Predicting Electrophoretic Mobility of Tryptic Peptides for High-Throughput CZE-MS Analysis
Bibliographic record
Abstract
A multiparametric sequence-specific model for predicting peptide electrophoretic mobility has been developed using large-scale bottom-up proteomic CE-MS data (5% (∼0.8M) acetic acid as background electrolyte). Peptide charge ( Z ) and size (molecular mass, M ) are the two major factors determining electrophoretic mobility, in complete agreement with previous studies. The extended size of the data set (>4000 peptides) permits access to many sequence-specific factors that impact peptide mobility. The presence of acidic residues Asp and Glu near the peptide N-terminus is by far the most prominent among them. The induction effect of the side chain of N-terminal Asp reduces the basicity of the N-terminal amino group and, as hence, its charge, by ∼0.27 units, lowering mobility. The correlation of the model ( R 2 ∼ 0.995) indicates that the peptide separation process in CZE is relatively simple and can be predicted to a much higher precision than current RP-HPLC models. Similar to RP-HPLC prediction studies, we anticipate future developments that introduce peptide migration standards, collect larger data sets for modeling through the alignment of multiple CZE-MS acquisitions, and study of the behavior of peptides carrying post-translational modifications. The increased size of data sets will also permit investigation of the fine-scale effects of peptide secondary structure on peptide mobility. We observed that peptides with higher helical propensity tend to have higher than predicted electrophoretic mobility; the incorporation of these features into CZE migration models will require significantly larger data sets.
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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 | 0.001 |
| 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 | 0.000 |
| 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".