Suppr超能文献

利用元动力学模拟对接触图空间中的蛋白质-蛋白质复合物进行精修。

Refinement of protein-protein complexes in contact map space with metadynamics simulations.

机构信息

Biomolecular Modelling Laboratory, The Francis Crick Institute, London, United Kingdom.

出版信息

Proteins. 2019 Jan;87(1):12-22. doi: 10.1002/prot.25612. Epub 2018 Oct 30.

Abstract

Accurate protein-protein complex prediction, to atomic detail, is a challenging problem. For flexible docking cases, current state-of-the-art docking methods are limited in their ability to exhaustively search the high dimensionality of the problem space. In this study, to obtain more accurate models, an investigation into the local optimization of initial docked solutions is presented with respect to a reference crystal structure. We show how physics-based refinement of protein-protein complexes in contact map space (CMS), within a metadynamics protocol, can be performed. The method uses 5 times replicated 10 ns simulations for sampling and ranks the generated conformational snapshots with ZRANK to identify an ensemble of n snapshots for final model building. Furthermore, we investigated whether the reconstructed free energy surface (FES), or a combination of both FES and ZRANK, referred to as CS , can help to reduce snapshot ranking error.

摘要

准确预测蛋白质-蛋白质复合物,达到原子细节水平,是一个具有挑战性的问题。对于柔性对接情况,当前最先进的对接方法在穷尽搜索问题空间的高维度方面能力有限。在这项研究中,为了获得更准确的模型,针对参考晶体结构,对初始对接解决方案的局部优化进行了研究。我们展示了如何在元动力学协议内,通过接触映射空间 (CMS) 中的基于物理的蛋白质-蛋白质复合物细化,来实现这一点。该方法使用 5 次重复的 10 ns 模拟进行采样,并使用 ZRANK 对生成的构象快照进行排序,以识别 n 个快照的集合用于最终模型构建。此外,我们还研究了重建的自由能表面 (FES),或者 FES 和 ZRANK 的组合,称为 CS,是否有助于减少快照排序错误。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验