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2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) (2016)
Atlanta, GA, USA
Oct. 13, 2016 to Oct. 15, 2016
ISSN: 2473-4659
ISBN: 978-1-5090-4200-5
pp: 1
Nurit Haspel , Department of Computer Science, UMass Boston, MA 02125, USA
Dong Luo , Department of Computer Science, UMass Boston, MA 02125, USA
Eduardo Gonzalez , Department of Mathematics, UMass Boston, MA 02125, USA
ABSTRACT
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. 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. Our hierarchical clustering method was able to produce compact, well separated clusters for all the tested examples.
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CITATION

N. Haspel, Dong Luo and E. Gonzalez, "Detecting intermediate protein conformations using algebraic topology," 2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Atlanta, GA, USA, 2016, pp. 1.
doi:10.1109/ICCABS.2016.7802779
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