Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, 37235 Tennessee, United States.
Institut for Drug Discovery, Leipzig University, Leipzig SAC 04103, Germany.
J Chem Theory Comput. 2021 Jan 12;17(1):560-570. doi: 10.1021/acs.jctc.0c00836. Epub 2020 Dec 29.
construction of loop regions is an important problem in computational structural biology. Compared to regions with well-defined secondary structure, loops tend to exhibit significant conformational heterogeneity. As a result, their structures are often ambiguous when determined using experimental data obtained by crystallography, cryo-EM, or NMR. Although structurally diverse models could provide a more relevant representation of proteins in their native states, obtaining large numbers of biophysically realistic and physiologically relevant loop conformations is a resource-consuming task. To address this need, we developed a novel loop construction algorithm, Hash/RCD, that combines knowledge-based conformational hashing with random coordinate descent (RCD). This hybrid approach achieved a closure rate of 100% on a benchmark set of 195 loops in 29 proteins that range from 3 to 31 residues. More importantly, the use of templates allows Hash/RCD to maintain the accuracy of state-of-the-art coordinate descent methods while reducing sampling time from over 400 to 141 ms. These results highlight how the integration of coordinate descent with knowledge-based sampling overcomes barriers inherent to either approach in isolation. This method may facilitate the identification of native-like loop conformations using experimental data or full-atom scoring functions by allowing rapid sampling of large numbers of loops. In this manuscript, we investigate and discuss the advantages, bottlenecks, and limitations of combining conformational hashing with RCD. By providing a detailed technical description of the Hash/RCD algorithm, we hope to facilitate its implementation by other researchers.
环区的构建是计算结构生物学中的一个重要问题。与具有明确二级结构的区域相比，环区往往表现出显著的构象异质性。因此，当使用晶体学、低温电子显微镜或 NMR 获得的实验数据来确定它们的结构时，它们的结构往往是不明确的。尽管结构多样的模型可以更准确地表示蛋白质在其自然状态下的结构，但获得大量具有生物物理真实性和生理相关性的环构象是一项资源密集型任务。为了解决这一需求，我们开发了一种新的环构建算法，Hash/RCD，它将基于知识的构象哈希与随机坐标下降（RCD）相结合。这种混合方法在一个由 29 种蛋白质中的 195 个环组成的基准集中实现了 100%的封闭率，这些环的长度从 3 到 31 个残基不等。更重要的是，模板的使用允许 Hash/RCD 在保持最先进的坐标下降方法准确性的同时，将采样时间从 400 多毫秒减少到 141 毫秒。这些结果突出了坐标下降与基于知识的采样相结合如何克服两种方法各自存在的固有障碍。这种方法可以通过允许快速采样大量的环，使用实验数据或全原子评分函数来识别天然样环构象。在本文中，我们研究并讨论了将构象哈希与 RCD 相结合的优点、瓶颈和限制。通过提供 Hash/RCD 算法的详细技术描述，我们希望为其他研究人员提供实现它的便利。
