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

A novel biosensor for ferrous iron developed via CoBiSe: A computational method for rapid biosensor design | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results A novel biosensor for ferrous iron developed via CoBiSe: A computational method for rapid biosensor design View ORCID Profile Athanasios Papadopoulos , View ORCID Profile Manuel T. Anlauf , View ORCID Profile Jens Reiners , View ORCID Profile Sueng-Hyun Paik , View ORCID Profile Aileen Krüger , View ORCID Profile Benita Lückel , View ORCID Profile Michael Bott , View ORCID Profile Thomas Drepper , View ORCID Profile Julia Frunzke , View ORCID Profile Holger Gohlke , View ORCID Profile Stefanie Weidtkamp-Peters , View ORCID Profile Sander H.J. Smits , View ORCID Profile Christoph G.W. Gertzen doi: https://doi.org/10.1101/2025.07.16.665043 Athanasios Papadopoulos 1 Center for Structural Studies, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf , Düsseldorf, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Athanasios Papadopoulos Manuel T. Anlauf 2 Center for Advanced imaging, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf , Düsseldorf, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Manuel T. Anlauf Jens Reiners 1 Center for Structural Studies, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf , Düsseldorf, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jens Reiners Sueng-Hyun Paik 3 Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf , Forschungszentrum Jülich, Jülich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sueng-Hyun Paik Aileen Krüger 4 Institute of Bio- and Geosciences (IBG-1: Biotechnology) , Forschungszentrum Jülich, Jülich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Aileen Krüger Benita Lückel 4 Institute of Bio- and Geosciences (IBG-1: Biotechnology) , Forschungszentrum Jülich, Jülich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Benita Lückel Michael Bott 4 Institute of Bio- and Geosciences (IBG-1: Biotechnology) , Forschungszentrum Jülich, Jülich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael Bott Thomas Drepper 5 Institute of Bio- and Geosciences (IBG-4: Bioinformatics) , Forschungszentrum Jülich, Jülich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thomas Drepper Julia Frunzke 4 Institute of Bio- and Geosciences (IBG-1: Biotechnology) , Forschungszentrum Jülich, Jülich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julia Frunzke Holger Gohlke 5 Institute of Bio- and Geosciences (IBG-4: Bioinformatics) , Forschungszentrum Jülich, Jülich, Germany 6 Institute for Pharmaceutical and Medicinal Chemistry, Faculty of Mathematics and Natural Sciences, Heinrich Heine University , Düsseldorf, Düsseldorf, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Holger Gohlke Stefanie Weidtkamp-Peters 2 Center for Advanced imaging, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf , Düsseldorf, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stefanie Weidtkamp-Peters Sander H.J. Smits 1 Center for Structural Studies, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf , Düsseldorf, Germany 7 Institute for Biochemistry, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf , Düsseldorf, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sander H.J. Smits For correspondence: Sander.Smits{at}hhu.de Christoph G.W. Gertzen 1 Center for Structural Studies, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf , Düsseldorf, Germany 5 Institute of Bio- and Geosciences (IBG-4: Bioinformatics) , Forschungszentrum Jülich, Jülich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christoph G.W. Gertzen For correspondence: Sander.Smits{at}hhu.de Abstract Full Text Info/History Metrics Supplementary material Preview PDF 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. Introduction Genetically encoded biosensors are minimally invasive tools that allow to monitor changes in various metabolite concentrations in living systems. Functional biosensors combine a molecular recognition element (MRE), which selectively binds a target metabolite, with a reporter module, composed of one or more fluorescent proteins (FP). Fluctuations in metabolite concentration alter the binding to the MRE, which subsequently results in a modulation of the fluorescence signal of the reporter module that can then be detected 1 – 3 . This powerful feature enables monitoring of cellular dynamics, and provides insights into complex metabolic processes with high spatial and temporal resolution 4 . The design of genetically encoded biosensors has traditionally relied