Occurrence and Detection of Phosphopeptide Isomers in Large-Scale Phosphoproteomics Experiments
Bibliographic record
Abstract
The past decade has been marked by the emergence of selective affinity media and sensitive mass spectrometry instrumentation that facilitated large-scale phosphoproteome analyses and expanded the repertoire of protein phosphorylation. Despite these remarkable advances, the precise location of the phosphorylation site still represents a sizable challenge in view of the labile nature of the phosphoester bond and the presence of neighboring phosphorylatable residues within the same peptide. This difficulty is exacerbated by the combinatorial distribution of phosphorylated residues giving rise to different phosphopeptide isomers. These peptides have similar physicochemical properties, and their separation by LC is often problematic. Few studies have described the frequency and distribution of phosphoisomers in large-scale phosphoproteomics experiments, and no convenient informatics tools currently exist to facilitate their detection. To address this analytical challenge, we developed two algorithms to detect separated and co-eluting phosphopeptide isomers and target their subsequent identification using an inclusion list in LC-MS/MS experiments. Using these algorithms, we determined that the proportion of isomers present in phosphoproteomics studies from mouse, rat, and fly cell extracts represents 3-6% of all identified phosphopeptides. While conventional analysis can identify chromatographically separated phosphopeptides, targeted LC-MS/MS analyses using inclusion lists provided complementary identification and expanded the number of phosphopeptide isomers by at least 52%. Interestingly, these analyses revealed that the occurrence of phosphopeptides isomers can also correlate with the presence of extended phosphorylatable amino acids that can act as a "phosphorylation switch" to bind complementary domains such as those present in SR proteins and ribonucleoprotein complexes.
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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 | 0.002 | 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 | 0.001 |
| 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".