Neutralizing Epitopes in the Membrane-Proximal External Region of HIV-1 gp41 Are Influenced by the Transmembrane Domain and the Plasma Membrane
Bibliographic record
Abstract
Failure to elicit broadly neutralizing (bNt) antibodies (Abs) against the membrane-proximal external region of HIV-1 gp41 (MPER) reflects the difficulty of mimicking its neutralization-competent structure (NCS). Here, we analyzed MPER antigenicity in the context of the plasma membrane and identified a role for the gp41 transmembrane domain (TM) in exposing the epitopes of three bNt monoclonal Abs (MAbs) (2F5, 4E10, and Z13e1). We transiently expressed DNA constructs encoding gp41 ectodomain fragments fused to either the TM of the platelet-derived growth factor receptor (PDGFR) or the gp41 TM and cytoplasmic tail domain (CT). Constructs encoding the MPER tethered to the gp41 TM followed by a 27-residue CT fragment (MPER-TM1) produced optimal MAb binding. Critical binding residues for the three Nt MAbs were identified using a panel of 24 MPER-TM1 mutants bearing single amino acid substitutions in the MPER; many were previously shown to affect MAb-mediated viral neutralization. Moreover, non-Nt mutants of MAbs 2F5 and 4E10 exhibited a reduction in binding to MPER-TM1 and yet maintained binding to synthetic MPER peptides, indicating that MPER-TM1 better approximates the MPER NCS than peptides. Replacement of the gp41 TM and CT of MPER-TM1 with the PDGFR TM reduced binding by MAb 4E10, but not 2F5, indicating that the gp41 TM plays a pivotal role in orienting the 4E10 epitope, and more globally, in affecting MPER exposure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".