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A glycopolymer sensor array that differentiates lectins and bacteria [dataset] Open Access

Synthetic materials that recognise bacterial lectins offer an attractive route to the development of new diagnostics, but their realisation is complicated by the generally low selectivity of carbohydrate-protein interactions, which frustrates the design of specific sensors. Here we describe a glycopolymer-based sensor array which can identify a selection of plant- and bacterially- derived lectins with similar carbohydrate recognition preferences through a pattern-based approach. Receptors within the array were generated using a polymer scaffold functionalised with an environmentally-sensitive fluorophore, along with simple carbohydrate recognition units. Exposure to lectins induced changes in the emission profiles of the receptors, enabling the discrimination of analytes via a machine learning approach. The resultant algorithm was used for lectin identification across a range of concentrations, and within complex mixtures of proteins, demonstrating the utility of our approach for the detection of disease-associated lectins in biological environments. The ability of this sensor array to discriminate different strains of pathogenic bacteria was shown, demonstrating the potential application of the sensor array as a rapid diagnostic tool to characterise bacterial infections and identify bacterial virulence factors such as production of adhesins and antibiotic resistance.

Descriptions

Resource type

Dataset

Contributors

Mahon, Clare ¹
Creator: Leslie, Kathryn ¹

¹ Durham University, UK

Funder

Engineering and Physical Sciences Research Council
Marie Sklodowska-Curie Fellowship
UKRI Future Leaders Fellowship

Research methods

Other description

Keyword

Sensor arrays
Glycopolymers
Bacteria
Lectins

Subject

Detectors
Polymers
Bacteria

Location

Language

Cited in

Identifier

ark:/32150/r1js956f86t
doi:10.15128/r1js956f86t

Rights

Creative Commons Attribution 4.0 International (CC BY)

Publisher

Durham University

Date Created

File Details

Depositor

K.G. Leslie

Date Uploaded

3 June 2024, 08:06:36

Date Modified

3 June 2024, 11:06:21

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Audits have not yet been run on this file.

Characterization

File format: zip (ZIP Format)

Mime type: application/zip

File size: 5857

Last modified: 2024:06:03 09:30:43+01:00

Filename: models.zip

Original checksum: 0b49426ab87a4bef895ade4fce2447c1