Method for Distinguishing Specific from Nonspecific Protein−Ligand Complexes in Nanoelectrospray Ionization Mass Spectrometry
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it