title: Matching protein beta-sheet partners by feedforward and recurrent neural networks.
abstract: Predicting the secondary structure (alpha-helices, beta-sheets, coils) of proteins is an important step towards understanding their three dimensional conformations. Unlike alpha-helices that are built up from one contiguous region of the polypeptide chain, beta-sheets are more complex resulting from a combination of two or more disjoint regions. The exact nature of these long distance interactions remains unclear. Here we introduce two neural-network based methods for the prediction of amino acid partners in parallel as well as anti-parallel beta-sheets. The neural architectures predict whether two residues located at the center of two distant windows are paired or not in a beta-sheet structure. Variations on these architecture, including also profiles and ensembles, are trained and tested via five-fold cross validation using a large corpus of curated data. Prediction on both coupled and non-coupled residues currently approaches 84% accuracy, better than any previously reported method.
authors.affiliation: Department of Information and Computer Science, University of California, Irvine 92697-3425, USA. pfbaldi@ics.uci.edu
authors.forename: P
authors.initials: P
authors.lastname: Baldi
authors.forename: G
authors.initials: G
authors.lastname: Pollastri
authors.forename: C A
authors.initials: CA
authors.lastname: Andersen
authors.forename: S
authors.initials: S
authors.lastname: Brunak
mesh_terms: D000818:Animals; D006801:Humans; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*
chemical_list: D011506:Proteins