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Abstract Background The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in millions of infections worldwide. While the search for an effective antiviral is still ongoing, experimental therapies based on repurposing of available antivirals is being attempted, of which HIV protease inhibitors (PIs) have gained considerable interest. Inhibition profiling of the PIs directly against the viral protease has never been attempted in vitro, and while few studies reported an efficacy of lopinavir and ritonavir in SARS-CoV-2 context, the mechanism of action of the drugs remains to be validated. Methods We carried out an in-depth analysis of the efficacy of HIV PIs against the main protease of SARS-CoV-2 (Mpro) in cell culture and in vitro enzymatic assays, using a methodology that enabled us to focus solely on any potential inhibitory effects of the inhibitors against the viral protease. For cell culture experiments a dark-to-bright GFP reporter substrate system was designed. Results Lopinavir, ritonavir, darunavir, saquinavir, and atazanavir were able to inhibit the viral protease in cell culture, albeit in concentrations much higher than their achievable plasma levels, given their current drug formulations. While inhibition by lopinavir was attributed to its cytotoxicity, ritonavir was the most effective of the panel, with IC50 of 13.7 µM. None of the inhibitors showed significant inhibition of SARS-CoV-2 Mpro in our in vitro enzymatic assays up to 100 µM concentration. Conclusion Targeting of SARS-CoV-2 Mpro by some of the HIV PIs might be of limited clinical potential, given the high concentration of the drugs required to achieve significant inhibition. Therefore, given their weak inhibition of the viral protease, any potential beneficial effect of the PIs in COVID-19 context might perhaps be attributed to acting on other molecular target(s), rather than SARS-CoV-2 Mpro.

Abstract Background Utilization of quantitative proteomics data on the network level is still a challenge in proteomics data analysis. Currently existing models use sophisticated, sometimes hard to implement analysis techniques. Our aim was to generate a relatively simple strategy for quantitative proteomics data analysis in order to utilize as much of the data generated in a proteomics experiment as possible. Results In this study, we applied label-free proteomics, and generated a network model utilizing both qualitative, and quantitative data, in order to examine the early host response to Human Immunodeficiency Virus type 1 (HIV-1). A weighted network model was generated based on the amount of proteins measured by mass spectrometry, and analysis of weighted networks and functional sub-networks revealed upregulation of proteins involved in translation, transcription, and DNA condensation in the early phase of the viral life-cycle. Conclusion A relatively simple strategy for network analysis was created and applied to examine the effect of HIV-1 on host cellular proteome. We believe that our model may prove beneficial in creating algorithms, allowing for both quantitative and qualitative studies of proteome change in various biological and pathological processes by quantitative mass spectrometry.