title: Prediction of the number of residue contacts in proteins.
abstract: Knowing the number of residue contacts in a protein is crucial for deriving constraints useful in modeling protein folding and/or scoring remote homology search. Here we focus on the prediction of residue contacts and show that this figure can be predicted with a neural network based method. The accuracy of the prediction is 12 percentage points higher than that of a simple statistical method. The neural network is used to discriminate between two different states of residue contacts, characterized by a contact number higher or lower than the average value of the residue distribution. When evolutionary information is taken into account, our method correctly predicts 69% of the residue states in the data base and it adds to the prediction of residue solvent accessibility. The predictor is available at htpp://www.biocomp.unibo.it
authors.affiliation: Dept. of Biology University of Bologna, Italy. Piero@biocomp.unibo.it
authors.forename: P
authors.initials: P
authors.lastname: Fariselli
authors.forename: R
authors.initials: R
authors.lastname: Casadio
mesh_terms: D000818:Animals; D016208:Databases, Factual; D019143:Evolution, Molecular; D006801:Humans; D011237:Predictive Value of Tests; D011506:Proteins / Q000737:chemistry / Q000145:classification* / Q000235:genetics*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins