bonds in the AMBER RNA force field are corrected with automatically determined parameters that we call gHBfix_opt. A validation involving a separate simulation of a system present in the training set (gcUUCGgc) and new systems not seen during training (CAAU and UUUU tetramers) displays improvements regarding the native population of the tetraloop as well as good agreement with NMR experiments for tetramers when using the new parameters. Then, we simulate folded RNAs (a kink–turn and L1 stalk rRNA) including hydrogen bond types not sufficiently present in the training set. This allows a final modification of the parameter set which is named gHBfix21 and is suggested to be applicable to a wider range of RNA systems.

中文翻译：

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AMBER RNA 力场中氢键固定的自动学习

当前力场再现 RNA 结构动力学的能力是有限的。已经开发了几种方法来利用实验数据来加强与实验的一致性。在这里，我们扩展了一个现有框架，该框架允许通过量化从模拟和相应实验中反算的可观察量之间的差异来拟合任意选择的力场校正项。我们应用了一个健壮的正则化协议来避免过度拟合，并另外引入和比较了许多不同的正则化策略，即 L1、L2、Kish 大小、相对 Kish 大小和相对熵惩罚。训练集包括 GACC 四聚体以及更具挑战性的系统，即 gcGAGAgc 和 gcUUCGgc RNA 四环。_选择。涉及对训练集中存在的系统 (gcUUCGgc) 和训练期间未见的新系统 (CAAU 和 UUUU 四聚体) 的单独模拟的验证显示了关于四环的天然种群的改进以及与四聚体的核磁共振实验的良好一致性。使用新参数。然后，我们模拟折叠的 RNA（扭结转角和 L1 茎 rRNA），包括训练集中不充分存在的氢键类型。这允许最终修改名为 gHBfix21 的参数集，并建议适用于更广泛的 RNA 系统。