Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
J Comput Chem. 2011 Jan 30;32(2):196-209. doi: 10.1002/jcc.21564.
There is an increasing dataset of solved biomolecular structures in more than one conformation and increasing evidence that large-scale conformational change is critical for biomolecular function. In this article, we present our implementation of a dynamic importance sampling (DIMS) algorithm that is directed toward improving our understanding of important intermediate states between experimentally defined starting and ending points. This complements traditional molecular dynamics methods where most of the sampling time is spent in the stable free energy wells defined by these initial and final points. As such, the algorithm creates a candidate set of transitions that provide insights for the much slower and probably most important, functionally relevant degrees of freedom. The method is implemented in the program CHARMM and is tested on six systems of growing size and complexity. These systems, the folding of Protein A and of Protein G, the conformational changes in the calcium sensor S100A6, the glucose-galactose-binding protein, maltodextrin, and lactoferrin, are also compared against other approaches that have been suggested in the literature. The results suggest good sampling on a diverse set of intermediates for all six systems with an ability to control the bias and thus to sample distributions of trajectories for the analysis of intermediate states.
越来越多的生物分子结构具有多种构象，越来越多的证据表明，大规模构象变化对于生物分子功能至关重要。在本文中，我们介绍了我们实现的一种动态重要性抽样（DIMS）算法，该算法旨在增进我们对实验定义的起始和结束点之间重要中间状态的理解。这补充了传统的分子动力学方法，在传统的分子动力学方法中，大部分采样时间都花在由这些初始和最终点定义的稳定自由能阱中。因此，该算法创建了一组候选跃迁，为更慢且可能更重要的、与功能相关的自由度提供了深入的见解。该方法在 CHARMM 程序中实现，并在六个规模和复杂度不断增加的系统上进行了测试。这些系统包括蛋白 A 和蛋白 G 的折叠、钙传感器 S100A6 的构象变化、葡萄糖-半乳糖结合蛋白、麦芽糖、乳铁蛋白，还与文献中提出的其他方法进行了比较。结果表明，对于所有六个系统，在一组多样化的中间状态上进行了良好的采样，并且能够控制偏差，从而对轨迹分布进行采样，以分析中间状态。
