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

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.