Matching Protein / 3-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. Introduction Predicting the secondary structure (a-helices, B-sheets, 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 built up from a combination of several disjoint regions. Such regions, or B strands are typically 5-10 residues long. In the and Department of Biological Chemistry, College of Medicine, University of California, Irvine. To whom all corresl~ondence should be addressed. Copyright © 2000, American Association for Artificial Intelligence folded protein, these strands are aligned adjacent to each other in parallel or anti-parallel fashion. Hydrogen bonds can form between C’O groups of one strand and NH groups on the adjacent strand and vice versa with Ca atoms successively a little above or below the plane of the sheet. Hydrogen bonds between parallel and anti-parallel strands have distinctive patterns, but the exact nature and behavior of B-sheet long-ranged interactions is not clear. While the majority of sheets seems to consist of either parallel or antiparallel strands, mixed sheets are not uncommon. A B-strand can have 1 or 2 partner strands, and an individual amino acid can have 0,1 or 2 hydrogen bonds with one or two residues in a partner strand. Sometimes one or several partner-less residues are found in a strand, giving rise to the so-called Bbulges. Finally, B-strand partners are often located on a different protein chain. How amino acids located far apart in the sequence find one another to form B-sheets is still poorly understood, as is the degree of specificity between side-chain/side-chain interactions between residues on neighboring strands, which seems to be very weak (Wouters & Curmi, 1995). The presence of a turn between strands is also an essential ingredient. Partly as a result of the exponentially growing amount of available 3D data, machine learning methods have in general been among the most successful in secondary structure prediction (Baldi & Brunak, 1998). The best existing methods for predicting protein secondary structure, i.e. for classifying amino acids in a chain in one of the three classes, achieve prediction accuracy in the 75-77% range (Baldi et al., 1999; Cuff & Barton, 1999; Jones, 1999). Not surprisingly, ISMB2~O 25 From: ISMB-00 Proceedings. Copyright © 2000, AAAI (www.aaai.org). All rights reserved. fl-sheet is almost invariably the weakest category in terms of correct percentages, and it is never the highest scoring in terms of correlation coefficients. Therefore any improvement in 3-sheet prediction is significant as a stand-alone result, but also in relation to secondary and tertiary structure prediction methods in general. Here we design and train a neural network architecture for the prediction of amino acid partners in flsheets (see also (Hubbard, 1994; Zhu & Braun, 1999; Street & Mayo, 1999)).