Optimization of peptidic HIV‐1 fusion inhibitor T20 by phage display
Bibliographic record
Abstract
The HIV fusion inhibitor T20 has been approved to treat those living with HIV/AIDS, but treatment gives rise to resistant viruses. Using combinatorial phage-displayed libraries, we applied a saturation scan approach to dissect the entire T20 sequence for binding to a prefusogenic five-helix bundle (5HB) mimetic of HIV-1 gp41. Our data set compares all possible amino acid substitutions at all positions, and affords a complete view of the complex molecular interactions governing the binding of T20 to 5HB. The scan of T20 revealed that 12 of its 36 positions were conserved for 5HB binding, which cluster into three epitopes: hydrophobic epitopes at the ends and a central dyad of hydrophilic residues. The scan also revealed that the T20 sequence was highly adaptable to mutations at most positions, demonstrating a striking structural plasticity that allows multiple amino acid substitutions at contact points to adapt to conformational changes, and also at noncontact points to fine-tune the interface. Based on the scan result and structural knowledge of the gp41 fusion intermediate, a library was designed with tailored diversity at particular positions of T20 and was used to derive a variant (T20v1) that was found to be a highly effective inhibitor of infection by multiple HIV-1 variants, including a common T20-escape mutant. These findings show that the plasticity of the T20 functional sequence space can be exploited to develop variants that overcome resistance of HIV-1 variants to T20 itself, and demonstrate the utility of saturation scanning for rapid epitope mapping and protein engineering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".