Construction of a highly selective and sensitive carbohydrate-detecting biosensor utilizing Computational Identification of Non-disruptive Conjugation sites (CINC) for flexible and streamlined biosensor design

Fluorescently-labeled solute-binding proteins that alter their fluorescence output in response to ligand binding have been utilized as biosensors for a variety of applications. Coupling protein ligand binding to altered fluorescence output often requires trial and error-based testing of both multiple labeling positions and fluorophores to produce a functional biosensor with the desired properties. This approach is laborious and can lead to reduced ligand binding affinity or altered ligand specificity. Here we report the Computational Identification of Non disruptive Conjugation sites (CINC) for streamlined identification of fluorophore conjugation sites. By exploiting the structural dynamics properties of proteins, CINC identifies positions where conjugation of a fluorophore results in a fluorescence change upon ligand binding without disrupting protein function. We show that a CINCdeveloped maltooligosaccharide (MOS)-detecting biosensor is capable of rapid (k(on) = 20 mu M(-1)s(-1)), sensitive (sub-mu M K-D) and selective MOS detection. The MOS-detecting biosensor is modular with respect to the spectroscopic properties and demonstrates portability to detecting MOS released via alpha-amylase-catalyzed depolymerization of starch using both a stopped-flow and a microplate reader assay. Our MOS-detecting biosensor represents a first-in-class probe whose design was guided by changes in localized dynamics of individual amino acid positions, supporting expansion of the CINC pipeline as an indispensable tool for a wide range of protein engineering applications.