Screening for Noncovalent Ligand−Receptor Interactions by Electrospray Ionization Mass Spectrometry-Based Diffusion Measurements
Bibliographic record
Abstract
The application of a novel method for the identification of low-molecular-weight noncovalent ligands to a macromolecular target is reported. This technique is based on the measurement of analyte diffusion coefficients by electrospray mass spectrometry (ESI-MS) (Clark et al., Rapid Commun. Mass Spectrom. 2002, 16, 1454-1462). Potential ligands have large diffusion coefficients as long as they are free in solution. Binding to a macromolecular target, however, drastically reduces the diffusional mobility of any ligand species. Mixtures containing six different saccharides [ribose, rhamnose, glucose, maltose, maltotriose, and N,N',N''-triacetylchitotriose (NAG(3))] were screened for noncovalent binding to lysozyme. Of these six compounds, only NAG(3) is known to bind to the protein. In "direct" binding tests, NAG(3) shows a significantly reduced diffusion coefficient in the presence of the protein. No changes were observed for any of the other saccharides. In a second set of experiments, the use of a "competition" screening method was explored in which mixtures of candidate saccharides were tested for their ability to displace a reference ligand from the target. The addition of NAG(3)-containing mixtures significantly increased the diffusion coefficient of the reference ligand NAG(4) (N,N',N'',N'''-tetraacetylchitotetrose), whereas mixtures that did not contain NAG(3) had no effect. These data clearly indicate the potential of ESI-MS-based diffusion measurements as a novel tool to screen compound libraries for binding to proteins and other macromolecular targets. In contrast to conventional ESI-MS-based ligand-receptor binding studies, this method does not rely on the preservation of noncovalent interactions in the gas phase.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".