Discovery of protein acetylation patterns by deconvolution of peptide isomer mass spectra
Bibliographic record
Abstract
Protein post-translational modifications (PTMs) play important roles in the control of various biological processes including protein-protein interactions, epigenetics and cell cycle regulation. Mass spectrometry-based proteomics approaches enable comprehensive identification and quantitation of numerous types of PTMs. However, the analysis of PTMs is complicated by the presence of indistinguishable co-eluting isomeric peptides that result in composite spectra with overlapping features that prevent the identification of individual components. In this study, we present Iso-PeptidAce, a novel software tool that enables deconvolution of composite MS/MS spectra of isomeric peptides based on features associated with their characteristic fragment ion patterns. We benchmark Iso-PeptidAce using dilution series prepared from mixtures of known amounts of synthetic acetylated isomers. We also demonstrate its applicability to different biological problems such as the identification of site-specific acetylation patterns in histones bound to chromatin assembly factor-1 and profiling of histone acetylation in cells treated with different classes of HDAC inhibitors.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".