Abstract
Genetically encoded biosensors enable the monitoring of metabolite dynamics in living organisms. We present CoBiSe, a computational biosensor design approach using Constraint Network Analysis to identify optimal insertion sites for reporter modules in molecular recognition elements (MREs). Applied to the iron-binding protein DtxR from Corynebacterium glutamicum, CoBiSe identified a flexible connective loop (residues 138-150) for inserting the reporter module, resulting in IronSenseR, a novel ratiometric biosensor for ferrous iron (Fe(2+)). IronSenseR demonstrates high specificity for Fe(2+) with dissociation constants of 1.78 ± 0.03 (FeSO(4)) and 2.90 ± 0.12 μM (FeCl(2)), while showing no binding to Fe(3+) and other divalent cations. In vivo assessment in Escherichia coli, Pseudomonas putida, and Corynebacterium glutamicum confirmed IronSenseR's capability to detect changes in the intracellular iron pool. The creation of IronSenseR underlines that by reducing search space and eliminating labor-intensive screening, CoBiSe streamlines biosensor development and enables precise creation of next-generation biosensors for diverse metabolites.