Self Organizing Maps to efficiently cluster and functionally interpret protein conformational ensembles

Domenico Fraccalvieri
(Department of Earth and Environmental Sciences, University of Milano Bicocca, Milano IT)
Laura Bonati
(Department of Earth and Environmental Sciences, University of Milano Bicocca, Milano IT)
Fabio Stella
(Department of Informatics, Systems and Communication, University of Milano Bicocca, Milano IT)

An approach that combines Self-Organizing maps, hierarchical clustering and network components is presented, aimed at comparing protein conformational ensembles obtained from multiple Molecular Dynamic simulations. As a first result the original ensembles can be summarized by using only the representative conformations of the clusters obtained. In addition the network components analysis allows to discover and interpret the dynamic behavior of the conformations won by each neuron. The results showed the ability of this approach to efficiently derive a functional interpretation of the protein dynamics described by the original conformational ensemble, highlighting its potential as a support for protein engineering.

In Alex Graudenzi, Giulio Caravagna, Giancarlo Mauri and Marco Antoniotti: Proceedings Wivace 2013 - Italian Workshop on Artificial Life and Evolutionary Computation (Wivace 2013), Milan, Italy, July 1-2, 2013, Electronic Proceedings in Theoretical Computer Science 130, pp. 83–86.
Published: 30th September 2013.

ArXived at: http://dx.doi.org/10.4204/EPTCS.130.13 bibtex PDF
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