An Intracellular Peptide Library Screening Platform Identifies Irreversible Covalent Transcription Factor Inhibitors

Andrew Brennan1, Scott Lovell1, Keith W Vance1

  • 1Department of Life Sciences, University of Bath, Bath, BA2 7AY, UK.

Insights

Researchers developed a new screening platform to find covalent inhibitors of transcription factors (TFs). This method identified a potent peptide that irreversibly blocks the oncogenic TF cJun, inhibiting cancer cell viability.

Area of Science:

  • Molecular Biology
  • Drug Discovery
  • Chemical Biology

Background:

  • The Transcription Block Survival (TBS) assay was previously used to find reversible antagonists of the transcription factor (TF) cJun.
  • Existing methods lack the ability to identify covalent inhibitors and ensure high selectivity.

Purpose of the Study:

  • To expand the TBS methodology for identifying covalent and highly selective TF inhibitors.
  • To develop a peptide inhibitor targeting the oncogenic TF cJun with enhanced efficacy.

Main Methods:

  • Screening of a 131,072-member peptide library with cysteine options for disulfide bond formation.
  • Utilizing a non-reducing cell line to identify specific cysteine residues for covalent modification.
  • Substituting cysteine with an electrophile to create irreversible covalent inhibitors.

Main Results:

  • A single cysteine residue was identified for disulfide bond formation with cJun C269, leading to increased potency.
  • A selective covalent cJun inhibitor was generated, capable of penetrating melanoma cells and reducing oncogenic cJun levels.
  • The novel inhibitor demonstrated enhanced efficacy compared to previous cJun-targeting peptides.

Conclusions:

  • The enhanced covalent-TBS screening pipeline is a robust method for identifying ligandable cysteines on protein surfaces.
  • This approach yields potent, selective covalent antagonists with precisely positioned reactive groups.
  • The developed inhibitor effectively targets oncogenic cJun, inhibiting cell viability in melanoma models.