Effects of Multidentate Metal Interactions on the Structure of Collisionally Activated Proteins: Insights from Ion Mobility Spectrometry and Molecular Dynamics Simulations
Bibliographic record
Abstract
Much remains to be learned about the way in which bound metal ions modulate the response of electrosprayed proteins and protein complexes to collisional excitation. Nonspecific metal adducts can affect the extent of collision-induced unfolding (CIU) and collision-induced dissociation (CID). Here, we examine how Na(+) and Ca(2+) adducts alter the CIU response of monomeric proteins under native electrospray conditions. Both of these metals are commonly encountered in biological samples. Measured collision cross sections are largely independent of metal adduction as long as in-source excitation is minimized. In contrast, under CIU conditions, the metal-adducted proteins are markedly more compact than their metal-free counterparts. This phenomenon is particularly pronounced for Ca(2+) binding, but Na(+) adducts have significant effects as well. Molecular dynamics simulations reproduce the experimentally observed trends. The simulations show that structural expansion of the collisionally unfolded proteins is limited by multidentate metal contacts that restrict the conformational freedom of the polypeptide chains. Multidentate interactions with carboxylates and other electron-rich moieties are to be anticipated for divalent metals such as Ca(2+). It is surprising that Na(+) also engages in multidentate ligation. Electrostatic mapping reveals that the propensity of both Na(+) and Ca(2+) to interact with multiple electron-rich groups is caused by ineffective charge shielding during ion pairing. Despite their compactness, the CIU structures of metalated proteins do not retain native-like elements. Instead, CIU generates inside-out conformations where previously surface-exposed hydrophilic side chains get buried along with most of the metal ions. Our findings caution that the observation of compact conformers after collisional excitation does not imply the survival of solution-like structural features. We also discuss possible implications of adduct-mediated effects for CIU fingerprinting studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".