Deconvolution in mass spectrometry based proteomics
Bibliographic record
Abstract
Mass spectrometry (MS) has played a vital role across a broad range of fields and applications in proteomics. The development of high-resolution MS has significantly advanced biology in areas such as protein structure, function, post-translational modification and global protein dynamics. The two most widely used MS ionization techniques in proteomics are electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI). ESI typically yields multiple charge values for each molecular mass and an isotopic cluster for each nominal mass-to-charge (m/z) value. Although MALDI mass spectra typically contain only singly charged ions, overlapping isotope patterns can be problematic for accurate mass measurement. To overcome these challenges of overlapping isotope patterns associated with complex samples in MS-based proteomics research, deconvolution strategies are being used. This manuscript describes a wide variety of deconvolution strategies, including de-isotoping and de-charging processes, deconvolution of co-eluting isomers or peptides with different sequences in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes, and data analysis in intact protein mass determination, ion mobility MS, native MS, and hydrogen/deuterium exchange MS. It concludes with a discussion of future prospects in the development of bioinformatics and potential new applications in proteomics.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".