HIV-1 Vif protein sequence variations in South African people living with HIV and their influence on Vif-APOBEC3G interaction
Bibliographic record
Abstract
PURPOSE: Despite extensive research, HIV-1 remains a global epidemic with variations in pathogenesis across regions and subtypes. The Viral Infectivity Factor (Vif) protein, which neutralizes the host protein APOBEC3G, has been implicated in differences in clinical outcomes among people living with HIV (PLHIV). Most studies on Vif sequence diversity have focused on subtype B, leaving gaps in understanding Vif variations in HIV-1C regions like South Africa. This study aimed to identify and compare Vif sequence diversity in a cohort of 51 South African PLHIV and other HIV-1C prevalent regions. METHODS: Sanger sequencing was used for Vif analysis in the cohort, and additional sequences were obtained from the Los Alamos database. Molecular modeling and docking techniques were employed to study the influence of subtype-specific variants on Vif-APOBEC3G binding affinity. RESULTS: The findings showed distinct genetic variations between Vif sequences from India and Uganda, while South African sequences had wider distribution and closer relatedness to both. Specific amino acid substitutions in Vif were associated with geographic groups. Molecular modeling and docking analyses consistently identified specific residues (ARGR19, LYS26, TYR30, TYR44, and TRP79) as primary contributors to intermolecular contacts between Vif and APOBEC3G, essential for their interaction. The Indian Vif variant exhibited the highest predicted binding affinity to APOBEC3G among the studied groups. CONCLUSIONS: These results provide insights into Vif sequence diversity in HIV-1C prevalent regions and shed light on differential pathogenesis observed in different geographical areas. The identified Vif amino acid residues warrant further investigation for their diagnostic, prognostic, and therapeutic potential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".