Sangam: A Confluence of Knowledge Streams

Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps

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dc.creator Fraccalvieri, D
dc.creator Pandini, A
dc.creator Stella, F
dc.creator Bonati, L
dc.date 2015-01-30T15:31:40Z
dc.date 2011-05-14
dc.date 2015-01-30T15:31:40Z
dc.date 2011
dc.date.accessioned 2022-05-25T14:53:41Z
dc.date.available 2022-05-25T14:53:41Z
dc.identifier BMC Bioinformatics, 12:158, 2011
dc.identifier 1471-2105
dc.identifier http://www.biomedcentral.com/1471-2105/12/158
dc.identifier http://bura.brunel.ac.uk/handle/2438/10036
dc.identifier http://dx.doi.org/10.1186/1471-2105-12-158
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/172690
dc.description Background: Molecular dynamics (MD) simulations are powerful tools to investigate the conformational dynamics of proteins that is often a critical element of their function. Identification of functionally relevant conformations is generally done clustering the large ensemble of structures that are generated. Recently, Self-Organising Maps (SOMs) were reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data mining problems. We present a novel strategy to analyse and compare conformational ensembles of protein domains using a two-level approach that combines SOMs and hierarchical clustering.Results: The conformational dynamics of the α-spectrin SH3 protein domain and six single mutants were analysed by MD simulations. The Cα's Cartesian coordinates of conformations sampled in the essential space were used as input data vectors for SOM training, then complete linkage clustering was performed on the SOM prototype vectors. A specific protocol to optimize a SOM for structural ensembles was proposed: the optimal SOM was selected by means of a Taguchi experimental design plan applied to different data sets, and the optimal sampling rate of the MD trajectory was selected. The proposed two-level approach was applied to single trajectories of the SH3 domain independently as well as to groups of them at the same time. The results demonstrated the potential of this approach in the analysis of large ensembles of molecular structures: the possibility of producing a topological mapping of the conformational space in a simple 2D visualisation, as well as of effectively highlighting differences in the conformational dynamics directly related to biological functions.Conclusions: The use of a two-level approach combining SOMs and hierarchical clustering for conformational analysis of structural ensembles of proteins was proposed. It can easily be extended to other study cases and to conformational ensembles from other sources. © 2011 Fraccalvieri et al; licensee BioMed Central Ltd.
dc.language eng
dc.language en
dc.relation BMC Bioinformatics
dc.relation BMC Bioinformatics
dc.subject Molecular dynamics (MD)
dc.subject Proteins
dc.title Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps
dc.type Article


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