on empirical approaches, where biosensors were created through trial-and-error processes 1 , 4 , 5 . Typically, two biosensor designs are favored: The FRET-based biosensors rely on changes in FRET (Förster resonance energy transfer) between two FP domains, whereas the single-FP biosensors are based on environmental modulation of the fluorescence properties of the reporter FP 1 , 6 . In both biosensor designs, conformational changes of the MRE upon metabolite binding result in structural rearrangements of the whole biosensor protein and alter the fluorescence properties of the reporter module enabling biosensing readout 1 , 4 , 6 , 7 . While FRET-based biosensors are rather simple to design by sandwiching an MRE in between a suitable donor and acceptor pair of FPs, the creation of single-FP biosensors often requires a more sophisticated split of the MRE to insert the reporter module, which in turn allows to transmit the signal between both biosensor elements 1 , 7 . Commonly, single-FP biosensors utilize reporter modules that consist of at least a circular-permutated fluorophore (cpFP), which is fused into the MRE in a peculiar fashion 7 . This fusion of the reporter module to the MRE allows for an environmental-sensitive and conformational-dependent modulation of the fluorescence properties upon metabolite binding to the MRE 7 , 8 . Certainly, both popular biosensor designs, FRET-based and single-FP, present advantages and disadvantages: FRET-based biosensors are simple to design and can be used in a ratiometric fashion, but possess a comparably low signal-to-noise ratio, dynamic range and sensitivity 4 , 6 . Single-FP biosensors overcome these limitations but support solely intensiometric measurement applications and are more challenging to create 6 , 7 . To combine the advantages of both biosensor designs, the Matryoshka biosensor designs was recently developed, which enables the creation of ratiometric biosensors that are excitable at a single wavelength and possess high signal-to-noise ratio, dynamic range, and sensitivity 6 , 9 , 10 . While the empirical approach has yielded functional FRET-, single-FP and Matryoshka biosensors in the past, it often requires the labor-intensive and time-consuming creation and screening of large libraries with putative biosensor variants 5 , 11 , 12 . Additionally, empirical biosensor design approaches may result in biosensors with limited specificity, sensitivity, or dynamic range, and such biosensors frequently require extensive downstream optimization to achieve adequate performance in a biological context 5 , 12 . In contrast, previous rational biosensor design approaches leverage detailed knowledge of molecular structures and binding mechanisms of the MRE to systematically engineer biosensors with predictable and tunable properties 7 . These processes are initiated by identifying or designing the biosensor’s modular components, such as the individual MREs and FPs and their linking amino acid residues 7 . Computational modeling and structural biology allowed researchers to predict and enhance interactions between the MRE and its target metabolite, providing a more efficient way to achieve high specificity and affinity 1 . These predictions are often refined and validated experimentally by protein bioengineering approaches using site-directed mutagenesis and directed evolution to further optimize biosensor performance 1 , 5 . Although, previous rational design approaches accelerated the development process and yielded biosensors that are inherently more robust, precise, and adaptable, the initial fusion of the MRE to the respective reporter module remained widely random 1 , 3 , 5 , 7 , 13 . Current rational approaches for biosensor creation, which already include structural analysis of the MRE, are mostly limited to probing loop-regions or surface residues to identify suitable insertion sites 14 – 18 . This reduces the search space but is oblivious to the mechanical function of the MRE, thus maintaining too many putative insertion sites of the reporter module and still resulting in non-functional biosensor variants or affecting the biosensors’ affinity and/or specificity. Therefore, a holistic structure-based approach to identify suitable insertion sites would be a major boost in biosensor development. Our goal was to develop a novel, quick and robust method that allows the in silico prediction of insertion sites for the reporter module within the MRE without a loss of binding properties for the target metabolite. Hence, we utilized the Constraint Network Analysis (CNA) approach, which