Abstract
BackgroundUnderstanding 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.ResultsWe 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.ConclusionsOur hierarchical clustering method was able to produce compact, well separated clusters for all the tested examples.
Highlights
Understanding protein structure and dynamics is essential for understanding their function
When running spherical PCA (sphPCA) on the conformational spaces of all the proteins, the first three dimensions account for 90% or more of the variance in the data
We present a persistent homology and dimensionality reduction based hierarchical method to detect clusters of intermediate structures in the conformational spaces of proteins undergoing large-scale changes
Summary
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. It is important to detect highly populated regions which could correspond to intermediate structures or local minima. Conformational exploration methods aim to characterize the conformational space of proteins in order to find minimum energy regions corresponding to highly populated structures [1, 2]. These intermediate states are transient and hard to detect experimentally. They may be crucial to understanding dynamic events such
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