Department of Chemistry, Durham University, Durham DH1 3LE, U.K.
School of Chemistry University of Sydney, Sydney, NSW 2006, Australia.
Biomacromolecules. 2024 Nov 11;25(11):7466-7474. doi: 10.1021/acs.biomac.4c01129. Epub 2024 Oct 18.
Identification of bacterial lectins offers an attractive route to the development of new diagnostics, but the design of specific sensors is complicated by the low selectivity of carbohydrate-lectin interactions. Here we describe a glycopolymer-based sensor array which can identify a selection of lectins with similar carbohydrate recognition preferences through a pattern-based approach. Receptors were generated using a polymer scaffold functionalized with an environmentally sensitive fluorophore, along with simple carbohydrate motifs. Exposure to lectins induced changes in the emission profiles of the receptors, enabling the discrimination of analytes using linear discriminant analysis. The resultant algorithm was used for lectin identification across a range of concentrations and within complex mixtures of proteins. The sensor array was shown to discriminate different strains of pathogenic bacteria, demonstrating its potential application as a rapid diagnostic tool to characterize bacterial infections and identify bacterial virulence factors such as production of adhesins and antibiotic resistance.
鉴定细菌凝集素为开发新的诊断方法提供了一条有吸引力的途径，但由于碳水化合物-凝集素相互作用的选择性较低，因此设计特定的传感器变得复杂。在这里，我们描述了一种基于糖聚合物的传感器阵列，通过基于模式的方法可以识别具有相似碳水化合物识别偏好的选择凝集素。受体是使用功能化有环境敏感荧光团的聚合物支架以及简单的碳水化合物基序生成的。与凝集素接触会引起受体发射谱的变化，从而可以使用线性判别分析对分析物进行区分。使用该算法在一系列浓度和复杂的蛋白质混合物中进行了凝集素识别。该传感器阵列能够区分不同的致病菌菌株，证明其具有作为快速诊断工具的潜在应用，可用于表征细菌感染并识别细菌毒力因子，如黏附素的产生和抗生素耐药性。
