Tracking Cell Signaling Protein Expression and Phosphorylation by Innovative Proteomic Solutions
Bibliographic record
Abstract
The most challenging and fruitful biomedical research endeavor of this decade will be the mapping of cell signaling systems and establishing their linkages to normal and disease-related processes. Amongst other things, the Human Genome Sequencing Project has greatly facilitated MALDI-TOF mass spectrometry identification of proteins that have been resolved by standard 2D gel electrophoresis. However, the low abundance of protein kinases and other signal transduction proteins has rendered their analyses particularly problematic without some means of purification and enrichment from cell and tissue lysates. Antibodies have been the most specific affinity probes for tracking target proteins, but their variable quality and high cost preclude their deployment in most discovery-based proteomics studies. Current multi-immunoblotting techniques can permit the probing of a single mini-SDS-PAGE gel with 50 or more antibodies at a time to monitor large changes in the expression and phosphorylation states of signaling proteins. The development of new affinity probes to replace antibodies is necessary to drive large scale proteomics studies. Such affinity probes could include short peptide antibody mimetics (PAM's) and oligonucleotide aptamers that when spotted in 2D array formats (e.g. membrane macroarrays, glass microarrays) or presented on specific beads (e.g. Luminex beads) can capture target proteins for their specific enrichment. The bound target proteins can then be detected using reporter antibodies or other specific probes for their quantitation by high throughput systems. These new proteomics methodologies will accelerate assessment of specific protein expression, post-translational modification, protein-protein interactions and protein-drug interactions to provide a more holistic view of cellular operations and how they might be manipulated under pathological circumstances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".