Beta-sheet Prediction Using Interstrand Residue Pairs and Refinement with Hopfield Neural Network

Proceedings Of The Fifth International Conference On Intelligent Systems For Molecular Biology

Volume

Proceedings Of The Fifth International Conference On Intelligent Systems For Molecular Biology

Contents

Many secondary prediction methods have been studied, but the prediction accuracy is still unsatisfactory, since beta-sheet prediction is difficult. In this research, we gathered statistics of pairs of three residue sub-sequences in beta-sheets, calculated propensities for them. When a sequence is given, all possible three residue sub-sequences are examined whether they form beta-sheets. A shortcoming is that many false predictions are made. To exclude false predictions and improve the prediction, we employed a Hopfiel Neural Network, in which the natural limitations on protein tertiary structure and preference of chemically stable long beta-sheet are expressed in a form of energy functions. To clarify the prediction for heads and tails of beta-sheets, special variables are introduced, which are similar to the line process proposed by Geman.