ArticleScientific reports2026
Predicting the evolutionary and structural compensation in Tat ARM region across HIV-1 groups using machine learning approach.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Trans-activator of transcription (Tat), a key regulatory protein of HIV-1, binds the TAR RNA element and is essential for early viral pathogenesis. Remarkably, Tat from HIV-1 groups M, N, O, and P shows notable sequence variability, especially in the critical Arginine-Rich Motifs (ARM) required for TAR binding, presenting both challenges and opportunities for therapeutic targeting. Computational analysis and machine learning (ML) approaches were utilized to analyze over 45,000 HIV-1 tat sequences, retrieved from the Los Alamos National Laboratory (LANL) database spanning 1997 to 2021. Host-mediated selection on tat gene ensued distinct group-specific pattern, reflected in non-synonymous vs. synonymous ratio (dn/ds) of 2.20, 2.04, 2.45, and 1.08 for groups M, N, O, and P, with corresponding entropy scores of 0.21, 0.35, 0.14, and 0.01, respectively. Groups M and N exhibited comparable selective pressure, while O and P evolved under distinct environments. Remarkably, group-specific sequence variability was evident in TAR binding region, particularly within the ARM, likely influencing viral pathogenicity. Docking analysis of wild-type Tat-TAR complexes supported this, showing binding affinities (docking scores) of − 313, − 242, − 282, and − 239, for groups M, N, O, and P, respectively. Phylogenetic Dependency Network (PDN) analysis revealed 1375, 36, 135, and 6 co-evolutionary associations within Tat for these groups, including 183, 1, 12, and 0 from the ARM motif. Structural modeling combined with PDN analysis identified 355 associated mutations in group M conferring structural compensation, of which 40 were from the ARM region. By leveraging these structural and co-evolutionary insights, we developed an ML model to classify compensatory mutations. HIV-1 Tat evolves differentially under host-mediated selective pressures, with group M exhibiting the highest adaptability through coevolutionary compensation, reflecting its global infection dynamics. Targeting conserved regions and compensatory mutational pathways in Tat may yield novel strategies for HIV-1 therapeutics.
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