Radical Fragment Ions in Collision-Induced Dissociation Mass Spectrometry
Bibliographic record
Abstract
Collision-induced dissociation (CID) is a common fragmentation strategy in mass spectrometry (MS) analysis. A conventional understanding is that fragment ions generated in low-energy CID should follow the even-electron rule. As such, (de)protonated precursor ions should predominately generate (de)protonated fragment ions with very few radical fragment ions (RFIs). However, the extent to which RFIs present in MS2 spectra has not been comprehensively investigated. This work uses the latest NIST 20 tandem mass spectral library to investigate of the occurrence of RFIs in CID MS2 experiments. In particular, RFIs were recognized using their integer double bond equivalent (DBE) values calculated from their annotated molecular formulas. Our study shows unexpected results as 65.4% and 68.8% of MS2 spectra contain at least 10% RFIs by ion-count (total number of ions) in positive and negative electrospray ionization (ESI) modes, respectively. Furthermore, we classified chemicals based on their compound classes and chemical substructures, and calculated the percentages of RFIs in each class. Results show that “Organic 1,3-dipolar compounds” and “Lignans, neolignans and related compounds” are the top 2 compound superclasses which tend to produce RFIs in their CID MS2 spectra. Moreover, aromatic, arylbromide, heteroaromatic, alkylarylether, phenol, and conjugated double bond-containing chemicals are more likely to produce RFIs. We also found four possible patterns of change in RFI percentages as a function of CID collision energy. Finally, we demonstrate that the inadequate consideration of RFIs in most conventional bioinformatic tools might cause problems during in silico fragmentation and de novo annotation of MS2 spectra. This work provides a further understanding of CID MS2 mechanism, and the unexpectedly large percentage of RFIs suggests a need for consideration in the development of bioinformatic software for MS2 interpretation.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".