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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 teacher head, 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".