title: High-accuracy prediction of protein structural classes using PseAA structural properties and secondary structural patterns.
abstract: Since introduction of PseAAs and functional domains, promising results have been achieved in protein structural class predication, but some challenges still exist in the representation of the PseAA structural correlation and structural domains. This paper proposed a high-accuracy prediction method using novel PseAA structural properties and secondary structural patterns, reflecting the long-range and local structural properties of the PseAAs and certain compact structural domains. The proposed prediction method was tested against the competing prediction methods with four experiments. The experiment results indicate that the proposed method achieved the best performance. Its overall accuracies for datasets 25 PDB, D640, FC699 and 1189 are 88.8%, 90.9%, 96.4% and 87.4%, which are 4.5%, 7.6%, 2% and 3.9% higher than the existing best-performing method. This understanding can be used to guide development of more powerful methods for protein structural class prediction. The software and supplement material are freely available at http://bioinfo.zstu.edu.cn/PseAA-SSP.
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: Yan
authors.initials: Y
authors.lastname: Li
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. Electronic address: daiailiu04@yahoo.com.
authors.forename: Qi
authors.initials: Q
authors.lastname: Dai
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 Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
authors.forename: Pingan
authors.initials: P
authors.lastname: He
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D030562:Databases, Protein; D057927:Hydrophobic and Hydrophilic Interactions; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein; D060388:Support Vector Machine
chemical_list: D011506:Proteins
keywords: Local structural correlation; Long-range structural property; Protein structural class prediction; PseAAs; Support vector machine