Native Taylor/Non‐Taylor Dispersion–Mass Spectrometry (TNT‐MS) Allows Rapid Protein Desalting and Multiplexed, Label‐Free Ligand Screening
Bibliographic record
Abstract
Native mass spectrometry (MS) is an important technique in structural biology and drug discovery, due to its ability to study non-covalent assemblies in the gas phase. Drawbacks include the incompatibility of electrospray ionization (ESI) with non-volatile salts and the risk of protein signal suppression by small molecules. Overcoming these often requires offline buffer exchange and/or parallel sample preparation to other methods, reducing the adoption and throughput of native MS. Here, we exploit the dynamics of analytes flowing through an open tubular capillary to keep molecules with a small hydrodynamic radius (e.g., salts) inside a Taylor dispersion regime while pushing larger species (e.g., proteins) into a non-Taylor regime. As such, larger species elute earlier, and are effectively buffer exchanged within the capillary in seconds. In addition to desalting of proteins injected in biologically relevant buffers we demonstrate separation of unbound small molecules from protein-ligand complexes, enabling multiplexed ligand screening. Finally, we investigated the dependence of the critical flow rate for non-Taylor behavior on protein size, enabling limited size-based separation of proteins. Taylor/non-Taylor dispersion mass spectrometry (TNT-MS) was implemented using an unmodified liquid chromatography - mass spectrometry (LC-MS) system operated without a chromatographic column and coupled to an autosampler, which allowed significant automation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".