is based on protein rigidity theory 19 and divides the MRE into flexible and rigid parts via a constraint dilution simulation representing thermal unfolding 20 , 21 . Rigid parts, in this context, means that no internal motions can propagate efficiently through the interconnected network of stabilizing interactions, facilitating the transfer of conformational changes or dynamic perturbations across the structure while maintaining the overall rigidity of the region 22 , 23 . To not disturb the function of the MRE our novel approach intends to identify flexible regions as insertion sites close to the metabolite binding site or preferably between domains connected by it. Thus, rigid parts that are vital for the function of the MRE should not be disturbed by the by the insertion of the reporter module. Ideally the motion and/or forces created upon structural rearrangements by metabolite binding to the MRE should be still transferred between MRE and FP via their collective motions to create and/or trigger a signal in form of changes of the fluorescence properties of the reporter module. The peculiar connection of MRE and reporter module allows for a dynamic fluorescent readout that correlates to changes in metabolite levels. In summary, the utilization of CNA identifies flexible structural elements within the MRE and discriminates those from rigid clusters within the protein, enabling a highly effective prediction of putative insertion sites for the fusion with the reporter module for the first time. This novel workflow was named CoBiSe, computational prediction for rapid biosensor design. The advantage of the CoBiSe approach is that it strongly reduces the large number of empirically identified or experimentally determined insertion sites. Thus, aiming to identify suitable insertion sites and eliminating the pool of random insertions bypasses the previously required labor-intensive approaches by far and significantly reduces the duration of the whole biosensor creation process. Thus, CoBiSe essentially enhances biosensor creation processes in comparison to previously utilized approaches. To verify that CoBiSe is suitable for the prediction of insertion sites, prominent pre-existing biosensors were used for retrospective insertion site analysis, and the results of identified insertion sites are in line with the described sites in literature. Finally, CoBiSe was utilized to successfully design a completely novel ratiometric Matryoshka biosensor that senses ferrous iron (Fe²⁺), a metabolite for which genetically encoded fluorescent-based biosensors barely exist 24 , 25 . Results and Discussion A retrospective analysis reveals the robustness of the rational biosensor design approach by CoBiSe First, we looked carefully at the insertion sites of well described biosensors single-FP and tried to identify a rationale for the position of the fluorescent reporter module in the molecular recognition element (MRE). Here, the focus was on MRE into which reporter modules were inserted, that belong to the family of single fluorophore (FP) biosensors. The list of analyzed sensors comprises HyPer3 26 , RexYFP 27 , PercevalHR 28 , MalB2 29 , Tre-CO4 29 , and QUE7µ 30 . Depending on the availability of their protein structures, either the experimentally determined structure or an AlphaFold model 31 , 32 of the MRE was used for further analysis. The respective structural information of the MREs were subjected to flexibility analysis by the Constraint Network Analysis (CNA) software using ensembles of network topologies and fuzzy noncovalent constraints (ENT FNC ), which does not require generating a structural ensemble but uses an ensemble of network topologies 20 , 21 . According to the results of the CAN analysis, regions of the MRE were selected as suitable insertion sites if they met three criteria: I. They are categorized as flexible at the start of or early in the thermal unfolding simulation by CNA, II. They are at the surface of the MRE as adding the reporter module in the core would disturb the function greatly, and III. They were close to the metabolite binding site or, preferably, they connect two different domains or secondary structure elements, that comprise the metabolite binding site. The latter was deemed more promising as a larger movement is to be expected upon metabolite binding, which then may be transferred to the reporter module. These three steps comprise the CoBiSe workflow. The closer the flexible sites are to the binding site and the earlier they become flexible during the thermal unfolding simulation, the more likely they