Exploring the conformational landscape of a lanthipeptide synthetase using native mass spectrometry
Bibliographic record
Abstract
Abstract Lanthipeptides are ribosomally-synthesized and post-translationally modified peptide (RiPP) natural products that are biosynthesized in a multistep maturation process by enzymes (lanthipeptide synthetases) that possess relaxed substrate specificity. Recent evidence has suggested that some lanthipeptide synthetases are structurally dynamic enzymes that are allosterically activated by precursor peptide binding, and that conformational sampling of the enzyme-peptide complex may play an important role in defining the efficiency and sequence of biosynthetic events. These “biophysical” processes, while critical for defining the activity and function of the synthetase, remain very challenging to study with existing methodologies. Herein, we show that native nanoelectrospray ionization coupled to ion mobility mass spectrometry (nanoESI-IM-MS) provides a powerful and sensitive means for investigating the conformational landscapes and intermolecular interactions of lanthipeptide synthetases. Namely, we demonstrate that the class II lanthipeptide synthetase (HalM2) and its non-covalent complex with the cognate HalA2 precursor peptide can be delivered into the gas phase in a manner that preserves native structures and intermolecular enzyme-peptide contacts. Moreover, gas phase ion mobility studies of the natively-folded ions demonstrate that peptide binding and mutations to dynamic structural elements of HalM2 alter the conformational landscape of the enzyme, and that the precursor peptide itself exhibits higher order structure in the mass spectrometer. Cumulatively, these data support previous claims that lanthipeptide synthetases are structurally dynamic enzymes that undergo functionally relevant conformational changes in response to precursor peptide binding. This work establishes nanoESI-IM-MS as a versatile approach for unraveling the relationships between protein structure and biochemical function in RiPP biosynthetic systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".