A novel biosensor for ferrous iron developed via CoBiSe: A computational method for rapid biosensor design

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Abstract

Genetically encoded biosensors enable monitoring of metabolite dynamics in living organisms. We present CoBiSe, a computational 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 (Fe²⁺). IronSenseR demonstrates high specificity for Fe²⁺ with dissociation constants of 1.55 ± 0.08 µM (FeSO 4 ) and 2.44 ± 0.28 µM (FeCl 2 ), while showing no binding to Fe³⁺ 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.

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