were deemed insertion points. For the aforementioned pre-existing biosensors, the CoBiSe approach resulted in predicted insertion sites in the MRE matching the reported insertion sites in the literature ( Figure 1 and Figure S1). Details about the predicted insertion sites can be found in the SI. This poses the question of how much work the application of CoBiSe reduces upon biosensor creation process. Download figure Open in new tab Figure 1: Computational determination of insertion sites for biosensor cassettes. The retrospective computational analysis indicates the utilized insertion sites for the circular-permutated fluorophores in pre-existing biosensors. The AlphaFold3 (AF3) models of the substrate binding protein monomer (left) and the respective biosensor (right) are depicted. The computationally suggested insertion areas (magenta) are highlighted. Flanking residues (orange) and novel insertion sites (cyan) are indicated particularly. Utilized fluorophores are labeled and colored according to their fluorescence emission. A : The ε subunit of the bacterial F 0 F 1 ATP-Synthase was utilized to create ATPQueen biosensor for ATP. B : The bacterial transcriptional repressor Rex was utilized to create RexYFP biosensor for NAD + /NADH ratio. C : The bacterial maltose binding protein MalE was utilized to create MalB2 biosensor for maltose. The utilized insertion site is in line with the predicted area between the indicated flexible loop and the flanking amino acid residue. D : The bacterial trehalose-maltose binding protein was utilized and modified to create TreCO4 biosensor for trehalose. The computational analysis determined multiple putative insertion areas (light rose), with the utilized site being most prominent. E : The regulatory domain of OxyR H 2 O 2 binding protein (OxyR RD ) utilized to create HyPer series of biosensors for H 2 O 2 . The predicted insertion sites are in line with the utilized areas for HyPer biosensor creation. Additional mutational screenings in terms of optimization of fluorescence parameters led to the novel insertion sites (orange and cyan) for HyPer3. F : The nucleotide binding protein GlnK was utilized to create Perceval biosensor for ATP/ADP ratio. The protein is active as trimer and only one protomer is used for fluorophore insertion, whereby the determined insertion area is in line with the utilized site. The light cyan region in PercevalHR indicates structural elements of the protomer. G : The total number of residues of all tested MREs are indicated (pink) and the predicted residues are highlighted (magenta). H : The percentage of predicted residues compared to loop regions of the MREs (light blue) and the percentage of predicted residues compared to the total MREs are indicated (blue) showing the reduced amount of screening for putative insertion sites. I : The number of residues in the loop regions of all tested MREs are indicated (rose) and the predicted residues are highlighted (magenta). Comparing CoBiSe to commonly used random site insertion or whole sequence screening, we achieve a reduction of on average 94 % of the search space, whereas comparing it to a structure-aware approach, which only considers loops regions CoBiSe achieves a reduction of the search space of on average 82 % ( Figure 1 , G-I). Details for each pre-existing sensor can be found in the SI. In summary, CoBiSe successfully identified insertion sites in six out of six MREs and among those proteins identified seven out of nine insertion sites in a retrospective analysis in published biosensors ( Figure 1 and S1). Furthermore, the applied computational analysis of insertion sites reduced the search space tremendously, regardless of whether the entire MRE or only the loop regions were analyzed. To verify the robustness of CoBiSe in a prospective manner we designed a completely novel ratiometric biosensor for ferrous iron (Fe²⁺). CoBiSe analysis revealed insertion sites for a reporter module into iron binding protein By leveraging structural data in terms to find a suitable iron binding protein for biosensor creation, the global iron regulator ( D iphtheria to x in regulator protein, DtxR) from Corynebacterium glutamicum was identified. DtxR is a ferrous iron binding protein that can serve as the molecular recognition element (MRE) to be utilized for a putative iron biosensor. By applying the CoBiSe approach for DtxR, it was possible to identify putative insertion sites for the reporter module within the protein ( Figure 2 , A). A flexible connective loop (residues 139-150) was identified as the optimal insertion