Matching Protein -Sheet Partners by Neural Networks
Predicting the secondary structure ( -helices, sheets, coils) of proteins is an important step towards understanding their three dimensional conformations. Unlike -helices that are built up from one contiguous region of the polypeptide chain, -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 antiparallel -sheets. The neural architectures predict whether two residues located at the center of two distant windows are paired or not in a -sheet structure. Variations on these architecture, including also pro les and ensembles, are trained and tested via ve-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.