Predicting [beta]-turns in protein using kernel logistic regression.

A ß-turn is a secondary protein structure type that plays a significant role in protein configuration and function. On average 25% of amino acids in protein structures are located in ß-turns. It is very important to develope an accurate and efficient method for ß-turns prediction. Most of the current successful ß-turns prediction methods use support vector machines (SVMs) or neural networks (NNs). The kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems. However, it is often not found in ß-turns classification, mainly because it is computationally expensive. In this paper, we used KLR to obtain sparse ß-turns prediction in short evolution time. Secondary structure information and position-specific scoring matrices (PSSMs) are utilized as input features. We achieved Q total of 80.7% and MCC of 50% on BT426 dataset. These results show that KLR method with the right algorithm can yield performance equivalent to or even better than NNs and SVMs in ß-turns prediction. In addition, KLR yields probabilistic outcome and has a well-defined extension to multiclass case. [ABSTRACT FROM AUTHOR]