Neural network prediction of 3(10)-helices in proteins.
Secondary structure prediction from the primary sequence of a protein is fundamental to understanding its structure and folding properties. Although several prediction methodologies are in vogue, their performances are far from being completely satisfactory. Among these, non-linear neural networks have been shown to be relatively effective, especially for predicting beta-turns, where dominant interactions are local, arising from four sequence-contiguous residues. Most 3(10)-helices in proteins are also short, comprising of three sequence-contiguous residues and two capping residues. In order to understand the extent of local interactions in these 3(10)-helices, we have applied a neural network model with varying window size to predict 3(10)-helices in proteins. We found the prediction accuracy of 3(10)-helices (approximately 14%), as judged by the Matthew's Correlation Coefficient, to be less than that of beta-turns (approximately 20%). The optimal window size for the prediction of 3(10)-helices was about 9 residues. The significance and implications of these results in understanding the occurrence of 3(10)-helices and preferences of amino acid residues in 3(10)-helices are discussed.