Suppr超能文献

使用加速分子动力学研究生物分子的功能动力学。

Studying functional dynamics in bio-molecules using accelerated molecular dynamics.

机构信息

Department of Chemistry and Biochemistry, University of California-San Diego, 9500 Gilman Drive, Urey Hall, La Jolla, California 92003-0365, USA.

出版信息

Phys Chem Chem Phys. 2011 Dec 7;13(45):20053-65. doi: 10.1039/c1cp22100k. Epub 2011 Oct 21.

Abstract

Many biologically important processes such as enzyme catalysis, signal transduction, ligand binding and allosteric regulation occur on the micro- to millisecond time-scale. Despite the sustained and rapid increase in available computational power and the development of efficient simulation algorithms, molecular dynamics (MD) simulations of proteins and bio-machines are generally limited to time-scales of tens to hundreds of nano-seconds. In this perspective article we present a comprehensive review of Accelerated Molecular Dynamics (AMD), an extended biased potential molecular dynamics approach that allows for the efficient study of bio-molecular systems up to time-scales several orders of magnitude greater than those accessible using standard classical MD methods, whilst still maintaining a fully atomistic representation of the system. Compared to many other approaches, AMD affords efficient enhanced conformational space sampling without any a priori understanding of the underlying free energy surface, nor does it require the specific prior definition of a reaction coordinate or a set of collective variables. Successful applications of the AMD method, including the study of slow time-scale functional dynamics in folded proteins and the conformational behavior of natively unstructured proteins are discussed and an outline of the different variants and extensions to the standard AMD approach is presented.

摘要

许多重要的生物过程,如酶催化、信号转导、配体结合和变构调节,都发生在微秒到毫秒的时间尺度上。尽管可用的计算能力持续快速增长,高效的模拟算法也在不断发展,但蛋白质和生物机器的分子动力学 (MD) 模拟通常限于几十到几百纳秒的时间尺度。在这篇观点文章中,我们全面回顾了加速分子动力学 (AMD),这是一种扩展的有偏势分子动力学方法,允许在比使用标准经典 MD 方法可达到的时间尺度大几个数量级的范围内高效地研究生物分子系统,同时仍然保持对系统的全原子表示。与许多其他方法相比,AMD 提供了有效的增强构象空间采样,而无需对潜在的自由能表面有任何先验的了解,也不需要对反应坐标或一组集体变量进行特定的预先定义。AMD 方法的成功应用,包括折叠蛋白质中慢时间尺度功能动力学的研究和天然无结构蛋白质的构象行为的研究,都进行了讨论,并概述了标准 AMD 方法的不同变体和扩展。

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