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近年来,结构生物学研究越来越注重生物大分子复合物的解析,因为许多重要生物学过程都离不开复合物的参与.溶液核磁共振是目前重要的结构解析方法之一.X射线小角散射(SAXS)作为一种新的结构生物学实验手段,近年来发展迅速.SAXS 能提供生物大分子复合物的较低分辨率结构信息,而核磁共振能解析复合物中各个亚基的原子分辨率结构.此外,通过核磁共振还能得到亚基之间的界面、取向以及距离信息.因此近年来通过计算机模拟,整合核磁共振和 SAXS 不同分辨率的结构信息,可以用来搭建生物大分子复合物的结构模型.该综述重点介绍这方面的研究进展.  相似文献   
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Molecular dynamics (MD) simulation has become a powerful tool to investigate the structurefunction relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets containing millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape (geometric clustering) and kinetics (kinetic clustering). In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, agglomerative algorithms (single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage), center-based algorithms (K-Means, K-Medoids, K-Centers, and APM), density-based algorithms (neighbor-based, DBSCAN, density-peaks, and Robust-DB), and spectral-based algorithms (PCCA and PCCA+). In particular, differences between geometric and kinetic clustering metrics will be discussed along with the performances of different clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algorithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.  相似文献   
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