Structure-based humanization of a therapeutic antibody for Multiple Myeloma
Bibliographic record
Abstract
Abstract The optimal efficacy of xenogeneically generated proteins intended for application in humans requires that their own antigenicity be minimized. This necessary adaptation of antibodies to a humanized version poses challenges since modifications even distant from the binding sites can greatly influence antigen recognition and this is the primary feature that must be maintained during all modifications. Current strategies often rely on grafting and/or randomization/selection to arrive at a humanized variant retaining the binding properties of the original molecule. However, in terms of speed and efficiency, rationally directed approaches can be superior, provided the requisite structural information is available. We present here a humanization procedure based on the high-resolution X-ray structure of a chimeric IgG against a marker for Multiple Myeloma. Based on in silico modelling of humanizing amino acid substitutions identified from sequence alignments, we devised a straightforward cloning procedure to rapidly evaluate the proposed sequence changes. Careful inspection of the structure allowed identification of a potentially problematic amino acid change that indeed disrupted antigen binding. Subsequent optimization of the antigen binding loop sequences resulted in substantial recovery of binding affinity lost in the completely humanized antibody. X-ray structures of the humanized and optimized variants demonstrate that the antigen binding mode is preserved, with surprisingly few direct contacts to antibody atoms. These results underline the importance of structural information for the efficient optimization of protein therapeutics.
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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 | 0.001 |
| 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".