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 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 at http://hivpre.cvr.gla.ac.uk/.Author summary: Human immunodeficiency virus type 1 (HIV-1) is the cause of acquired immunodeficiency syndrome (AIDS), a disease with no effective cure despite decades of research. A better understanding of the molecular interactions between HIV-1 and human host proteins can facilitate the discovery of potential host targets which may be of great importance for the development of antiviral drugs that go beyond mere control of infection. In this study, we elucidate some host-dependency and antiviral factors that may be helpful to distinguish HIV-1 interacting human proteins (VIPs) from non-HIV-1 interacting human proteins (non-VIPs). We also consider the ‘directionality’ in the HIV-1-host protein-protein interactions, i.e., whether the interaction with the host molecule is in the interest of the virus or part of the anti-viral response. We design a machine learning framework to generate models based on the known information and use them for the classification of VIPs and non-VIPs. Our predictions have the potential to provide refined sets of human host targets aiding in the discovery of novel HIV-1 therapeutics.