site for the Matryoshka biosensor cassette, consisting of superfolder circularly permutated GFP (cpsfGFP) and large stokes shift mApple (LSSmApple), which was successfully integrated into the DtxR protein. Following expression in Escherichia coli and protein purification, subsequent functional characterization revealed five insertion variants (I138, D141, D147, S148, and G149) displaying robust positive responses to ferrous iron (0-25 µM) with dynamic ranges exceeding 50% ( Figure 2 , Figure S2). These variants exhibited significant concentration-dependent increases in cpsfGFP fluorescence upon Fe²⁺ binding, with dynamic ranges between 84.4% and 107%, demonstrating their enhanced sensitivity to DtxR structural changes induced by iron binding. For a detailed description, see SI. Next, to assess the dynamic behavior between the five most responsive variants, the signal to reference correlation of the biosensors was evaluated to determine which variant yields the best signal-to-noise ratio ( Figure 2 E). Among the tested biosensor variants, the one created by insertion of the reporter module at position G149 of DtxR demonstrated the highest signal to reference correlation, indicating superior signal-to-noise ratio, sensitivity, and responsiveness to ferrous iron ( Figure 2 E). These results highlight that the insertion at position G149 created an effective iron biosensor MDtxR G149 GA. The superior biosensor variant MDtxR G149 GA was named IronSenseR and utilized for further applications. Download figure Open in new tab Figure 2: Identification of suitable insertion sites for the creation of responsive biosensors for iron ions. A : The AlphaFold3 (AF3) model of DtxR from C. glutamicum is depicted. The protein serves as molecular recognition element for iron. The metal binding sites of DtxR (MBS1: H79, E83, H98 and MBS2: E105, C102, H106) and coordinated metal ions are shown. Subdomains (SH3 domain and DNA binding domain) are indicated. The flexible loop (magenta) indicates residues that are used as putative insertion sites for the Matryoshka cassette. B : The AlphaFold3 model of the Matryoshka cassette is depicted, the cassette consists of a nested LSSmApple (red) nested to a circular-permutated super folder GFP (cpsfGFP, green). Due to the permutation the amino- and carboxy termini of sfGFP sequence are switched, thus the original C-terminal sequence (sfGFPc, green) appears before the original N-terminal sequence (sfGFP N , light green). The linker residues (PA and NP) are indicated. C : The titration of FeSO 4 (0-25 µM) used to monitor the sensing action of DtxR-based ratiometric biosensor variants when insertion of the matryoshka cassette conducted at the proposed positions within the identified flexible loop. D : The dynamic range of the respective biosensor variants is depicted [ΔR/R 0 , %]. E : The signal-to-reference correlation of the cpsfGFP intensities to the intensity of the reference fluorophore is depicted. The response monitored as an increase of cpsfGFP fluorescence upon ion binding. The variant created upon insertion at position G149 indicates enhanced dynamic range and signal-to-noise ratio in comparison to the other biosensor variants. Characterization of the genetically encoded biosensor for ferrous iron Structural characterization of IronSenseR using SAXS confirmed its monomeric state in solution, with the experimental data matching the AF3 predicted model (χ² = 1.08) ( Figure 3 A, Figure S5, and Table S1). Fluorimetric binding analysis revealed high specificity for ferrous iron (Fe²⁺), with dissociation constants of 1.55 ± 0.08 µM for FeSO₄ and 2.44 ± 0.28 µM for FeCl₂, while showing minimal to no binding with Fe³⁺ compounds and other tested metal ions (MgSO₄, NiSO₄), with only slight responses to MnSO₄ and unsaturated binding to CoSO₄ ( Figure 3 B and C, Figure S3 A–H). Control experiments with binding-deficient mutants (H79A, H98A, and C102A) confirmed the specificity of the sensor’s interaction with ferrous iron, as these variants showed negligible fluorescence changes upon Fe²⁺ titration ( Figure 3 D, Figure S3 I–K). A detailed characterization can be found in the SI. Together, these results demonstrate IronSenseR’s capability to selectively detect ferrous iron over ferric iron and other divalent cations under in vitro conditions. Download figure Open in new tab Figure 3: Characterization of IronSenseR: a ratiometric biosensor for ferrous iron A : Molecular architecture and AlphaFold3 (AF3) model of IronSenseR, the Matryoshka biosensor for iron is depicted. The flexible loop (magenta) and the insertion site