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Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks
Author(s) -
de Brevern A.G.,
Etchebest C.,
Hazout S.
Publication year - 2000
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/1097-0134(20001115)41:3<271::aid-prot10>3.0.co;2-z
Subject(s) - sequence (biology) , computer science , probabilistic logic , block (permutation group theory) , bayesian probability , protein structure prediction , algorithm , protein sequencing , protein structure , data mining , artificial intelligence , mathematics , peptide sequence , chemistry , combinatorics , biochemistry , gene
By using an unsupervised cluster analyzer, we have identified a local structural alphabet composed of 16 folding patterns of five consecutive C α (“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 (α/β 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. Proteins 2000;41:271–287. © 2000 Wiley‐Liss, Inc.