期刊
PLOS COMPUTATIONAL BIOLOGY
卷 18, 期 2, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1009720
关键词
-
资金
- China Scholarship Council [201706620069]
- Medical Research Council [MC_UU_1201412]
This study analyzed and predicted the directionality of the interactions between HIV-1 and human host proteins. Using support vector machine learning models, significant differences were found between VIPs and non-VIPs, and the direction of interactions between HIV-1 and 1351 human host proteins was inferred.
Human immunodeficiency virus type 1 (HIV-1) continues to be a major cause of disease and premature death. As with all viruses, HIV-1 exploits a host cell to replicate. Improving our understanding of these molecular interactions between virus and human host proteins is crucial for a mechanistic understanding of virus biology, infection and host antiviral activities. This knowledge will potentially permit the identification of host molecules for targeting by drugs with antiviral properties. Here, we propose a data-driven approach for the analysis and prediction of the HIV-1 interacting proteins (VIPs) with a focus on the directionality of the interaction: host-dependency versus antiviral factors. Using support vector machine learning models and features encompassing genetic, proteomic and network properties, our results reveal some significant differences between the VIPs and non-HIV-1 interacting human proteins (non-VIPs). As assessed by comparison with the HIV-1 infection pathway data in the Reactome database (sensitivity > 90%, threshold = 0.5), we demonstrate these models have good generalization properties. We find that the 'direction' of the HIV-1-host molecular interactions is also predictable due to different characteristics of 'forward'/pro-viral versus 'backward'/pro-host proteins. Additionally, we infer the previously unknown direction of the interactions between HIV-1 and 1351 human host proteins. A web server for performing predictions is available & nbsp;at http://hivpre.cvr.gla.ac.uk/.
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