Automating variant peptide target selection to streamline proteogenomic assay development with Mass Spectrometry
Bibliographic record
Abstract
The quantification of protein variants produced in cancer cells is valuable as it can provide important insights into the processes underlying tumorigenesis. Quantifying protein variants using targeted MS requires the development of one assay per variant/protein. The selection of a suitable surrogate peptide from a parent protein of interest is an important first step in assay development, but it is often highly laborious and becomes impractical when attempting to scale the production of these assays. To solve this issue, we developed a tool built in R to streamline the selection of ideal variant peptide targets for quantification. This pipeline generates variant peptide sequences from transcripts and mutations coded in the Human Genome Variation Society (HGVS) nomenclature for cDNA and proteins (HGVSc and HGVSp, respectively). The pipeline also evaluates peptides using various selection criteria for target suitability, including factors such as ease of synthesis, peptide uniqueness, proteotypicity, and peptide stability. Validation of this pipeline was done by predicting variant peptides from sequencing data in human cancer cell lines (MOLT-4, MCF-7, A549, CCRF-CEM, COLO-205, NCI-H226, NCI-H23, RPMI-8226, and T47D) and comparing this list to peptides observed experimentally using 4D deep proteomics experiments using offline high pH fractionation with LC-timsTOF. Of the predicted variant peptides, 2.3–16.6% were observed experimentally between the 9 cell lines. Additionally, this pipeline was used to develop QuaVaProt, an online knowledge base, by applying it to the vast list of mutations in the National Cancer Institute Genomic Data Commons (NCI-GDC) and Catalogue of Somatic Mutations in Cancer (COSMIC). QuaVaProt catalogues the mutations and predicted peptide variants, and many annotations for searching through a user-friendly interface. We note that while overview statistics are made available for both the NCI-GDC and COSMIC datasets, only the former is searchable in QuaVaProt at this time. By hosting 1,702,821 annotated variant peptides from the NCI-GDC dataset for searching, we hope that our work will help streamline the development of assays targeting variant proteins and be a valuable resource for the proteogenomics community
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".