Optimization of Immobilized Bacterial Disaccharides for Surface Plasmon Resonance Imaging Measurements of Antibody Binding
Bibliographic record
Abstract
The interactions between proteins and immobilized carbohydrates are crucial to biological events such as cell signaling and immune response. The modification of surfaces with carbohydrates to create sensing platforms provides a pathway to study these interactions in a laboratory setting. In this work, a family of structurally related Salmonella disaccharide epitopes is immobilized on thin gold films in an array format to probe antibody binding with surface plasmon resonance (SPR) imaging. The disaccharides are modified with an alkyl thiol linker for facile immobilization to gold. Small differences in the stereochemistry of the immobilized, modified disaccharides are shown to greatly influence the binding of a monoclonal antibody. Specifically, binding is only observed to an immobilized abequose dideoxyhexose relative to a tyvelose or a paratose analogue. However, both the amount and relative strength of bound antibody depends on the distribution of disaccharide moieties in a mixed monolayer of the epitope and a nonbinding diluent molecule. We thoroughly characterize the mixed monolayers with a variety of techniques to understand the optimal density and distribution of the disaccharide for antibody capture. This work reinforces the importance of controlling the density of ligands at the interface for optimized surface based bioassays.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".