Enhanced prediction of DNA-binding proteins and classes

Abstract

Predicting DNA-binding proteins computationally based on proteins features is a very challenging process. This is due to the diversity of DNA-binding patterns and classes. Therefore, the accurate prediction of DNA-binding proteins and their classes is essential. This chapter proposes efficient protein feature representations for the prediction of DNA-binding proteins and their classes. The prediction results achieved are comparable or superior to previously published results using different benchmark datasets. A protein representation of sequence, psychochemical and structural features achieved accuracy improvement of about 7% on average for the prediction ofDNA-binding proteins.Moreover, a newly proposed structure-based protein representation that takes distance and angle patterns into accounts was evaluated for DNA-binding proteins prediction. It achieved when combined with other feature representations improvement in accuracy over previously published results about 7 and 12% on average for the prediction of DNA-binding proteins and DNAbinding protein classes, respectively. All results were evaluated using two classifiers, Random Forest and SVM.