Matrix Effect on In-Source Decay Products of Peptides in Matrix-Assisted Laser Desorption/Ionization
Bibliographic record
Abstract
MALDI-ISD of peptides were studied using several salicylic acid derivatives, 2,5-dihydroxybenzoic acid (2,5-DHB), 5-aminosalicylic acid (5-ASA), 5-formylsalicylic acid (5-FSA), and 5-nitrosalicylic acid (5-NSA) as matrices. The difference in the nature of the functional group at the 5-position in the salicylic acid derivatives can dramatically affect the ISD products. The use of 2,5-DHB and 5-ASA leads to "hydrogen-abundant" peptide radicals and subsequent radical-induced N-Cα bonds cleavage. N-Cα bond cleavage gave a c'/z (·) fragment pair and radical z (·)-series fragments gain a hydrogen radical or react with a matrix radical. In contrast, the use of 5-NSA resulted in the production of a "hydrogen-deficient" peptide radical that contained a radical site on the amide nitrogen in the peptide backbone. Subsequently, the radical site on the amide nitrogen induces Cα-C bond dissociation, leading to a (·)/x fragment pair. The a (·)-series ions undergo further hydrogen abstraction to form a-series ions after Cα-C bond cleavage. Since the Pro residue does not contain a nitrogen-centered radical site, Cα-C bond cleavage does not occur. Alternatively, the specific cleavage of CO-N bonds leads to a b (·)/y fragment pair at Xxx-Pro which occurs via hydrogen abstraction from the Cα-H in the Pro residue. The use of 5-FSA generated both a (·)/x- and c'/z (·)-series fragment pairs. An oxidizing matrix provides useful complementary information in MALDI-ISD compared to a reducing matrix for the analysis of amino acid sequencing and site localization in cases of phosphopeptides. MALDI-ISD, when used in conjunction with both reducing and oxidizing matrices is a potentially useful method for de novo peptide sequencing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".