G149 for the Matryoshka cassette are indicated. B : The titration of FeSO 4 (0-25 µM) used to monitor function of the ratiometric biosensor. C : The change of the fluorescence intensities of the reporter FP (cpsfGFP) used to calculate the dynamic range [ΔR/R0] of IronSenseR upon titration of various metal ions at different concentrations. The binding affinities (K d ’s) are indicated. D : The dynamic range for binding-deficient IronSenseR mutants H79A, H98A and C102A are shown. The zoom-in serves for a more detailed view on the data. All the data shown are averages of at least three biological replicates (n=3) Assessment of genetically encoded biosensor for ferrous iron in vivo After in vitro characterization, IronSenseR was assessed in vivo in Escherichia coli via fluorescence microscopy. Bacteria were cultivated in nutrient-rich media, and iron availability was subsequently altered after cell growth by adding varying concentrations of the membrane-permeable iron chelator 2,2’-Bipyridine (BPD) 33 , 34 . This was done since iron plays an essential role in the growth and viability of cells, so that alteration of iron homeostasis is challenging and often affects cell growth and protein expression 35 . Microscopy data revealed a reduction in the fluorescence ratio of cpsfGFP/LSSmApple upon increasing BPD concentration (from 1.0 without chelator to 0.84 with 250 µM BPD), with cpsfGFP fluorescence decreasing while LSSmApple fluorescence remained constant ( Figure 4 and Figure S4 A-E). Additionally, binding-deficient mutants H79A, H98A and C102A of IronSenseR did not indicate ratiometric changes upon addition of BPD or were greatly diminished in comparison the wild type IronSenseR ( Figure 4 and Figure S4 A-E). The ratiometric design allowed analysis of cells with heterogeneous expression levels. While the apparent dynamic range in vivo was reduced compared to in vitro measurements, likely due to the indirect alteration of intracellular iron and challenges in accurately detecting the change of intracellular iron concentrations, the decreasing cpsfGFP/LSSmApple ratio is in line with the expected sensing ode of IronSenseR. In summary, these results demonstrate that the ratiometric Matryoshka IronSenseR can effectively measure dynamic changes in intracellular iron levels in E. coli , though precise correlation between fluorescence ratios and exact or absolute ferrous iron concentrations remains challenging. Download figure Open in new tab Figure 4: In vivo imaging of IronSenseR in Escherichia coli Response of IronSenseR (WT) and binding deficient mutants (H79A, H98A and C102A) to increasing concentrations of the iron chelator 2,2′-Bipyridine (BPD) in bacterial cells. Confocal images of E. coli BL21(DE3) expressing the biosensor without addition of BPD (0 µM, PBS top row) and after incubation with either 20 µM BPD (middle row) or 250 µM BPD (bottom row) are depicted. The ratio of green (cpsfGFP) to red fluorescence (LSSmApple) is displayed using a rainbow colored lookup table with two zoomed-in sections depicted in next to the main columns. The scale bar in the zoomed-in images is 1 µm. LUT 0.6-1.1 is depicted by the color bar (down right) indicating the decrease in ratio G/A upon BPD addition. When iron is present, ratio G/A and LUT is high (red) but drops (blue) when iron is chelated by BPD for IronSenseR WT. In case of the binding deficient mutants, this effect is not observable for H79A and H98A or highly diminished as for C102A. Data acquired in biological replicates, n=6 for WT and n=3 for binding deficient mutants. Next the IronSenseR was utilized in Pseudomonas putida and Corynebacterium glutamicum , to demonstrate the broad applicability of the biosensor in different bacteria ( Figure 5 ). The corresponding IronSenseR encoding gene-cassette was codon-optimized for its use in P. putida , a Gram-negative bacterium relevant to biotechnology and siderophore-based microbial interactions 36 , 37 . Under iron depleted conditions, various Pseudomonas species produce and secrete the siderophore pyoverdine, which binds environmental ferric iron. The ferri-pyoverdine complex can be specifically taken up by the bacteria, thereby playing an important role in bacterial iron homeostasis 38 . To test if changes in the cytosolic iron pool in P. putida can be detected by IronSensR, the biosensor encoding gene-cassette was genomically integrated into the wild-type strain KT2440 (WT) and a pyoverdine-deficient mutant strain ( ΔpvdD ). The recombinant sensor strains were grown under iron-supplemented and iron-depleted conditions. As indicated by the constant fluorescence ratios of the biosensor, increasing the iron concentration in the growth medium did not affect the labile iron pool in the cells of either strain ( Figure 5 , A–C). However, due to the loss of siderophore-mediated iron acquisition in the ΔpvdD strain, increasing concentrations of BPD led to in a detectable decrease in the cytosolic pool of free ferrous iron and a reduction in the fluorescence intensity of the biosensor reporter domain. Conversely, the gradual depletion of iron resulted in the induction of pyoverdine biosynthesis in the WT strain thereby maintaining the intracellular iron pool at a constant level ( Figure 5 , D–F). These observations confirm the important role of pyoverdine in iron homeostasis and thereby clearly prove the in vivo applicability of IronSenseR. Download figure Open in new tab Figure 5: In vivo iron sensing in Pseudomonas putida and Corynebacterium glutamicum Response of IronSenseR to increasing concentrations of the iron chelator 2,2′-Bipyridine (BPD) and ferrous iron (FeSO 4 ) was determined via fluorescence analysis in the P. putida wild type (WT) and pyoverdine-lacking mutant (Δ pvdD ) strain. A : Comparison of reporter fluorescence (FL) at 510 nm between P. putida WT and Δ pvdD upon addition of ferrous iron (FeSO 4 ). B : Comparison of reporter fluorescence (FL) at 510 nm between P. putida WT and Δ pvdD upon addition of BPD. C : IronSenseR utilized to sense varying iron content upon cultivation of C. glutamicum . Increasing iron uptake and/or availability lead to increasing reporter fluorescence of IronSenseR. Comparison of C. glutamicum WT and Δ dtxR strains upon cultivation in 3.6 µM iron and expression of IronSenseR. The mutant strain Δ dtxR lacks the regulation of iron acquisition and is not limited in comparison to the WT, therefore IronSenseR signal is increased indicating a large pool of intracellular iron in comparison to the WT. The Experiments conduct in n=6 biological replicates for P. putida and n=4 biological replicates for C. glutamicum . C. glutamicum expressing IronSenseR was cultivated with varying iron concentrations from a standard high amount of 36 µM, over a sufficient one of 3.6 µM to a limitation of 1 µM ( Figure 5 G). A clear reduction in reporter (cpsfGFP) fluorescence emission signal can be observed upon decreasing iron concentrations. Moreover, a deletion mutant strain ( C. glutamicum , Δ dtxR ) was transformed with the IronSenseR encoding plasmid and iron-dependent reporter signals compared to the wild type. DtxR is the master regulator of iron homeostasis in C. glutamicum , acting as an iron-activated repressor of iron starvation response 39 . Therefore, it was expected that the mutant accumulates and maintains relatively more intracellular iron than the WT, due to further iron uptake despite iron sufficiency. Using IronSenseR, the expected effect could be observed at the 3.6 µM iron condition, indicated by an increased reporter emission signal in comparison to the wild type ( Figure 5 H). Additionally, it is crucial to notify that the Δ dtxR mutant possess a growth defect due to iron overload 40 . This growth defect was indeed observed upon monitoring the optical density (OD600) upon cultivation, indicating that the DtxR-based IronSenseR does not regulatory interfere in C. glutamicum with the global regulator pathway. Conclusions The development of biosensors has evolved significantly, transitioning from traditional empirical design methods to rational, computationally informed strategies 1 . Empirical design, while foundational, relies heavily on trial-and-error approaches that are time-consuming, inefficient, and often yield suboptimal biosensors requiring extensive refinement. In contrast, rational design leverages molecular knowledge and computational tools to systematically engineer biosensors with predictable and tunable properties 1 , 7 . Constraint Network Analysis (CNA) facilitates the identification of flexible structural elements 20 , 21 within molecular recognition elements (MREs), predicting optimal insertion sites for reporter modules. We demonstrated that our computational approach, CoBiSe, retrospectively predicted insertion sites over a diverse set of MREs ( Figure 1 ) and prospectively led to the successful design of a novel ratiometric Matryoshka biosensor for ferrous iron named IronSenseR. For the retrospective computational analysis of insertion sites in pre-existing biosensors, we used either high-quality X-ray crystal structures or AlphaFold models 31 , 32 of the MREs. For multimeric MREs, both the multimeric