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Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks.

Abstract : By using an unsupervised cluster analyzer, we have identified a local structural alphabet composed of 16 folding patterns of five consecutive C(alpha) ("protein blocks"). The dependence that exists between successive blocks is explicitly taken into account. A Bayesian approach based on the relation protein block-amino acid propensity is used for prediction and leads to a success rate close to 35%. Sharing sequence windows associated with certain blocks into "sequence families" improves the prediction accuracy by 6%. This prediction accuracy exceeds 75% when keeping the first four predicted protein blocks at each site of the protein. In addition, two different strategies are proposed: the first one defines the number of protein blocks in each site needed for respecting a user-fixed prediction accuracy, and alternatively, the second one defines the different protein sites to be predicted with a user-fixed number of blocks and a chosen accuracy. This last strategy applied to the ubiquitin conjugating enzyme (alpha/beta protein) shows that 91% of the sites may be predicted with a prediction accuracy larger than 77% considering only three blocks per site. The prediction strategies proposed improve our knowledge about sequence-structure dependence and should be very useful in ab initio protein modelling.
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Contributor : Alexandre G. de Brevern <>
Submitted on : Friday, September 4, 2009 - 8:54:29 AM
Last modification on : Tuesday, November 3, 2020 - 11:18:02 AM
Long-term archiving on: : Wednesday, April 7, 2010 - 12:30:36 AM


  • HAL Id : inserm-00132821, version 1
  • PUBMED : 11025540


Alexandre de Brevern, Catherine Etchebest, Serge Hazout. Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks.. Proteins - Structure, Function and Bioinformatics, Wiley, 2000, 41 (3), pp.271-87. ⟨inserm-00132821⟩



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