Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naïve Bayes

• Published in: PloS one Vol. 9; no. 1; p. e86703
• Main Authors: Lou, Wangchao, Wang, Xiaoqing, Chen, Fan, Chen, Yixiao, Jiang, Bo, Zhang, Hua
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.01.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Amino acid sequence; Amino acids; Amino Acids - chemistry; Amino Acids - metabolism; Animals; Artificial intelligence; Basis functions; Bayes Theorem; Bayesian analysis; Bioinformatics; Biology; Classifiers; Computer Science; Datasets; Datasets as Topic; Decision trees; Deoxyribonucleic acid; DNA; DNA - chemistry; DNA - metabolism; DNA-binding protein; DNA-Binding Proteins - chemistry; DNA-Binding Proteins - metabolism; Engineering; Forest management; Functions (mathematics); Gene expression; Gene regulation; Humans; Mathematical analysis; Mathematics; Methods; Neural networks; Normal Distribution; Nucleotide sequence; Position-Specific Scoring Matrices; Predictions; Protein structure; Protein Structure, Secondary; Proteins; Radial basis function; Ribonucleic acid; RNA; RNA-binding protein; RNA-Binding Proteins - chemistry; RNA-Binding Proteins - metabolism; ROC Curve; Secondary structure; Sequence Analysis, Protein - statistics & numerical data; Solvents; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0086703

Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins.