ClustENMD：在原子分辨率下高效采样生物分子构象空间。
ClustENMD: efficient sampling of biomolecular conformational space at atomic resolution.
机构信息
Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.
出版信息
Bioinformatics. 2021 Nov 5;37(21):3956-3958. doi: 10.1093/bioinformatics/btab496.
SUMMARY
Efficient sampling of conformational space is essential for elucidating functional/allosteric mechanisms of proteins and generating ensembles of conformers for docking applications. However, unbiased sampling is still a challenge especially for highly flexible and/or large systems. To address this challenge, we describe a new implementation of our computationally efficient algorithm ClustENMD that is integrated with ProDy and OpenMM softwares. This hybrid method performs iterative cycles of conformer generation using elastic network model for deformations along global modes, followed by clustering and short molecular dynamics simulations. ProDy framework enables full automation and analysis of generated conformers and visualization of their distributions in the essential subspace.
AVAILABILITY AND IMPLEMENTATION
ClustENMD is open-source and freely available under MIT License from https://github.com/prody/ProDy.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
为了阐明蛋白质的功能/变构机制并生成用于对接应用的构象集，高效地采样构象空间至关重要。然而，无偏采样仍然是一个挑战，特别是对于高度灵活和/或大型系统。为了解决这个挑战，我们描述了一种新的实现方法，即将我们计算效率高的算法 ClustENMD 与 ProDy 和 OpenMM 软件集成。这种混合方法使用弹性网络模型沿全局模式进行构象生成的迭代循环，然后进行聚类和短分子动力学模拟。ProDy 框架能够完全自动化生成构象的分析，并在重要子空间中可视化它们的分布。
可用性和实现
ClustENMD 是开源的，根据麻省理工学院的许可协议（MIT License）可在 https://github.com/prody/ProDy 上免费获得。
补充信息
补充数据可在 Bioinformatics 在线获得。
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