title: 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.
authors.affiliation: Equipe de Bioinformatique Génomique et Moléculaire, INSERM U436, Université Paris 7, Paris, France. debrevern@urbb.jussieu.fr
authors.forename: A G
authors.initials: AG
authors.lastname: de Brevern
authors.forename: C
authors.initials: C
authors.lastname: Etchebest
authors.forename: S
authors.initials: S
authors.lastname: Hazout
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D016000:Cluster Analysis; D003198:Computer Simulation; D016208:Databases, Factual; D005544:Forecasting; D008025:Ligases; D008958:Models, Molecular; D016571:Neural Networks, Computer; D010446:Peptide Fragments / Q000737:chemistry* / Q000145:classification; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D014452:Ubiquitins / Q000378:metabolism
chemical_list: D010446:Peptide Fragments; D014452:Ubiquitins; D008025:Ligases