Biocomputing Unit, Centro Nacional de Biotecnología (CSIC), Calle Darwin 3, 28049 Madrid, Spain.
Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA 15213, USA.
Acta Crystallogr D Struct Biol. 2022 Apr 1;78(Pt 4):399-409. doi: 10.1107/S2059798322001966. Epub 2022 Mar 16.
Cryo-electron microscopy (cryoEM) has become a well established technique with the potential to produce structures of large and dynamic supramolecular complexes that are not amenable to traditional approaches for studying structure and dynamics. The size and low resolution of such molecular systems often make structural modelling and molecular dynamics simulations challenging and computationally expensive. This, together with the growing wealth of structural data arising from cryoEM and other structural biology methods, has driven a trend in the computational biophysics community towards the development of new pipelines for analysing global dynamics using coarse-grained models and methods. At the centre of this trend has been a return to elastic network models, normal mode analysis (NMA) and ensemble analyses such as principal component analysis, and the growth of hybrid simulation methodologies that make use of them. Here, this field is reviewed with a focus on ProDy, the Python application programming interface for protein dynamics, which has been developed over the last decade. Two key developments in this area are highlighted: (i) ensemble NMA towards extracting and comparing the signature dynamics of homologous structures, aided by the recent SignDy pipeline, and (ii) pseudoatom fitting for more efficient global dynamics analyses of large and low-resolution supramolecular assemblies from cryoEM, revisited in the CryoDy pipeline. It is believed that such a renewal and extension of old models and methods in new pipelines will be critical for driving the field forward into the next cryoEM revolution.
冷冻电子显微镜(cryoEM)已成为一种成熟的技术,具有产生大的和动态的超分子复合物结构的潜力,这些结构不适用于传统的结构和动力学研究方法。此类分子系统的大小和低分辨率通常使得结构建模和分子动力学模拟具有挑战性和计算成本高。再加上冷冻电镜和其他结构生物学方法产生的越来越多的结构数据,这促使计算生物物理学界开发了新的分析方法,使用粗粒度模型和方法来分析全局动力学。这一趋势的核心是回归弹性网络模型、正常模式分析(NMA)和主成分分析等整体分析方法,以及利用它们的混合模拟方法的发展。本文重点介绍了过去十年中开发的 Python 应用程序编程接口 ProDy,该接口用于蛋白质动力学。该领域的两个关键进展得到了强调:(i)通过最近的 SignDy 管道,对同源结构的特征动力学进行提取和比较的集合 NMA,以及(ii)用于从冷冻电镜中更有效地分析大的和低分辨率的超分子组装体的全局动力学的伪原子拟合,在 CryoDy 管道中进行了重新探讨。人们相信,在新的分析方法中对旧模型和方法的这种更新和扩展对于推动该领域进入下一个冷冻电镜革命至关重要。