Method for Distinguishing Specific from Nonspecific Protein−Ligand Complexes in Nanoelectrospray Ionization Mass Spectrometry
Bibliographic record
Abstract
A new methodology for distinguishing between specific and nonspecific protein-ligand complexes in nanoelectrospray ionization mass spectrometry (nanoES-MS) is described. The method involves the addition of an appropriate reference protein (P(ref)), which does not bind specifically to any of the solution components, to the nanoES solution containing the protein(s) and ligand(s) of interest. The occurrence of nonspecific protein-ligand binding is monitored by the appearance of nonspecific (P(ref) + ligand) complexes in the nanoES mass spectrum. Furthermore, the fraction of P(ref) undergoing nonspecific ligand binding provides a quantitative measure of the contribution of nonspecific binding to the measured intensities of protein and specific protein-ligand complexes. As a result, errors introduced into protein-ligand association constants, K(assoc), as determined with nanoES-MS, by nonspecific ligand binding can be corrected. The principal assumptions on which this methodology is based, namely, that the fraction of proteins and protein complexes that engage in nonspecific ligand binding during the nanoES process is determined by the number of free ligand molecules in the offspring droplets leading to gaseous ions and is independent of the size and structure of the protein or protein complex, are shown to be generally valid. The application of the method for the determination of K(assoc) for two protein-carbohydrate complexes, under conditions where nonspecific ligand binding is prevalent, is demonstrated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".