prediction of rna- and dna-binding proteins using various machine learning classifiers

Background: nucleic acid-binding proteins play major roles in different biological processes, such as transcription, splicing and translation. therefore, the nucleic acidbinding function prediction of proteins is a step toward full functional annotation of proteins. the aim of our research was the improvement of nucleic-acid binding function prediction. methods: in the current study, nine machine-learning algorithms were used to predict rna- and dna-binding proteins and also to discriminate between rna-binding proteins and dna-binding proteins. the electrostatic features were utilized for prediction of each function in corresponding adapted protein datasets. the leave-one-out crossvalidation process was used to measure the performance of employed classifiers. results: radial basis function classifier gave the best results in predicting rna- and dna-binding proteins in comparison with other classifiers applied. in discriminating between rna- and dna-binding proteins, multilayer perceptron classifier was the best one. conclusion: our findings show that the prediction of nucleic acid-binding function based on these simple electrostatic features can be improved by applied classifiers. moreover, a reasonable progress to distinguish between rna- and dna-binding proteins has been achieved.