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

Abstract

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.

Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information

“…As reported in [28], the PDB1075 dataset includes the highest number of protein sequences with low similarity, which is desirous for model evaluation. The other benchmark dataset, called PDB186, was recently constructed by Lou et al [30], and contains 93 actual DNA-binding and 93 non-DNA-binding proteins also collected from PDB. The PDB186 dataset provides an independent test for validating the predictors.…”

“…Given the importance of DNA-binding proteins, methods for identifying members of this protein class are highly desired. In early research, DNA-binding proteins were determined by experimental approaches; typically by filter binding assays, genetic analysis, chromatin immune precipitation on microarrays, and X-ray crystallography [30]. However, experimental methods are costly in terms of time and ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T resources [21].…”

Exploring local discriminative information from evolutionary profiles for cytokine–receptor interaction prediction

“…In particular, a bunch of recent work has reported its high efficiency when applied into various fields, such as protein structural class prediction [52], DNA-binding protein prediction [59], as well as cytokine-receptor interaction prediction [28], and cell penetrating peptide prediction [60], etc.…”

DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues

“…Machine learning algorithms were employed to construct models to predict DNA-binding proteins and produced effective performances [4–9,11–19]. Interestingly, the support vector machine (SVM) algorithm has been used frequently to predict DNA-binding proteins [4–6,8,12–16]. Cai and Lin first applied the SVM algorithm for DNA-binding protein prediction using a protein’s amino acid composition and a limited range of correlations of hydrophobicity and solvent-accessible surface areas as input features [4].…”

“…Random forest (RF) alorgithm, which is a useful machine learning classifier, was aslo used to prdict DNA-binding proteins. Lou et al applied the RF algorithm to predict DNA-binding proteins using predicted secondary structure, predicted relative solvent accessibility and position-specific scoring matrix as the primary sequence features[8]. …”