Substrate Specificity of Protein Tyrosine Phosphatases 1B, RPTPα, SHP-1, and SHP-2
Bibliographic record
Abstract
We determined the substrate specificities of the protein tyrosine phosphatases (PTPs) PTP1B, RPTPα, SHP-1, and SHP-2 by on-bead screening of combinatorial peptide libraries and solution-phase kinetic analysis of individually synthesized phosphotyrosyl (pY) peptides. These PTPs exhibit different levels of sequence specificity and catalytic efficiency. The catalytic domain of RPTPα has very weak sequence specificity and is approximately 2 orders of magnitude less active than the other three PTPs. The PTP1B catalytic domain has modest preference for acidic residues on both sides of pY, is highly active toward multiply phosphorylated peptides, but disfavors basic residues at any position, a Gly at the pY-1 position, or a Pro at the pY+1 position. By contrast, SHP-1 and SHP-2 share similar but much narrower substrate specificities, with a strong preference for acidic and aromatic hydrophobic amino acids on both sides of the pY residue. An efficient SHP-1/2 substrate generally contains two or more acidic residues on the N-terminal side and one or more acidic residues on the C-terminal side of pY but no basic residues. Subtle differences exist between SHP-1 and SHP-2 in that SHP-1 has a stronger preference for acidic residues at the pY-1 and pY+1 positions and the two SHPs prefer acidic residues at different positions N-terminal to pY. A survey of the known protein substrates of PTP1B, SHP-1, and SHP-2 shows an excellent agreement between the in vivo dephosphorylation pattern and the in vitro specificity profiles derived from library screening. These results suggest that different PTPs have distinct sequence specificity profiles and the intrinsic activity/specificity of the PTP domain is an important determinant of the enzyme's in vivo substrate specificity.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".