title: Predicting DNA-binding proteins: approached from Chou's pseudo amino acid composition and other specific sequence features.
abstract: DNA-binding proteins play a pivotal role in gene regulation. It is vitally important to develop an automated and efficient method for timely identification of novel DNA-binding proteins. In this study, we proposed a method based on alone the primary sequences of proteins to predict the DNA-binding proteins. DNA-binding proteins were encoded by autocross-covariance transform, pseudo-amino acid composition, dipeptide composition, respectively and also the different combinations of the three encoded methods; further, these feature matrices were applied to support vector machine classifiers to predict the DNA-binding proteins. All modules were trained and validated by the jackknife cross-validation test. Through comparing the performance of these substituted modules, the best result was obtained from pseudo-amino acid composition with the overall accuracy of 96.6% and the sensitivity of 90.7%. The results suggest that it can efficiently predict the novel DNA-binding proteins only using the primary sequences.
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, China.
authors.forename: Y
authors.initials: Y
authors.lastname: Fang
authors.forename: Y
authors.initials: Y
authors.lastname: Guo
authors.forename: Y
authors.initials: Y
authors.lastname: Feng
authors.forename: M
authors.initials: M
authors.lastname: Li
mesh_terms: D000596:Amino Acids / Q000737:chemistry* / Q000378:metabolism*; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D019295:Computational Biology; D004268:DNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein
chemical_list: D000596:Amino Acids; D004268:DNA-Binding Proteins