Using focused structural proteomics to elucidate the molecular basis of MAPK regulation in T cells
Bibliographic record
Abstract
Comprehensive molecular insights into a specific disease most often require whole pathways and processes to be considered.Structural biology together with complementing biochemical studies is the major means for achieving detailed insight into the molecular mechanisms of proteins in such pathways: how they interact, how they are regulated, and how enzymes recognize and transform substrates.This information provides a knowledge basis for current target drug design efforts and the structural information can directly assist in the rational drug design cycle.The Structural Genomics Consortium (SGC) is an Anglo-Canadian-Swedish consortium pursuing a systematic effort at generating structural insights into proteins of disease related pathways and structural families.At the SGC-Stockholm node, "the little brother" in the consortium, some 430 proteins are currently studied within areas such as; receptor signaling (Toll-, TGF-beta-and RTK-receptor based signalling), apoptosis signaling, phosphoinositol and other lipid signaling, ATPases (RNA-helicases and AAA-ATPases), poly-ADP ribose polymerase, as well as nucleotide and amino acid metabolism.Many of the proteins targeted are implied in diseases such as; cancer, inflammatory and infectious diseases.Approximately 70 novel human structures, plus follow-up structures, have been determined in the last three years at SGC-Stockholm.The specific structural genomic strategy applied on some of the pathways and families motioned above will be discussed, as well as examples of structural insights generated by this strategy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".