title: Prediction of protein structural classes for low-similarity sequences using reduced PSSM and position-based secondary structural features.
abstract: Many efficient methods have been proposed to advance protein structural class prediction, but there are still some challenges where additional insight or technology is needed for low-similarity sequences. In this work, we schemed out a new prediction method for low-similarity datasets using reduced PSSM and position-based secondary structural features. We evaluated the proposed method with four experiments and compared it with the available competing prediction methods. The results indicate that the proposed method achieved the best performance among the evaluated methods, with overall accuracy 3-5% higher than the existing best-performing method. This paper also found that the reduced alphabets with size 13 simplify PSSM structures efficiently while reserving its maximal information. This understanding can be used to design more powerful prediction methods for protein structural class.
authors.affiliation: College of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
authors.forename: Junru
authors.initials: J
authors.lastname: Wang
authors.affiliation: College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
authors.forename: Cong
authors.initials: C
authors.lastname: Wang
authors.affiliation: College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
authors.forename: Jiajia
authors.initials: J
authors.lastname: Cao
authors.affiliation: College of Sciences, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China.
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Liu
authors.affiliation: College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
authors.forename: Yuhua
authors.initials: Y
authors.lastname: Yao
authors.affiliation: College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China; Department of Molecular and Cell Biology, University of Texas at Dallas, Richardson, TX 75080, USA. Electronic address: daiailiu04@yahoo.com.
authors.forename: Qi
authors.initials: Q
authors.lastname: Dai
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D056510:Position-Specific Scoring Matrices; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins
keywords: Global correlation; Protein structural class prediction; Reduced PSSM; Secondary structural feature