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Peptide Tethering: Pocket-Directed Fragment Screening for Peptidomimetic Inhibitor Discovery.
Modell, Ashley E; Marrone, Frank; Panigrahi, Nihar R; Zhang, Yingkai; Arora, Paramjit S.
Affiliation
  • Modell AE; Department of Chemistry, New York University, 100 Washington Square East, New York, New York 10003, United States.
  • Marrone F; Department of Chemistry, New York University, 100 Washington Square East, New York, New York 10003, United States.
  • Panigrahi NR; Department of Chemistry, New York University, 100 Washington Square East, New York, New York 10003, United States.
  • Zhang Y; Department of Chemistry, New York University, 100 Washington Square East, New York, New York 10003, United States.
  • Arora PS; Department of Chemistry, New York University, 100 Washington Square East, New York, New York 10003, United States.
J Am Chem Soc ; 144(3): 1198-1204, 2022 01 26.
Article in En | MEDLINE | ID: mdl-35029987
ABSTRACT
Constrained peptides have proven to be a rich source of ligands for protein surfaces, but are often limited in their binding potency. Deployment of nonnatural side chains that access unoccupied crevices on the receptor surface offers a potential avenue to enhance binding affinity. We recently described a computational approach to create topographic maps of protein surfaces to guide the design of nonnatural side chains [J. Am. Chem. Soc. 2017, 139, 15560]. The computational method, AlphaSpace, was used to predict peptide ligands for the KIX domain of the p300/CBP coactivator. KIX has been the subject of numerous ligand discovery strategies, but potent inhibitors of its interaction with transcription factors remain difficult to access. Although the computational approach provided a significant enhancement in the binding affinity of the peptide, fine-tuning of nonnatural side chains required an experimental screening method. Here we implement a peptide-tethering strategy to screen fragments as nonnatural side chains on conformationally defined peptides. The combined computational-experimental approach offers a general framework for optimizing peptidomimetics as inhibitors of protein-protein interactions.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Peptidomimetics Type of study: Diagnostic_studies / Prognostic_studies / Screening_studies Language: En Journal: J Am Chem Soc Year: 2022 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Peptidomimetics Type of study: Diagnostic_studies / Prognostic_studies / Screening_studies Language: En Journal: J Am Chem Soc Year: 2022 Document type: Article