Determination of Affinity and Kinetic Rate Constants Using Surface Plasmon Resonance
Bibliographic record
Abstract
Surface plasmon resonance (SPR) is a relatively new technique extremely useful for studying macromolecular interactions between proteins, proteins and DNA, or proteins and lipids. Biospecific interaction analyses using SPR provides valuable information about the strength, speed, and stoichiometry of the interaction in real time and without the use of labels. An excellent review on the commercial SPR instrument called BIAcore™ has been published recently ( 1 ). In general the device is a biosensor that measures mass accretion/loss within a finite surface volume as a function of time. The initial step is to stably immobilize a known quantity of one of the interactants (ligand) on the surface. Following this, the second or free-flowing interactant (analyte) is made available for binding to its putative surface-linked homologue through diffusion from a pool or source that steadily flows over the immobilized ligand surface. By replacing analyte solution with buffer the source becomes a sink taking away complex-dissociated analyte from the ligand surface, resulting in a detectable loss of mass. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".