Functional comparison of Fc-engineering strategies to improve anti-HIV-1 antibody effector functions
Bibliographic record
Abstract
Substantial reduction of the intact proviral reservoir is essential towards HIV-1 cure. In vivo administration of broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) trimer can decrease the viral reservoir, through Fc-mediated killing of infected cells. In this study, we compared three commonly used antibody engineering strategies to enhance Fc-mediated effector functions: (i) glyco-engineering, (ii) protein engineering, and (iii) subclass/hinge modifications in a panel of anti-HIV-1 antibodies. We found that antibody-dependent cellular phagocytosis (ADCP) was improved by elongating the hinge domain and switching to an IgG3 constant domain. In addition, potent NK cell activation and ADCC activity was observed for afucosylated antibodies and antibodies bearing the GASDALIE mutations. The combination of these engineering strategies further increased NK cell activation and induced antibody dependent cytotoxicity (ADCC) of infected cells at low antibody concentrations. The bNAb N6 was most effective at killing HIV-1 infected cells, likely due to its high affinity and optimal angle of approach. Overall, the findings of this study are applicable to other antibody formats, and can aid the development of effective immunotherapies and antibody-based treatments for HIV-1 cure strategies. • Antibody-engineering may enhance the ability of HIV-1 bNAbs to eliminate HIV-1 infected cells. • IgG3 boost FcγRIIa-mediated phagocytosis but not the killing of infected cells. • Afucosylated and GASDALIE-bearing antibodies enhanced FcγRIIIa-mediated ADCC. • Combining engineering strategies further improved killing of HIV-1 infected cells. • bNAbs with enhanced FcγRIIIa-mediated ADCC may aid HIV-1 cure strategies.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".