Prediction of β-turn types in protein by using composite vector

Protein secondary structure prediction is an intermediate step in the overall process of tertiary structure prediction. beta-turns are important components of the secondary structure of a protein. Development of an accurate method of prediction of beta-turn types would be helpful for predicting the overall tertiary structure of proteins. In this work, we constructed a database of 2805 protein chains. Our work improved the previous input parameters and used the support vector machine algorithm to predict the beta-turn types: we obtained the overall prediction accuracy of 98.1%, 96.0%, 96.1%, 98.7%, 99.1%, 86.8%, 99.2% and 73.2% with the Matthews Correlation Coefficient values of 0.398, 0.460, 0.043, 0.463, 0.355, 0.172, 0.109 and 0.247, respectively, for types I, II, VIII, I', II', IV, VI and non-beta-turn, respectively. In addition, we also used same method to predict the beta-turn types in three databases of 426, 547 and 823 protein chains and found that our prediction results were better than other predictions. (C) 2011 Elsevier Ltd. All rights reserved.