Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks. - Inserm - Institut national de la santé et de la recherche médicale Access content directly
Journal Articles Proteins - Structure, Function and Bioinformatics Year : 2000

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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Dates and versions

inserm-00132821 , version 1 (04-09-2009)

Identifiers

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

Cite

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