title: Identification of DNA-binding proteins using support vector machine with sequence information.
abstract: DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.
authors.affiliation: Golden Audit College, Nanjing Audit University, Nanjing 210029, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Ma
authors.forename: Jiansheng
authors.initials: J
authors.lastname: Wu
authors.forename: Xiaoyun
authors.initials: X
authors.lastname: Xue
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001665:Binding Sites; D004268:DNA-Binding Proteins / Q000235:genetics* / Q000378:metabolism*; D030562:Databases, Protein / Q000706:statistics & numerical data; D060388:Support Vector Machine
chemical_list: D004268:DNA-Binding Proteins