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

Robert E. Jefferson, Aurélien Oggier, Andreas Füglistaler, Nicolas Camviel, Mahdi Hijazi, Ana Rico Villarreal, Caroline Arber and Patrick Barth ()
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Robert E. Jefferson: École Polytechnique Fédérale de Lausanne
Aurélien Oggier: École Polytechnique Fédérale de Lausanne
Andreas Füglistaler: École Polytechnique Fédérale de Lausanne
Nicolas Camviel: Ludwig Institute for Cancer Research Lausanne
Mahdi Hijazi: École Polytechnique Fédérale de Lausanne
Ana Rico Villarreal: École Polytechnique Fédérale de Lausanne
Caroline Arber: Ludwig Institute for Cancer Research Lausanne
Patrick Barth: École Polytechnique Fédérale de Lausanne

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-38491-9

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