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• W2100981947 endingPage "287" @default.
• W2100981947 startingPage "271" @default.
• W2100981947 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." @default.
• W2100981947 created "2016-06-24" @default.
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• W2100981947 date "2000-01-01" @default.
• W2100981947 modified "2023-10-17" @default.
• W2100981947 title "Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks" @default.
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• W2100981947 doi "https://doi.org/10.1002/1097-0134(20001115)41:3<271::aid-prot10>3.0.co;2-z" @default.
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