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

L. Regad, F. Guyon, J. Maupetit, P. Tufféry and A.C. Camproux

Computational Statistics & Data Analysis, 2008, vol. 52, issue 6, 3198-3207

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[alpha] 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.

Date: 2008
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