Tunable GUMBOS-based sensor array for label-free detection and discrimination of proteins

There is increasing interest in developing new sensor strategies for accurate detection and identification of proteins, primarily due to their significance in various biological processes. In this regard, fluorometric sensors and sensor arrays have been widely explored as facile and inexpensive analytical tools. In this manuscript, we report a sensor array approach, based on a novel group of 6-(p-toluidino)-2-naphthalenesulfonate (TNS)-based organic salts for sensitive and label-free sensing of proteins. In this proof-of-concept study, three proteins (human serum albumin (HSA), alpha-antitrypsin and beta-lactoglobulin (beta-lac)) and binary mixtures of HSA and alpha-antitrypsin were used to evaluate the senor array performance. The fingerprint sensor-response patterns, dependent on proteins and protein mixtures at different concentrations, generated from the TNS-based sensors were employed to develop predictive models using principal component analysis (PCA) and linear discriminant analysis (LDA). Interestingly, there is excellent correlation between clustering patterns of PCA and different concentrations of proteins and protein mixtures, which was employed for discrimination of proteins and protein mixtures regardless of concentrations. Furthermore, identification accuracy of the proposed fluorescence-sensor technique, towards discrimination of various concentrations of proteins and protein mixtures, was calculated to be 100%. Overall, this sensor strategy is found to be a very promising tool for accurate discrimination of absolute and relative concentrations of proteins and protein mixtures.