2011
DOI: 10.1186/1471-2105-12-158
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Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps

Abstract: BackgroundMolecular 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… Show more

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Cited by 47 publications
(52 citation statements)
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“…1000 sets of loop models were generated; then the ensemble of models of each loop was clustered on the basis of the backbone structural similarity by using the Self Organizing Map (SOM) approach previously described. 40 The models representing the cluster medoids were selected as representative of the conformational variability of that loop.…”
Section: Methodsmentioning
confidence: 99%
“…1000 sets of loop models were generated; then the ensemble of models of each loop was clustered on the basis of the backbone structural similarity by using the Self Organizing Map (SOM) approach previously described. 40 The models representing the cluster medoids were selected as representative of the conformational variability of that loop.…”
Section: Methodsmentioning
confidence: 99%
“…When performing geometrical clustering on MD simulations, we assume that conformations grouped into the same cluster are structurally similar, and therefore lie within the same basin on the freeenergy landscape of the protein. 116 Clusters are typically different sizes, due to conformational sampling progressing according to a Boltzmann distribution during simulation. Sparsely populated clusters are more likely to represent transient and/or higher energy states.…”
Section: Simulating Phosphorylated Receiver Domainsmentioning
confidence: 99%
“…Analysis of RMSF values aims to observe the flexibility of proteins occurring in each amino acid residue during molecular dynamics simulations. 20 We focused on the amino acid residues at the active site of ACE, including Gln281, His353, Ala354, Glu384, Glu411, Asp415, Asp453, Lys454, Lys511, His513, Tyr520, and Tyr523. Based on the graphs in Figure 4, it demonstrated that at temperatures of 300 K and 310 K all ligands that bonded with amino acid residues at the active sites of ACE did not exhibit a high RMSF value, so ligands were able to form stable conformation at the active site of ACE.…”
Section: S35mentioning
confidence: 99%