Fragmentation Reactions of Complexes of Alkali Metal Ions with Pentaserine,Pentaleucine and Pentalysine in Gas Phase
Bibliographic record
Abstract
For exploring the effects of alkali metal ions on the dissociations of peptides with different side chains in the gas phase,the complexes of Li +,Na +,K +,Rb + and Cs + with pentapeptides,Ser-Ser-Ser-Ser-Ser( S5), Leu-Leu-Leu-Leu-Leu( L5) and Lys-Lys-Lys-Lys-Lys( K5),were chosen to investigate the fragmentation reaction pathways by mass spectrometry.The experimental results indicated that alkali metal ions and S5,L5,K5 can form 1∶ 1 and 2∶ 1 non-covalent complexes in the gas phase,and the binding strength between alkali metal ions and pentapeptides descend with the increasing of the radii of alkali metal ions.Quantitative analyses of mass spectra demonstrated that the binding constants for the complexes of K + with S5, L5 and K5 are 8.94 × 10 4, 2.83 × 10 4 and 2.50 × 10 3 L/mol, respectively, revealing that the binding strength of complexes decrease in order of S5, L5 and K5.The analyses of the fragment ions arising from the complexes of alkali metal ions with pentapeptides showed that the dissociations mainly occur in the backbones of the peptides.Among three pentapeptides, the complexes of pentaserines are the easiest to fragment, pentaleucines take second place,while pentalysines are the hardest.The dissociations also occur in the side chains,and there are significant differences among the three complexes.Moreover,the relationships among various fragment ions arising from the complex of Na + with L5 were tested.Based on the theoretical model of Dunbar,it was proposed that in the fragmentation process, the alkali metal ion may shift to the N- or C-terminal of the pentapeptide.In 2∶ 1 complex, the first alkali metal ion may conjugate to the carbonyl groups of four amides, while the second one may conjugate to the oxygen atoms of carboxyl group of the pentapeptide.
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) | 0.002 | 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".