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

Sensing preferences for prokaryotic solute binding protein families

Solute binding proteins (SBPs) are of central physiological relevance for prokaryotes. These proteins present substrates to transporters, but they also stimulate different signal transduction receptors. SBPs form a superfamily of at least 33 protein Pfam families. To assess possible links between SBP sequence and the ligand recognized, we have inspected manually all SBP three-dimensional structures deposited in the protein data bank and retrieved 748 prokaryotic structures that have been solved in complex with bound ligand. These structures were classified into 26 SBP Pfam families. The analysis of the ligands recognized revealed that most families possess a preference for a compound class. There were three families each that bind preferentially saccharides and amino acids. In addition, we identified families that bind preferentially purines, quaternary amines, iron and iron-chelating compounds, oxoanions, bivalent metal ions or phosphates. Phylogenetic analyses suggest convergent evolutionary events that lead to families that bind the same ligand. The functional link between chemotaxis and compound uptake is reflected in similarities in the ligands recognized by SBPs and chemoreceptors. Associating Pfam families with ligand profiles will be of help to design experimental strategies aimed at the identification of ligands for uncharacterized SBPs.

Development of a Real-Time Pectic Oligosaccharide-Detecting Biosensor Using the Rapid and Flexible Computational Identification of Non-Disruptive Conjugation Sites (CINC) Biosensor Design Platform

Fluorescently labeled, solute-binding proteins that change their fluorescent output in response to ligand binding are frequently used as biosensors for a wide range of applications. We have previously developed a "Computational Identification of Non-disruptive Conjugation sites" (CINC) approach, an in silico pipeline utilizing molecular dynamics simulations for the rapid design and construction of novel protein-fluorophore conjugate-type biosensors. Here, we report an improved in silico scoring algorithm for use in CINC and its use in the construction of an oligogalacturonide-detecting biosensor set. Using both 4,5-unsaturated and saturated oligogalacturonides, we demonstrate that signal transmission from the ligand-binding pocket of the starting protein scaffold to the CINC-selected reporter positions is effective for multiple different ligands. The utility of an oligogalacturonide-detecting biosensor is shown in Carbohydrate Active Enzyme (CAZyme) activity assays, where the biosensor is used to follow product release upon polygalacturonic acid (PGA) depolymerization in real time. The oligogalacturonide-detecting biosensor set represents a novel enabling tool integral to our rapidly expanding platform for biosensor-based carbohydrate detection, and moving forward, the CINC pipeline will continue to enable the rational design of biomolecular tools to detect additional chemically distinct oligosaccharides and other solutes.