γ-Turn types prediction in proteins using the support vector machines

Recently, two different models have been developed for predicting γ-turns in proteins by Kaur and Raghava [2002. An evaluation of β-turn prediction methods. Bioinformatics 18, 1508–1514; 2003. A neural-network based method for prediction of γ-turns in proteins from multiple sequence alignment. Protein Sci. 12, 923–929]. However, the major limitation of previous methods is inability in predicting γ-turns types. Thus, there is a need to predict γ-turn types using an approach which will be useful in overall tertiary structure prediction. In this work, support vector machines (SVMs), a powerful model is proposed for predicting γ-turn types in proteins. The high rates of prediction accuracy showed that the formation of γ-turn types is evidently correlated with the sequence of tripeptides, and hence can be approximately predicted based on the sequence information of the tripeptides alone.