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

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

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.

Suggested Citation

  • Regad, L. & Guyon, F. & Maupetit, J. & Tufféry, P. & Camproux, A.C., 2008. "A Hidden Markov Model applied to the protein 3D structure analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3198-3207, February.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:6:p:3198-3207
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    References listed on IDEAS

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    1. Bouveyron, C. & Girard, S. & Schmid, C., 2007. "High-dimensional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 502-519, September.
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    1. Edler, Lutz & Lee, Jae Won & Mittlböck, Martina & Niland, Joyce & Victor, Norbert, 2009. "Computational statistics within clinical research," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 583-585, January.

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