Spatial Clustering of Molecular Dynamics Trajectories in Protein Unfolding Simulations

Authors:
Pedro Gabriel Ferreira;Cândida G. Silva;Paulo J. Azevedo;Rui M. Brito
Affiliations:
Genome Bioinformatics Laboratory, Center for Genomic Regulation, Barcelona, Spain;Chemistry Department, Faculty of Science and Technology, University of Coimbra, Coimbra, Portugal and Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal;Department of Informatics, CCTC, University of Minho, Braga, Portugal;Chemistry Department, Faculty of Science and Technology, University of Coimbra, Coimbra, Portugal and Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
Venue:
Computational Intelligence Methods for Bioinformatics and Biostatistics
Year:
2009

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Abstract

Molecular dynamics simulations is a valuable tool to study protein unfolding in silico . Analyzing the relative spatial position of the residues during the simulation may indicate which residues are essential in determining the protein structure. We present a method, inspired by a popular data mining technique called Frequent Itemset Mining, that clusters sets of amino acid residues with a synchronized trajectory during the unfolding process. The proposed approach has several advantages over traditional hierarchical clustering.