Computational Approaches for Predicting Site Directed miRNAs as Transcriptional Regulators Against HTLV-1 Virus Infection

Human T-cell leukemia virus type 1 (HTLV-1) is the first identified carcinogenic human retrovirus, with current estimates suggesting 5 to 10 million people are infected globally, likely an underestimation due to limited data. HTLV-1 transmission occurs mainly through direct contact with cell-rich body fluids such as blood, breast milk, and semen. In this study, we used computational tools (RNA22, RNAhybrid, and miRanda) to predict host microRNAs (miRNAs) that may interact with the HTLV-1 genome. These tools identify miRNA binding sites based on factors such as seed match, conservation, free energy, and site accessibility. Computational studies indicate that has-mir-4487 could be involved in HTLV-1 infection. This was followed by prediction of the binding sites of has‑mir-4487 to the tax of HTLV-1 genome. In addition, structural predictions using MC-Fold and MC-Sym were carried out to make predictions of the three-dimensional structure of the miRNA and the target mRNA respectively. Molecular docking simulations were used to confirm the interaction profile between the miRNA and mRNA. All these findings point to the fact that hsa-miR-4487 might be able to bind directly to the mRNA of the HTLV-1 Tax gene, and thus directly suppress its translation and, as a result, reduce the level of Tax protein, which is a key regulator of viral transcription and replication.