Differential sensing with arrays of de novo designed peptide assemblies

William M. Dawson (), Kathryn L. Shelley, Jordan M. Fletcher, D. Arne Scott, Lucia Lombardi, Guto G. Rhys, Tania J. LaGambina, Ulrike Obst, Antony J. Burton, Jessica A. Cross, George Davies, Freddie J. O. Martin, Francis J. Wiseman, R. Leo Brady, David Tew, Christopher W. Wood () and Derek N. Woolfson ()
Additional contact information
William M. Dawson: University of Bristol, Cantock’s Close
Kathryn L. Shelley: University of Bristol, Cantock’s Close
Jordan M. Fletcher: University of Bristol, Cantock’s Close
D. Arne Scott: University of Bristol, Cantock’s Close
Lucia Lombardi: University of Bristol, Cantock’s Close
Guto G. Rhys: University of Bristol, Cantock’s Close
Tania J. LaGambina: Rosa Biotech, Science Creates St Philips
Ulrike Obst: Rosa Biotech, Science Creates St Philips
Antony J. Burton: University of Bristol, Cantock’s Close
Jessica A. Cross: University of Bristol, Cantock’s Close
George Davies: University of Bristol, Cantock’s Close
Freddie J. O. Martin: University of Bristol, Cantock’s Close
Francis J. Wiseman: University of Bristol, Cantock’s Close
R. Leo Brady: University of Bristol, Medical Sciences Building, University Walk
David Tew: GlaxoSmithKline (GSK)
Christopher W. Wood: University of Bristol, Cantock’s Close
Derek N. Woolfson: University of Bristol, Cantock’s Close

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling α-helical barrels (αHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As αHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings.

Date: 2023
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DOI: 10.1038/s41467-023-36024-y

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