Synthesis of small peptide compounds, molecular docking, and inhibitory activity evaluation against phosphatases PTP1B and SHP2
Bibliographic record
Abstract
Background: The protein tyrosine phosphatases PTP1B and SHP2 are promising drug targets in treatment design for breast cancer. Searching for specific inhibitors of their activity has recently become the challenge of many studies. Previous work has indicated that the promising PTP inhibitors may be small compounds that are able to bind and interact with amino residues from the binding site. Purpose: The main goal of our study was to synthesize and analyze the effect of selected small peptide inhibitors on oncogenic PTP1B and SHP2 enzymatic activity and viability of MCF7 breast cancer cells. We also performed computational analysis of peptides binding with allosteric sites of PTP1B and SHP2 phosphatases. Methods: We measured the inhibitory activity of compounds utilizing recombinant enzymes and MCF7 cell line. Computational analysis involved docking studies of binding conformation and interactions of inhibitors with allosteric sites of phosphatases. Results: The results showed that the tested compounds decrease the enzymatic activity of phosphatases PTP1B and SHP2 with IC 50 values in micromolar ranges. We observed higher inhibitory activity of dipeptides than tripeptides. Phe-Asp was the most effective against SHP2 enzymatic activity, with IC 50 =5.2±0.4 µM. Micromolar concentrations of tested dipeptides also decreased the viability of MCF7 breast cancer cells, with higher inhibitory activity observed for the Phe-Asp peptide. Moreover, the peptides tested were able to bind and interact with allosteric sites of PTP1B and SHP2 phosphatases. Conclusion: Our research showed that small peptide compounds can be considered for the design of specific inhibitors of oncogenic protein tyrosine phosphatases. Keywords: breast cancer, protein tyrosine phosphatase PTP1B, SHP2, peptides, PTP inhibitors
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".