Production, characterization, and antigen specificity of recombinant 62‐71‐3, a candidate monoclonal antibody for rabies prophylaxis in humans
Bibliographic record
Abstract
Rabies kills many people throughout the developing world every year. The murine monoclonal antibody (mAb) 62‐71‐3 was recently identified for its potential application in rabies postexposure prophylaxis (PEP). The purpose here was to establish a plant‐based production system for a chimeric mouse‐human version of mAb 62‐71‐3, to characterize the recombinant antibody and investigate at a molecular level its interaction with rabies virus glycoprotein. Chimeric 62‐71‐3 was successfully expressed in Nicotiana benthamiana. Glycosylation was analyzed by mass spectroscopy; functionality was confirmed by antigen ELISA, as well as rabies and pseudotype virus neutralization. Epitope characterization was performed using pseudotype virus expressing mutagenized rabies glycoproteins. Purified mAb demonstrated potent viral neutralization at 500 IU/mg. A critical role for antigenic site I of the glycoprotein, as well as for two specific amino acid residues (K226 and G229) within site I, was identified with regard to mAb 62‐71‐3 neutralization. Pseudotype viruses expressing glycoprotein from lyssaviruses known not to be neutralized by this antibody were the controls. The results provide the molecular rationale for developing 62‐71‐3 mAb for rabies PEP; they also establish the basis for developing an inexpensive plant‐based antibody product to benefit low‐income families in developing countries.—Both, L., van Dolleweerd, C., Wright, E., Banyard, A. C., Bulmer‐Thomas, B., Selden, D., Altmann, F., Fooks, A. R., Ma, J. K.‐C. Production, characterization, and antigen specificity of recombinant 62‐71‐3, a candidate monoclonal antibody for rabies prophylaxis in humans. FASEB J. 27, 2055–2065 (2013). www.fasebj.org
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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 | 0.001 |
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".