Optimization of Lectin Biotinylation for the Assessment of Therapeutic Protein Glycosylation
Bibliographic record
Abstract
RÉSUMÉ: La glycosylation est un attribut de qualité critique pour les protéines thérapeutiques, et est également un biomarqueur de plusieurs maladies humaines. Pour évaluer l’état de glycosylation des protéines, les lectines sont souvent utilisées car leurs spécificités pour les sucres sont bien caractérisées. Les lectines ont été proposées pour la détection et la caractérisation de plusieurs produits biothérapeutiques, tels que les vaccins viraux et les anticorps monoclonaux. Dans les tests à base de lectines, il est commun de conjuguer celles-ci avec de la biotine. Les étiquettes à base de biotine permettent aux lectines de recruter des complexes à base de streptavidine capables de générer des signaux, ou de capturer des lectines sur des surfaces de biocapteurs fonctionnalisées avec de la streptavidine. Dans ce travail, nous explorons l’application de lectines pour la détection de produits biothérapeutiques dans les biocapteurs à base de résonance de plasmons de surface et dans des tests à base d’enzymes liés aux lectines. En particulier, on démontre que le ratio de conjugaison biotine-lectine peut avoir un impact important sur la performance de ces deux tests à travers la modulation de l’interaction lectine-streptavidine. Dans les biocapteurs à base de résonance de plasmons de surface, une biotinylation excessive peut réduire la capture à des surfaces de streptavidine et peut modifier les cinétiques d’interactions entre les lectines et des glycoprotéines. Dans le test ELLA (« enzyme-linked lectin assay »), un haut niveau de conjugaison amplifie le signal spécifique et baisse la limite de détection. A la lumière de ces résultats, des recommandations sont formulées quant au niveau optimal de biotinylation des lectines selon l’application envisagée. ABSTRACT: Protein glycosylation is a crucial attribute for therapeutic proteins and a biomarker for many human diseases. For assessing protein glycosylation, lectins are often used as they have well characterized sugar specificities. Lectins have been proposed for the detection and characterization of many biotherapeutic products, including viral vaccines and monoclonal antibodies. In lectin-based assays, it is common to tag lectins with biotin. Biotin-tags allow lectins to recruit signal generating streptavidin-based compounds or capture lectins to streptavidin-functionalized biosensing surfaces. In this work, we explore the use of lectins for the detection of biotherapeutic products in surface plasmon resonance and the enzyme-linked lectin assay. In particular, we demonstrate that the biotin-lectin conjugation ratio can have a significant impact on the performance of these two assay types, via the modulation of the lectin-streptavidin interaction. In surface plasmon resonance assays, excess biotinylation can reduce capture to streptavidin surfaces and modify lectin-glycoprotein binding kinetics. In the enzyme-linked lectin assay, high biotin conjugation amplifies the specific signal and lowers the limit of detection. Finally, we present a workflow for the assessment of optimal lectin biotin conjugation ratio.
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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 | 0.001 | 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".