Residue Modification and Mass Spectrometry for the Investigation of Structural and Metalation Properties of Metallothionein and Cysteine-Rich Proteins
Bibliographic record
Abstract
Structural information regarding metallothioneins (MTs) has been hard to come by due to its highly dynamic nature in the absence of metal-thiolate cluster formation and crystallization difficulties. Thus, typical spectroscopic methods for structural determination are limited in their usefulness when applied to MTs. Mass spectrometric methods have revolutionized our understanding of protein dynamics, structure, and folding. Recently, advances have been made in residue modification mass spectrometry in order to probe the hard-to-characterize structure of apo- and partially metalated MTs. By using different cysteine specific alkylation reagents, time dependent electrospray ionization mass spectrometry (ESI-MS), and step-wise "snapshot" ESI-MS, we are beginning to understand the dynamics of the conformers of apo-MT and related species. In this review we highlight recent papers that use these and similar techniques for structure elucidation and attempt to explain in a concise manner the data interpretations of these complex methods. We expect increasing resolution in our picture of the structural conformations of metal-free MTs as these techniques are more widely adopted and combined with other promising tools for structural elucidation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".