title: Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles.
abstract: Computational prediction of protein structural class based solely on sequence data remains a challenging problem in protein science. Existing methods differ in the protein sequence representation models and prediction engines adopted. In this study, a powerful feature extraction method, which combines position-specific score matrix (PSSM) with auto covariance (AC) transformation, is introduced. Thus, a sample protein is represented by a series of discrete components, which could partially incorporate the long-range sequence order information and evolutionary information reflected from the PSI-BLAST profile. To verify the performance of our method, jackknife cross-validation tests are performed on four widely used benchmark datasets. Comparison of our results with existing methods shows that our method provides the state-of-the-art performance for structural class prediction. A Web server that implements the proposed method is freely available at http://202.194.133.5/xinxi/AAC_PSSM_AC/index.htm.
authors.affiliation: College of Information Sciences and Engineering, Shandong Agricultural University, Taian, 271018, China.
authors.forename: Taigang
authors.initials: T
authors.lastname: Liu
authors.forename: Xingbo
authors.initials: X
authors.lastname: Geng
authors.forename: Xiaoqi
authors.initials: X
authors.lastname: Zheng
authors.forename: Rensuo
authors.initials: R
authors.lastname: Li
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012984:Software; D060388:Support Vector Machine
chemical_list: D011506:Proteins