Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
J Chem Phys. 2013 Sep 7;139(9):090901. doi: 10.1063/1.4818908.
By focusing on essential features, while averaging over less important details, coarse-grained (CG) models provide significant computational and conceptual advantages with respect to more detailed models. Consequently, despite dramatic advances in computational methodologies and resources, CG models enjoy surging popularity and are becoming increasingly equal partners to atomically detailed models. This perspective surveys the rapidly developing landscape of CG models for biomolecular systems. In particular, this review seeks to provide a balanced, coherent, and unified presentation of several distinct approaches for developing CG models, including top-down, network-based, native-centric, knowledge-based, and bottom-up modeling strategies. The review summarizes their basic philosophies, theoretical foundations, typical applications, and recent developments. Additionally, the review identifies fundamental inter-relationships among the diverse approaches and discusses outstanding challenges in the field. When carefully applied and assessed, current CG models provide highly efficient means for investigating the biological consequences of basic physicochemical principles. Moreover, rigorous bottom-up approaches hold great promise for further improving the accuracy and scope of CG models for biomolecular systems.
通过关注基本特征，同时对不太重要的细节进行平均处理，粗粒化 (CG) 模型相对于更详细的模型具有显著的计算和概念优势。因此，尽管计算方法和资源取得了巨大的进展，但 CG 模型仍然越来越受欢迎，并且正在成为与原子细节模型同等重要的合作伙伴。本文综述了生物分子系统 CG 模型的快速发展。特别是，本综述试图提供几种不同 CG 模型开发方法的平衡、连贯和统一的表述，包括自上而下、基于网络、以天然结构为中心、基于知识和自下而上的建模策略。综述总结了它们的基本哲学、理论基础、典型应用和最新进展。此外，综述还确定了不同方法之间的基本关系，并讨论了该领域的突出挑战。当谨慎应用和评估时，当前的 CG 模型为研究基本物理化学原理的生物学后果提供了高效手段。此外，严格的自下而上方法为进一步提高生物分子系统 CG 模型的准确性和范围提供了巨大的潜力。
