An approach to efficiently cluster protein conformational ensembles from multiple MD simulations is presented. It is based on the combined use of Self-Organizing Maps and hierarchical clustering. In addition to the analysis of the representative conformation of each cluster, the application of the network components to the conformations in each neuron of the map is proposed. The results confirmed the ability of the approach to efficiently extract specific dynamic signatures related to biological function and highlighted the potential of this tool as a support for protein engineering.
Fraccalvieri, D., Bonati, L., Pandini, A., Stella, F. (2013). Functional interpretation of protein conformational ensembles using self-organizing maps and network components. In Proceedings.
Functional interpretation of protein conformational ensembles using self-organizing maps and network components
FRACCALVIERI, DOMENICO;BONATI, LAURA;STELLA, FABIO ANTONIO
2013
Abstract
An approach to efficiently cluster protein conformational ensembles from multiple MD simulations is presented. It is based on the combined use of Self-Organizing Maps and hierarchical clustering. In addition to the analysis of the representative conformation of each cluster, the application of the network components to the conformations in each neuron of the map is proposed. The results confirmed the ability of the approach to efficiently extract specific dynamic signatures related to biological function and highlighted the potential of this tool as a support for protein engineering.
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