title: Predict protein structural class for low-similarity sequences by evolutionary difference information into the general form of Chou's pseudo amino acid composition.
abstract: Knowledge of protein structural class plays an important role in characterizing the overall folding type of a given protein. At present, it is still a challenge to extract sequence information solely using protein sequence for protein structural class prediction with low similarity sequence in the current computational biology. In this study, a novel sequence representation method is proposed based on position specific scoring matrix for protein structural class prediction. By defined evolutionary difference formula, varying length proteins are expressed as uniform dimensional vectors, which can represent evolutionary difference information between the adjacent residues of a given protein. To perform and evaluate the proposed method, support vector machine and jackknife tests are employed on three widely used datasets, 25PDB, 1189 and 640 datasets with sequence similarity lower than 25%, 40% and 25%, respectively. Comparison of our results with the previous methods shows that our method may provide a promising method to predict protein structural class especially for low-similarity sequences.
authors.affiliation: College of Marine Life Science, Ocean University of China, Yushan Road, Qingdao 266003, PR China.
authors.forename: Lichao
authors.initials: L
authors.lastname: Zhang
authors.affiliation: College of Mathematical Science, Ocean University of China, Songling Road, Qingdao 266100, PR China. Electronic address: xqzhao62@163.com.
authors.forename: Xiqiang
authors.initials: X
authors.lastname: Zhao
authors.affiliation: College of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, PR China.
authors.forename: Liang
authors.initials: L
authors.lastname: Kong
mesh_terms: D000595:Amino Acid Sequence; D066264:Datasets as Topic; D019143:Evolution, Molecular; D008962:Models, Theoretical; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D020539:Sequence Analysis, Protein / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D011506:Proteins
keywords: Mutation difference information; Position specific score matrix; Sequence similarity