In silico prediction of HIV-1-host molecular interactions and their directionality.
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 the molecular interactions between virus and human host proteins is crucial for a mechanistic underst...
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Public Library of Science (PLoS)
2022-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009720&type=printable |
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| author | Haiting Chai Quan Gu Joseph Hughes David L Robertson |
| author_facet | Haiting Chai Quan Gu Joseph Hughes David L Robertson |
| author_sort | Haiting Chai |
| collection | DOAJ |
| description | 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 the 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 at http://hivpre.cvr.gla.ac.uk/. |
| format | Article |
| id | doaj-art-9c3c63d712434eb9a6b1a026b6ebd601 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2022-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-9c3c63d712434eb9a6b1a026b6ebd6012025-08-20T02:23:18ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-02-01182e100972010.1371/journal.pcbi.1009720In silico prediction of HIV-1-host molecular interactions and their directionality.Haiting ChaiQuan GuJoseph HughesDavid L RobertsonHuman 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 the 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 at http://hivpre.cvr.gla.ac.uk/.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009720&type=printable |
| spellingShingle | Haiting Chai Quan Gu Joseph Hughes David L Robertson In silico prediction of HIV-1-host molecular interactions and their directionality. PLoS Computational Biology |
| title | In silico prediction of HIV-1-host molecular interactions and their directionality. |
| title_full | In silico prediction of HIV-1-host molecular interactions and their directionality. |
| title_fullStr | In silico prediction of HIV-1-host molecular interactions and their directionality. |
| title_full_unstemmed | In silico prediction of HIV-1-host molecular interactions and their directionality. |
| title_short | In silico prediction of HIV-1-host molecular interactions and their directionality. |
| title_sort | in silico prediction of hiv 1 host molecular interactions and their directionality |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009720&type=printable |
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