Predicting β-Turns in Protein Using Kernel Logistic Regression

الملخص EN

A β-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 β-turns.

It is very important to develope an accurate and efficient method for β-turns prediction.

Most of the current successful β-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 β-turns classification, mainly because it is computationally expensive.

In this paper, we used KLR to obtain sparse β-turns prediction in short evolution time.

Secondary structure information and position-specific scoring matrices (PSSMs) are utilized as input features.

We achieved Qtotal 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 β-turns prediction.

In addition, KLR yields probabilistic outcome and has a well-defined extension to multiclass case.