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

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.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.Protein dynamics plays a central role in cell life. In many cases biological function involves molecular motion [1] and it was recently suggested that intrinsic dynamics also defines the ability of proteins to adapt and evolve new functions [2]. Therefore, a full understanding of protein function and evolution will require a deeper insight into biomolecular atomistic dynamics.Significant contributions in this direction have come from computational methods, in particular from Molecular dynamics (MD) simulations [3,4], by which a large ensemble of molecular structures can be generated to sample the accessible conformational space of a protein. Analysis of this ensemble ca