Classification and prediction of β-turn types by neural network

Although a beta-turn consists of only four amino acids, it assumes many different types in proteins. Is this basically dependent on the tetrapeptide sequence alone or as a result of variety of interactions with the other part of a protein? To answer this question, T. Kohonen's self-organization Model which is one of the typical neural networks is proposed that can reflect the sequence-coupling effect of a tetrapeptide is not only a beta-turn or non-beta-turn, but also different types of a p-turn. There are 6028 beta-turn tetrapeptides of beta-turn types I (1227), I' (125), II(405), II'(89), VI(55), VIII(320), and non-beta-turns (3807) in the training database as constructed recently by Chou and Blinn(1997). Using these training data the rate of comet prediction by the neural network for a given protein: rubredoxin (54 residues, 51 tetrapeptides) which includes 12 beta-turn types I tetrapeptides, 1 beta-turn types II tetrapeptides and 38 non-beta-turns reaches 90.2%. The high quality of prediction of the neural network model implies that the formation of different p-turn types or non-p-turns is considerably correlated with the sequence of a tetrapeptide, (C) 1999 Elsevier Science Ltd. All rights reserved.