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mulPBA: an efficient multiple protein structure alignment method based on a structural alphabet.

Abstract : The increasing number of available protein structures requires efficient tools for multiple structure comparison. Indeed, multiple structural alignments are essential for the analysis of function, evolution and architecture of protein structures. For this purpose, we proposed a new web server called multiple Protein Block Alignment (mulPBA). This server implements a method based on a structural alphabet to describe the backbone conformation of a protein chain in terms of dihedral angles. This 'sequence-like' representation enables the use of powerful sequence alignment methods for primary structure comparison, followed by an iterative refinement of the structural superposition. This approach yields alignments superior to most of the rigid-body alignment methods and highly comparable with the flexible structure comparison approaches. We implement this method in a web server designed to do multiple structure superimpositions from a set of structures given by the user. Outputs are given as both sequence alignment and superposed 3D structures visualized directly by static images generated by PyMol or through a Jmol applet allowing dynamic interaction. Multiple global quality measures are given. Relatedness between structures is indicated by a distance dendogram. Superimposed structures in PDB format can be also downloaded, and the results are quickly obtained. mulPBA server can be accessed at .
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Contributor : Alexandre G. de Brevern Connect in order to contact the contributor
Submitted on : Tuesday, May 6, 2014 - 11:46:55 AM
Last modification on : Wednesday, October 27, 2021 - 2:01:02 PM
Long-term archiving on: : Wednesday, August 6, 2014 - 11:00:38 AM


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Sylvain Léonard, Agnel Praveen Joseph, Narayanaswamy Srinivasan, Jean-Christophe Gelly, Alexandre de Brevern. mulPBA: an efficient multiple protein structure alignment method based on a structural alphabet.. Journal of Biomolecular Structure and Dynamics, Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2014, 32 (4), pp.661-8. ⟨10.1080/07391102.2013.787026⟩. ⟨inserm-00926338⟩



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