High-accuracy prediction of protein structural classes using PseAA structural properties and secondary structural patterns

• Published in: Biochimie Vol. 101; pp. 104 - 112
• Main Authors: Wang, Junru, Li, Yan, Liu, Xiaoqing, Dai, Qi, Yao, Yuhua, He, Pingan
• Format: Journal Article
• Language: English
• Published: France Elsevier B.V 01-06-2014
• Subjects: Algorithms; Amino Acid Sequence; Computer Simulation; Databases, Protein; Hydrophobic and Hydrophilic Interactions; Local structural correlation; Long-range structural property; Models, Molecular; Protein structural class prediction; Protein Structure, Secondary; Proteins - chemistry; PseAAs; Sequence Analysis, Protein; Support Vector Machine; Protein structural class prediction; Long-range structural property; Support vector machine; Local structural correlation; PseAAs
• ISSN: 0300-9084; 1638-6183
• DOI: 10.1016/j.biochi.2013.12.021

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 25PDB, 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.
•We explored the long-range structural properties of the PseAAs.•We first described local structural correlation among the contiguous m residues.•We first analyzed the patterns' distribution of compact structural domain.