Bioanalysis for Biocatalysis: Multiplexed Capillary Electrophoresis–Mass Spectrometry Assay for Aminotransferase Substrate Discovery and Specificity Profiling
Bibliographic record
Abstract
In this work, we introduce an entirely automated enzyme assay based on capillary electrophoresis coupled to electrospray ionization mass spectrometry termed MINISEP-MS for multiple interfluent nanoinjections-incubation-separation-enzyme profiling using mass spectrometry. MINISEP-MS requires only nanoliters of reagent solutions and uses the separation capillary as a microreactor, allowing multiple substrates to be assayed simultaneously. The method can be used to rapidly profile the substrate specificity of any enzyme and to measure steady-state kinetics in an automated fashion. We used the MINISEP-MS assay to profile the substrate specificity of three aminotransferases (E. coli aspartate aminotransferase, E. coli branched-chain amino acid aminotransferase, and Bacillus sp. YM-1 D-amino acid aminotransferase) for 33 potential amino acid substrates and to measure steady-state kinetics. Using MINISEP-MS, we were able to recapitulate the known substrate specificities and to discover new amino acid substrates for these industrially relevant enzymes. Additionally, we were able to measure the apparent K(M) and k(cat) parameters for amino acid donor substrates of these aminotransferases. Because of its many advantages, the MINISEP-MS assay has the potential of becoming a useful tool for researchers aiming to identify or create novel enzymes for specific biocatalytic applications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".