Abstract
The human T-lymphotropic virus type 1 (HTLV-1) infects millions of people globally and is endemic to various resource-limited regions. Infections persist for life and are associated with increased susceptibility to opportunistic infections and severe diseases including adult T cell leukemia/lymphoma and HTLV-1-associated myelopathy-tropical spastic paraparesis. No HTLV-1-specific anti-retrovirals have been developed and it is unclear whether existing anti-retrovirals developed for treatment of human immunodeficiency virus (HIV) have efficacy against HTLV-1. To understand the structural basis for therapeutic binding, homology modelling and machine learning were used to develop a structural model of the HTLV-1 reverse transcriptase. With this, molecular docking experiments using a panel of FDA-approved inhibitors of viral reverse transcriptases to assess their capacity for binding, and in turn, inhibition. Importantly, nucleoside/nucleotide reverse transcriptase inhibitor but not non-nucleoside reverse transcriptase inhibitors were predicted to bind the HTLV-1 reverse transcriptase, with similar affinity to HIV-1 reverse transcriptase. By strengthening the rationale for clinical testing of therapies such as tenofovir alafenamide, zidovudine, lamivudine, and azvudine for treatment of HTLV-1, this study has demonstrated the power of in silico structural biology approaches in drug design and therapeutic testing.