“Binding” or “Binding and Switching”? A Perspective on Resolving Conformational Changes of Surface-Attached Biomolecular Receptors
Bibliographic record
Abstract
Understanding the structural reconfiguration of a biomolecular receptor remains a topic of particular interest to the biosensing community. This is because conformationally changing receptors are commonly employed in biosensors to harness their capability to bind specifically to their target. Often, such receptors are attached to surfaces so that binding can be transduced into a measurable response. Doing so, however, can impose constraints on the possible configurations they can adopt. Such constraints can ultimately influence, for example, their receptor-target binding models or their affinity, which is essential to provide the desired analytical performances in biosensors. Motivated by the idea of gaining further insights into the impact of surface attachment on conformationally switching receptors attached to surfaces, we explore here the various surface-based techniques capable of monitoring structural changes. We decided to narrow our survey to techniques that have been applied to the investigation of nucleic acids to provide an overview of their key features. We envision that this will bring a broader perspective of the field and the challenges ahead with the hopes of "finding the switch" in surface-attached biomolecular receptors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".