A computational model for predicting integrase catalytic domain of retrovirus. (21st June 2017)

• Record Type: Journal Article
• Authors: Wu, Sijia; Han, Jiuqiang; Zhang, Xinman; Zhong, Dexing; Liu, Ruiling
• Is Part Of: Journal of theoretical biology. Volume 423(2017)
• Page Start: 63
• Page End: 70
• Publication Date: 2017-06-21
• Subjects: Pseudo amino acid composition -- Position weight matrix -- Random forest -- Support vector machine; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2017.04.020
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: A novel computational model for predicting integrase catalytic domain of retroviruses has been proposed. A software tool named ICDtool has been developed. A large number of new putative ICDs have been discovered. A tight connection between ICD and retroviral classification has been verified. Abstract: Integrase catalytic domain (ICD) is an essential part in the retrovirus for integration reaction, which enables its newly synthesized DNA to be incorporated into the DNA of infected cells. Owing to the crucial role of ICD for the retroviral replication and the absence of an equivalent of integrase in host cells, it is comprehensible that ICD is a promising drug target for therapeutic intervention. However, annotated ICDs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. Accordingly, it is of great importance to put forward a computational ICD model in this work to annotate these domains in the retroviruses. The proposed model then discovered 11, 660 new putative ICDs after scanning sequences without ICD annotations. Subsequently in order to provide much confidence in ICD prediction, it was tested under different cross-validation methods, compared with other database search tools, and verified on independent datasets. Furthermore, an evolutionary analysis performed on the annotated ICDs of retroviruses revealed a tight connection between ICD and retroviral classification. All the datasets involved in this paper and the application software tool of this model can be available for free download athttps://sourceforge.net/projects/icdtool/files/?source=navbar .