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. β-turns are important components of the secondary structure of a protein. Development of an accurate method of prediction of β-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 β-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-β-turn, respectively. In addition, we also used same method to predict the β-turn types in three databases of 426, 547 and 823 protein chains and found that our prediction results were better than other predictions.

► A database of 2805 protein chains has been constructed.
► The SVM algorithm has been used to predict the β-turn types.
► Used composite vector as the input parameter for SVM to predict the β-turn types.
► The independent test has been first used to predict the β-turn types.