
AbstractMotivation: Alternative structural models determined experimentally are available for an increasing number of proteins. Structural and functional studies of these proteins need to take these models into consideration as they can present considerable structural differences. The characterization of the structural differences and similarities between these models is a fundamental task in structural biology requiring appropriate methods.Results: We propose a method for characterizing sets of alternative structural models. Three types of analysis are performed: grouping according to structural similarity, visualization and detection of structural variation and comparison of subsets for identifying and locating distinct conformational states. The alpha carbon atoms are used in order to analyse the backbone conformations. Alternatively, side-chain atoms are used for detailed conformational analysis of specific sites. The method takes into account estimates of atom coordinate uncertainty. The invariant regions are used to generate optimal superpositions of these models. We present the results obtained for three proteins showing different degrees of conformational variability: relative motion of two structurally conserved subdomains, a disordered subdomain and flexibility in the functional site associated with ligand binding. The method has been applied in the analysis of the alternative models available in SCOP. Considerable structural variability can be observed for most proteins.Availability: The results of the analysis of the SCOP alternative models, the estimates of coordinate uncertainty as well as the source code of the implementation are available in the STRuster web site: http://struster.bioinf.mpi-inf.mpg.de.Contact: doming@mpi-sb.mpg.deSupplementary information: Supplementary data are available at Bioinformatics online.
Models, Molecular, Models, Chemical, Protein Conformation, Sequence Analysis, Protein, Molecular Sequence Data, Computer Simulation, Amino Acid Sequence, Algorithms, Pattern Recognition, Automated
Models, Molecular, Models, Chemical, Protein Conformation, Sequence Analysis, Protein, Molecular Sequence Data, Computer Simulation, Amino Acid Sequence, Algorithms, Pattern Recognition, Automated
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