Positional proteomics: is the technology ready to study clinical cohorts?
Bibliographic record
Abstract
INTRODUCTION: Positional proteomics provides proteome-wide information on protein termini and their modifications, uniquely enabling unambiguous identification of site-specific, limited proteolysis. Such proteolytic cleavage irreversibly modifies protein sequences resulting in new proteoforms with distinct protease-generated neo-N and C-termini and altered localization and activity. Misregulated proteolysis is implicated in a wide variety of human diseases. Protein termini, therefore, constitute a huge, largely unexplored source of specific analytes that provides a deep view into the functional proteome and a treasure trove for biomarkers. AREAS COVERED: We briefly review principal approaches to define protein termini and discuss recent advances in method development. We further highlight the potential of positional proteomics to identify and trace specific proteoforms, with a focus on proteolytic processes altered in disease. Lastly, we discuss current challenges and potential for applying positional proteomics in biomarker and pre-clinical research. EXPERT OPINION: Recent developments in positional proteomics have provided significant advances in sensitivity and throughput. In-depth analysis of proteolytic processes in clinical cohorts thus appears feasible in the near future. We argue that this will provide insights into the functional state of the proteome and offer new opportunities to utilize proteolytic processes altered or targeted in disease as specific diagnostic, prognostic and companion biomarkers.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".