Peptide arrays for kinome analysis: New opportunities and remaining challenges
Bibliographic record
Abstract
Phosphorylation is the predominant mechanism of post-translational modification for regulation of protein function. With central roles in virtually every cellular process, and strong linkages with many diseases, there is a considerable interest in defining, and ultimately controlling, kinase activities. Investigations of human cellular phosphorylation events, which includes over 500 different kinases and tens of thousands of phosphorylation targets, represent a daunting challenge for proteomic researchers and cell biologists alike. As such, there is a priority to develop tools that enable the evaluation of cellular phosphorylation events in a high-throughput, and biologically relevant, fashion. Towards this objective, two distinct, but functionally related, experimental approaches have emerged; phosphoproteome investigations, which focus on the sub-population of proteins which undergo phosphorylation and kinome analysis, which considers the activities of the kinase enzymes mediating these phosphorylation events. Within kinome analysis, peptide arrays have demonstrated considerable potential as a cost-effective, high-throughput approach for defining phosphorylation-mediated signal transduction activity. In particular, a number of recent advances in the application of peptide arrays for kinome analysis have enabled researchers to tackle increasingly complex biological problems in a wider range of species. In this review, recent advances in kinomic analysis utilizing peptides arrays including several of the biological questions studied by our group, as well as outstanding challenges still facing this technology, are discussed.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".