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

Autor: A.G. de Brevern, S. Hazout, Catherine Etchebest
Rok vydání: 2000
Předmět:
Zdroj: Proteins: Structure, Function, and Genetics. 41:271-287
ISSN: 1097-0134
0887-3585
DOI: 10.1002/1097-0134(20001115)41:3<271::aid-prot10>3.0.co;2-z
Popis: By using an unsupervised cluster analyzer, we have identified a local structural alpha- bet composed of 16 folding patterns of five consecu- tive C a ("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 accu- racy exceeds 75% when keeping the first four pre- dicted 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 predic- tion 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 (a/b protein) shows that 91% of the sites may be predicted with a prediction accu- racy larger than 77% considering only three blocks per site. The prediction strategies proposed im- prove our knowledge about sequence-structure de- pendence and should be very useful in ab initio protein modelling. Proteins 2000;41:271-287.
Databáze: OpenAIRE
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