Detection of noncovalent complex between ?-amylase and its microbial inhibitor tendamistat by electrospray ionization mass spectrometry
Bibliographic record
Abstract
Electrospray ionization mass spectrometry (ESI-MS) is now routinely used for detection of noncovalent complexes. However, detection of noncovalent protein-protein complexes is not a widespread practice and still produces some challenges for mass spectrometrists. Here we demonstrate the detection of a noncovalent protein-protein complex between alpha-amylase and its microbial inhibitor tendamistat using ESI-MS. Crude porcine pancreatic alpha-amylase was purified using a glycogen precipitation method. Noncovalent complexes between porcine pancreatic alpha-amylase and its microbial inhibitor tendamistat are probed and detected using ESI-MS. The atmosphere-vacuum ESI conditions along with solution conditions and the ratio of inhibitor over enzyme strongly affect the detection of noncovalent complexes in the gas phase. ESI mass spectra of alpha-amylase at pH 7 exhibited charge states significantly lower than that reported previously, which is indicative of a native protein conformation necessary to produce a noncovalent complex. Detection of noncovalent complexes in the gas phase suggests that further use of conventional biochemical approaches to provide a qualitative, and in some cases even quantitative, characterization of equilibria of noncovalent complexes in solution is possible.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".