Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany; Physics Department of Trento University, Povo (Trento), Italy.
SISSA - International School for Advanced Studies, Trieste, Italy.
Biophys J. 2023 Aug 8;122(15):3089-3098. doi: 10.1016/j.bpj.2023.06.012. Epub 2023 Jun 24.
Atomically detailed simulations of RNA folding have proven very challenging in view of the difficulties of developing realistic force fields and the intrinsic computational complexity of sampling rare conformational transitions. As a step forward in tackling these issues, we extend to RNA an enhanced path-sampling method previously successfully applied to proteins. In this scheme, the information about the RNA's native structure is harnessed by a soft history-dependent biasing force promoting the generation of productive folding trajectories in an all-atom force field with explicit solvent. A rigorous variational principle is then applied to minimize the effect of the bias. Here, we report on an application of this method to RNA molecules from 20 to 47 nucleotides long and increasing topological complexity. By comparison with analog simulations performed on small proteins with similar size and architecture, we show that the RNA folding landscape is significantly more frustrated, even for relatively small chains with a simple topology. The predicted RNA folding mechanisms are found to be consistent with the available experiments and some of the existing coarse-grained models. Due to its computational performance, this scheme provides a promising platform to efficiently gather atomistic RNA folding trajectories, thus retain the information about the chemical composition of the sequence.
鉴于开发真实力场的困难和采样稀有构象转变的内在计算复杂性，原子级详细的 RNA 折叠模拟一直极具挑战性。作为解决这些问题的一个步骤，我们将一种以前成功应用于蛋白质的增强路径采样方法扩展到 RNA 中。在该方案中，通过软历史相关的偏置力利用 RNA 天然结构的信息，该偏置力促进在具有显式溶剂的全原子力场中生成有生产性的折叠轨迹。然后应用严格的变分原理来最小化偏差的影响。在这里，我们报告了该方法在 20 到 47 个核苷酸长度且拓扑复杂度不断增加的 RNA 分子上的应用。通过与具有相似大小和结构的小型蛋白质进行的类似模拟进行比较，我们表明 RNA 折叠景观的阻碍性要大得多，即使对于拓扑结构简单的相对较小的链也是如此。预测的 RNA 折叠机制与可用的实验和一些现有的粗粒模型一致。由于其计算性能，该方案为高效收集原子 RNA 折叠轨迹提供了一个有前途的平台，从而保留了序列化学组成的信息。
