An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis

Abstract

BackgroundDNA-binding proteins (DNA-BPs) play a pivotal role in both eukaryotic and prokaryotic proteomes. There have been several computational methods proposed in the literature to deal with the DNA-BPs, many informative features and properties were used and proved to have significant impact on this problem. However the ultimate goal of Bioinformatics is to be able to predict the DNA-BPs directly from primary sequence.ResultsIn this work, the focus is how to transform these informative features into uniform numeric representation appropriately and improve the prediction accuracy of our SVM-based classifier for DNA-BPs. A systematic representation of some selected features known to perform well is investigated here. Firstly, four kinds of protein properties are obtained and used to describe the protein sequence. Secondly, three different feature transformation methods (OCTD, AC and SAA) are adopted to obtain numeric feature vectors from three main levels: Global, Nonlocal and Local of protein sequence and their performances are exhaustively investigated. At last, the mRMR-IFS feature selection method and ensemble learning approach are utilized to determine the best prediction model. Besides, the optimal features selected by mRMR-IFS are illustrated based on the observed results which may provide useful insights for revealing the mechanisms of protein-DNA interactions. For five-fold cross-validation over the DNAdset and DNAaset, we obtained an overall accuracy of 0.940 and 0.811, MCC of 0.881 and 0.614 respectively.ConclusionsThe good results suggest that it can efficiently develop an entirely sequence-based protocol that transforms and integrates informative features from different scales used by SVM to predict DNA-BPs accurately. Moreover, a novel systematic framework for sequence descriptor-based protein function prediction is proposed here.

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

“…The PSSM, which represents evolutionary information of amino acid sequences, has been used widely in research on the prediction of DNA-binding residues [30–35] and DNA-binding proteins [6,14,28] based on sequence information. Compared with other features, PSSM contributes most to improving the prediction performance of DNA-binding residues and DNA-binding proteins.…”

“…During the past few decades, a series of studies on the identification of DNA-binding proteins using sequence information have been published [4–14]. Machine learning algorithms were employed to construct models to predict DNA-binding proteins and produced effective performances [4–9,11–19].…”

“…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]. More recently, Zou et al developed an entirely sequence-based protocol that transforms and integrates informative features from different scales used by SVM to predict DNA-binding proteins [14]. Zhang et al proposed newDNA-Prot, a DNA-binding protein predictor that employs an SVM classifier and a comprehensive feature that categorized features into six groups: primary sequence-based, evolutionary profile-based, predicted secondary structure-based, predicted relative solvent accessibility-based, physicochemical property-based and biological function-based features [13].…”

PseDNA‐Pro: DNA‐Binding Protein Identification by Combining Chou’s PseAAC and Physicochemical Distance Transformation

“…Previous studies [4,22] showed that the overall amino acid composition of 20 standard amino acids was a widely used sequence feature in the field of bioinformatics. In this feature space, the overall amino acid composition is defined as the occurrence frequencies of 20 standard amino acids.…”

“…Amino acid composition of proteins associated with the biochemical properties are the commonly used sequence-based features, for example Cai and Lin [1] used protein's amino acid composition, limited range correlation of hydrophobicity and solvent accessible surface area to identify DBPs; Ahmad et al [2] found the specificity of sequence level and binding level and analyzed the relationship between them; Fang et al [3] encoded the feature space by autocross-covariance (ACC) transform, pseudoamino acid composition, dipeptide composition; Zou et al [4] adopted three different feature transformation methods to generate numeric feature vectors from protein sequences; Lin et al [5] represented each sequence as pseudo amino acid composition by applied grey model. For more accurately predictive performance, the combinations of different features were employed, for example Kumar et al [6] derived sequence properties by frequency of amino acid, amino acid groups, secondary structure, comAbstract: Identification of DNA-binding proteins is an important problem in biomedical research as DNA-binding proteins are crucial for various cellular processes.…”