Peptide Library Approach to Uncover Phosphomimetic Inhibitors of the BRCA1 C-Terminal Domain
Bibliographic record
Abstract
Many intracellular protein-protein interactions are mediated by the phosphorylation of serine, and phosphoserine-containing peptides can inhibit these interactions. However, hydrolysis of the phosphate by phosphatases, and the poor cell permeability associated with phosphorylated peptides has limited their utility in cellular and in vivo contexts. Compounding the problem, strategies to replace phosphoserine in peptide inhibitors with easily accessible mimetics (such as Glu or Asp) routinely fail. Here, we present an in vitro selection strategy for replacement of phosphoserine. Using mRNA display, we created a 10 trillion member structurally diverse unnatural peptide library. From this library, we found a peptide that specifically binds to the C-terminal domain (BRCT)2 of breast cancer associated protein 1 (BRCA1) with an affinity comparable to phosphorylated peptides. A crystal structure of the peptide bound reveals that the pSer-x-x-Phe motif normally found in BRCA1 (BRCT)2 binding partners is replaced by a Glu-x-x-4-fluoroPhe and that the peptide picks up additional contacts on the protein surface not observed in cognate phosphopeptide binding. Expression of the peptide in human cells led to defects in DNA repair by homologous recombination, a process BRCA1 is known to coordinate. Overall, this work validates a new in vitro selection approach for the development of inhibitors of protein-protein interactions mediated by serine phosphorylation.
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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 | 0.000 |
| 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) | 0.001 | 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".