Quantifying non‐covalent binding affinity using mass spectrometry: A systematic study on complexes of cyclodextrins with alkali metal cations
Bibliographic record
Abstract
RATIONALE: To date, the quantification of binding affinities for non-covalent complexes between cyclodextrin (CD) and alkali cations including Li(+) , Na(+) , K(+) , Rb(+) , and Cs(+) has not been investigated in detail by electrospray ionization mass spectrometry (ESI-MS) due to the unknown ionization efficiencies of the different species. In this study, the binding constants of CD-Cs(+) complexes were determined by an improved mass spectrometric titration methodology, which was based only on the peak intensities of equilibrium CD. Hence, the discrepancy of ionization efficiencies of CD, alkaline cation and their complex would not affect the measurement. Then the obtained lgKa values were provided as references for competitive ESI-MS. The binding constants for complexes of α-, β- or γ-CD with Li(+) , Na(+) , K(+) and Rb(+) could be derived directly and quickly. METHODS: The lgKa values between α-, β- or γ-CD and Cs(+) data were processed by curve fitting. These lgKa values were provided as references for competitive ESI-MS. In addition, linear fit equations for complexes of α-, β- or γ-CD with Cs(+) were derived. Through the linear fit equations of competitive ESI-MS, the binding constants for complexes of Li(+) , Na(+) , K(+) and Rb(+) with α-, β- or γ-CD were acquired. RESULTS: Results showed that the binding constant (lgKa ) values for the complexes of Cs(+) with α-, β- and γ-cyclodextrins were 3.94, 3.88 and 3.80, respectively, revealing that the binding strength decreased with the increase in diameter of cyclodextrins. The competitive ESI-MS results showed a clear trend of decreasing affinity for complexes of cyclodextrins in the order of Li(+) , Na(+) , K(+) , Rb(+) . CONCLUSIONS: The binding constants of non-covalent cyclodextrin-alkali cation complexes have been systematically studied by an improved mass spectrometric titration and competitive ESI-MS. Also, the structural features of the complexes were discussed. Our results are valuable for better understanding of mechanisms driving inclusion chemistry under well-defined conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".