Human immunodeficiency virus type 1 (HIV-1) continues to be a major cause of disease and premature death. As with any virus, 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 full mechanistic understanding of virus biology/infection and host antiviral activities. This knowledge will permit the discovery of potential host targets aiding in the development of novel antiviral drugs. Here, we proposed a data-driven approach for the analysis and prediction of HIV-1-host interacting proteins with a focus on the directionality, i.e., virus dependency factors versus anti-viral interactions. Using a support vector machine learning model and features encompassing genetic, proteomic and network properties, our results revealed some significant differences between HIV-1 interacting and non-interacting proteins. We demonstrated that the direction of virus-host molecular interactions is predictable due to different characteristics of ‘forward’/pro-viral versus backward/pro-host proteins. We validated the model using experimentally verified HIV-1 interacting proteins from other resources, achieving 90.6% sensitivity (threshold = 0.5) on 234 interactions. Additionally, we inferred the previously unknown direction of interaction between HIV-1 and 1351 human host proteins.
Please cite the following article if using any of the results from this webserver:
Chai H, Gu Q, Hughes J, Robertson DL (2022) In silico prediction of HIV-1-host molecular interactions and their directionality. PLoS Comput Biol 18(2): e1009720. https://doi.org/10.1371/journal.pcbi.1009720. link
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