A Hidden Markov Model applied to the protein 3D structure analysis

Authors:
L. Regad;F. Guyon;J. Maupetit;P. Tufféry;A. C. Camproux
Affiliations:
Equipe de Bioinformatique Génomique et Moléculaire, UMR-S726 Université Paris 7, case 7113, 2 place Jussieu, 75251 Paris, France;Equipe de Bioinformatique Génomique et Moléculaire, UMR-S726 Université Paris 7, case 7113, 2 place Jussieu, 75251 Paris, France;Equipe de Bioinformatique Génomique et Moléculaire, UMR-S726 Université Paris 7, case 7113, 2 place Jussieu, 75251 Paris, France;Equipe de Bioinformatique Génomique et Moléculaire, UMR-S726 Université Paris 7, case 7113, 2 place Jussieu, 75251 Paris, France;Equipe de Bioinformatique Génomique et Moléculaire, UMR-S726 Université Paris 7, case 7113, 2 place Jussieu, 75251 Paris, France
Venue:
Computational Statistics & Data Analysis
Year:
2008

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Abstract

Understanding and predicting protein structures depend on the complexity and the accuracy of the models used to represent them. A Hidden Markov Model has been set up to optimally compress 3D conformation of proteins into a structural alphabet (SA), corresponding to a library of limited and representative SA-letters. Each SA-letter corresponds to a set of short local fragments of four C"@a similar both in terms of geometry and in the way in which these fragments are concatenated in order to make a protein. The discretization of protein backbone local conformation as series of SA-letters results on a simplification of protein 3D coordinates into a unique 1D representation. Some evidence is presented that such approach can constitute a very relevant way to analyze protein architecture in particular for protein structure comparison or prediction.