Crystallographic analysis of response regulator protein from<i>Desulfovibrio vulgaris</i>Hildenborough
Bibliographic record
Abstract
Cells of the immune system have inherently high levels of tyrosine phosphorylation.In fact, they express more genes encoding protein tyrosine kinases (PTKs) and protein tyrosine phosphatases (PTPs) than nearly any other cell type.One PTP expressed exclusively in cells of the immune system is hematopoietic protein tyrosine phosphatase (HePTP).HePTP functions to negatively regulate the activation of T cells, and it does so by dephosphorylating and inactivating its only known substrates, the mitogen-activated protein kinases (MAPKs) Erk1/2 and p38.The importance of this regulation is highlighted by the fact that dysregulation of HePTP is associated with several diseases of the immune system, including acute myelogenous leukemia and non-Hodgkins lymphoma.In order to understand the molecular basis of the HePTP:MAPK interaction, we have generated a series of HePTP substrate-trapping mutants (STMs) in order to selectively populate an HePTP:MAPK peptide dephosphorylation complex.STMs have low catalytic activities yet retain high affinities for their substrates.We first determined the biochemical and biophysical characteristics of these HePTP STMs to identify those STMs most suitable for structural studies.We then crystallized and determined the structures of several HePTP STMs in both their apo forms and in complex with two distinct substrate peptide mimetics, e.g.peptides that correspond to the singly-and dually-phosphorylated activation loop of the MAPK Erk2.Finally, we describe how this biochemical, biophysical and structural data has allowed us to identify, for the first time, the structural features of HePTP that confer such a high degree of specificity for its MAPK substrates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".