Beta-sheet prediction using inter-strand residue pairs and refinement with Hopfield neural network.
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 short coming is that many false predictions are made. To exclude false predictions and improve the prediction, we employed a Hopfield 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.
Asogawa
M
M
C&C Research Laboratories, NEC Miyamae, Kanagawa, Japan. asogawa@csl.cl.nec.co.jp
D016208:Databases, Factual; D008433:Mathematics; D008956:Models, Chemical; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D013816:Thermodynamics