Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information

Abstract EN

DNA-binding proteins are fundamentally important in understanding cellular processes.

Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design.

We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information.

The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature.

The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes.

These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity.

The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively.

Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement.

In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.