Detecting intermediate protein conformations using algebraic topology

BMC Bioinformatics

Nurit Haspel¹  Dong Luo¹  Eduardo González² 

[1] Department of Computer Science, University of Massachusetts Boston;Department of Mathematics, University of Massachusetts Boston;

关键词: Algebraic topology;    Protein conformational sampling;    Clustering;    Protein structure;    Dimensionality reduction;   

DOI  :  10.1186/s12859-017-1918-z

来源: DOAJ

【 摘 要 】

Abstract Background Understanding protein structure and dynamics is essential for understanding their function. This is a challenging task due to the high complexity of the conformational landscapes of proteins and their rugged energy levels. In particular, it is important to detect highly populated regions which could correspond to intermediate structures or local minima. Results We present a hierarchical clustering and algebraic topology based method that detects regions of interest in protein conformational space. The method is based on several techniques. We use coarse grained protein conformational search, efficient robust dimensionality reduction and topological analysis via persistent homology as the main tools. We use two dimensionality reduction methods as well, robust Principal Component Analysis (PCA) and Isomap, to generate a reduced representation of the data while preserving most of the variance in the data. Conclusions Our hierarchical clustering method was able to produce compact, well separated clusters for all the tested examples.

【 授权许可】

Unknown