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Computational design of dynamic receptor—peptide signaling complexes applied to chemotaxis

Author

Listed:
  • Robert E. Jefferson

    (École Polytechnique Fédérale de Lausanne
    Ludwig Institute for Cancer Research Lausanne)

  • Aurélien Oggier

    (École Polytechnique Fédérale de Lausanne
    Ludwig Institute for Cancer Research Lausanne)

  • Andreas Füglistaler

    (École Polytechnique Fédérale de Lausanne
    Ludwig Institute for Cancer Research Lausanne)

  • Nicolas Camviel

    (Ludwig Institute for Cancer Research Lausanne
    University of Lausanne (UNIL))

  • Mahdi Hijazi

    (École Polytechnique Fédérale de Lausanne
    Ludwig Institute for Cancer Research Lausanne)

  • Ana Rico Villarreal

    (École Polytechnique Fédérale de Lausanne
    Ludwig Institute for Cancer Research Lausanne)

  • Caroline Arber

    (Ludwig Institute for Cancer Research Lausanne
    University of Lausanne (UNIL))

  • Patrick Barth

    (École Polytechnique Fédérale de Lausanne
    Ludwig Institute for Cancer Research Lausanne)

Abstract

Engineering protein biosensors that sensitively respond to specific biomolecules by triggering precise cellular responses is a major goal of diagnostics and synthetic cell biology. Previous biosensor designs have largely relied on binding structurally well-defined molecules. In contrast, approaches that couple the sensing of flexible compounds to intended cellular responses would greatly expand potential biosensor applications. Here, to address these challenges, we develop a computational strategy for designing signaling complexes between conformationally dynamic proteins and peptides. To demonstrate the power of the approach, we create ultrasensitive chemotactic receptor—peptide pairs capable of eliciting potent signaling responses and strong chemotaxis in primary human T cells. Unlike traditional approaches that engineer static binding complexes, our dynamic structure design strategy optimizes contacts with multiple binding and allosteric sites accessible through dynamic conformational ensembles to achieve strongly enhanced signaling efficacy and potency. Our study suggests that a conformationally adaptable binding interface coupled to a robust allosteric transmission region is a key evolutionary determinant of peptidergic GPCR signaling systems. The approach lays a foundation for designing peptide-sensing receptors and signaling peptide ligands for basic and therapeutic applications.

Suggested Citation

  • Robert E. Jefferson & Aurélien Oggier & Andreas Füglistaler & Nicolas Camviel & Mahdi Hijazi & Ana Rico Villarreal & Caroline Arber & Patrick Barth, 2023. "Computational design of dynamic receptor—peptide signaling complexes applied to chemotaxis," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38491-9
    DOI: 10.1038/s41467-023-38491-9
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    References listed on IDEAS

    as
    1. Yipin Lei & Shuya Li & Ziyi Liu & Fangping Wan & Tingzhong Tian & Shao Li & Dan Zhao & Jianyang Zeng, 2021. "A deep-learning framework for multi-level peptide–protein interaction prediction," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
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