Matching Protein b-Sheet Partners by Feedforward and Recurrent Neural Networks
Predicting the secondary structure (a-helices, j3sheets, coils) of proteins is an important step towards understanding their three dimensional conformations. Unlike a-helices that are built up from one contiguous region of the polypeptide chain, r-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 j3-sheets. The neural architectures predict whether two residues located at the center of two distant windows are paired or not in a r-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.