生物大分子多尺度理论和计算方法

分子模拟是研究生物大分子的重要手段. 过去二十年来, 人们将分子模拟与实验研究相结合, 揭示出生物大分子结构和动力学方面的诸多重要性质. 传统分子模拟主要采用全原子分子模型或各种粗粒化的分子模型. 在实际应用中, 传统分子模拟方法通常存在精度或效率瓶颈, 一定程度上限制了其应用范围. 近年来, 多尺度分子模型越来越受到人们的关注. 多尺度分子模型基于统计力学原理, 将全原子模型和粗粒化模型相耦合, 有望克服传统分子模拟方法中的精度/效率瓶颈, 进而拓展分子模拟在生物大分子研究中的应用范围. 根据模型之间的耦合方式, 近年来发展起来的多尺度分子模拟方法可归纳为如下四种类型: 混合分辨多尺度模型、并行耦合多尺度模型、单向耦合多尺度模型、以及自学习多尺度模型. 本文将对上述四类多尺度模型做简要介绍, 并讨论其主要优缺点、应用范围以及进一步发展方向.

Multiscale theory and computational method for biomolecule simulations

Molecular simulation is one of the most important ways of studying biomolecules. In the last two decades, by combining the molecular simulations with experiments, a number of key features of structure and dynamics of biomolecules have been reflealed. Traditional molecular simulations often use the all-atom model or some coarse grained models. In practical applications, however, these all-atom models and coarse grained models encounter the bottlenecks in accuracy and efficiency, respectively, which hinder their applications to some extent. In reflent years, the multiscale models have attracted much attention in the field of biomolecule simulations. In the multiscale model, the atomistic models and coarse grained models are combined together based on the principle of statistical physics, and thus the bottlenecks encountered in the traditional models can be overcome. The currently available multiscale models can be classified into four categories according to the coupling ways between the all-atom model and coarse gained model. They are 1) hybrid resolution multiscale model, 2) parallel coupling multiscale model, 3) one-way coupling multiscale model, and 4) self-learning multiscale model. All these multiscale strategies have achieved great success in certain aspects in the field of biomolecule simulations, including protein folding, aggregation, and functional motions of many kinds of protein machineries. In this review, we briefly introduce the above-mentioned four multiscale strategies, and the examples of their applications. We also discuss the limitations and advantages, as well as the application scopes of these multiscale methods. The directions for future work on improving these multiscale models are also suggested. Finally, a summary and some prospects are preflented.