en 30800250 <p>Background: Nucleic acid-binding proteins play major roles in different biological processes, such as transcription, splicing and translation. Therefore, the nucleic acid-binding 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.<br /> 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 cross-validation process was used to measure the performance of employed classifiers.<br /> 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.<br /> 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.</p> DNA-binding proteins, Machine-learning algorithms, RNA-binding proteins 104 111 https://www.ajmb.org/En/Article.aspx?id=10341 https://www.ajmb.org/PDF/En/FullText/10341.pdf MehdiPoursheikhali AsghariDepartment of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran11351ParvizAbdolmalekiDepartment of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran533