Genome to Kinome: Species-Specific Peptide Arrays for Kinome Analysis
Bibliographic record
Abstract
Tools for conducting high-throughput kinome analysis do not exist for many species. For example, two commonly used techniques for monitoring phosphorylation events are phosphorylation-specific antibodies and peptide arrays. The majority of phosphorylation-specific antibodies are for human or mouse targets, and the construction of peptide arrays relies on information from phosphorylation databases, which are similarly biased toward human and mouse data. This is a substantial obstacle because many species other than mouse represent important biological models. On the basis of the observation that phosphorylation events are often conserved across species with respect to their relative positioning within proteins and their biological function, we demonstrate that it is possible to predict the sequence contexts of phosphorylation events in other species for the production of peptide arrays for kinome analysis. Through this approach, genomic information can be rapidly used to create inexpensive, customizable, species-specific peptide arrays for high-throughput kinome analysis. We anticipate that these arrays will be valuable for investigating the conservation of biological responses across species, validating animal models of disease, and translating research to clinical applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".