structure and single protomers were analyzed, with both approaches correctly predicting the insertion sites ( Figure 1 , A–F). This demonstrates the robustness of our approach, showing that no specific structural state or method of structure determination is required. Knowledge of active and inactive protein states is also not a prerequisite, although we are investigating methods to exploit such information to further reduce the search space. Additionally, the ENT FNC approach 21 implemented in CNA enables this method even when resources for computationally intensive tasks like molecular dynamics simulations are unavailable. Furthermore, MREs with similar folds shared predicted flexible regions (e.g., both MalE in MalB2 and TMBP in Tre-CO4 exhibit a Venus fly trap mechanism), supporting our fundamental concept of viewing MREs as mechanical structures with rigid and flexible parts that move in specific ways to function. This idea is corroborated by investigations showing strong correlations between protein structure and dynamics 41 . Ideally, function should remain undisturbed, so we aim to insert reporter modules in flexible parts less likely to participate in the MRE’s concerted mechanical movements. Insertion sites are not exclusively found in loop regions (though there is a preference), making the CNA flexibility prediction advantageous. Both predicted and experimentally identified sites predominantly occur at transitions between flexible and rigid regions, supporting our hypothesis that when rigid parts move in concert, maximum force is exerted at these transitions. Thus, our CoBiSe approach eliminates the need for labor-intensive random mutagenesis 1 , 5 , accelerating development while ensuring optimal sensor performance. To validate CoBiSe’s capability to predict suitable insertion sites for de novo biosensor creation, we applied CNA to the DtxR protein from C. glutamicum to predict insertion sites for a ferrous iron (Fe²⁺) biosensor ( Figure 2 , A). Ferrous iron plays central roles in numerous cellular processes, serving as a cofactor in oxygen transport, redox reactions, energy production, and gene regulation for bacteria as well as in general for living cells 35 , 42 , 43 . Its chemical reactivity promotes the generation of damaging reactive oxygen species (ROS) 44 , necessitating tight cellular regulation to balance essential functions against potential toxicity 44 . Previous attempts to create ferrous iron biosensors resulted in limited or indirect detection methods 24 , 25 . Our CNA identified a flexible connective loop between I138 and V150 of DtxR as putative insertion sites for the reporter module ( Figure 2 , A). Inserting the next-generation Matryoshka cassette 9 ( Figure 2 , B) into these positions yielded 13 ratiometric biosensor candidates that were rapidly screened for ferrous iron binding in vitro ( Figure 2 , C). The variant with insertion at position G149 demonstrated superior sensing behavior and high specificity for Fe²⁺ in the physiological micromolar range, without binding to other relevant cations such as Fe³⁺ ( Figure 2 , C and Figure 3 ). The next-generation Matryoshka biosensor module provided enhanced signal-to-noise ratio, allowing finely tuned responses upon iron binding. This novel ratiometric biosensor, IronSenseR, was successfully used for in vivo analysis of the intracellular iron pool in different bacterial cells ( Figure 4 and 5 ), addressing a critical need for precise and selective detection of ferrous iron in biological contexts 24 , 25 . IronSenseR development validates our computational approach, which enabled rapid biosensor creation with optimized dynamic range, specificity, and sensitivity in a single step, demonstrating the adaptability of our design framework. IronSenseR represents a significant advancement with potential applications in studying iron homeostasis and its dysregulation in cells. Beyond basic research, such tools can support diagnostic screenings of agents targeting iron-related functions in bacterial pathogens 43 . By providing precise, reliable methods to explore biochemical pathways and cellular processes, this technology supports advancements across various research areas. CoBiSe transforms rational biosensor design by strategically combining structural and computational information, making it adaptable to virtually any protein of interest. This approach enables rapid biosensor creation regardless of target metabolite, as demonstrated by IronSenseR. By significantly narrowing the search space for biosensor cassette insertion sites, CoBiSe offers an efficient, rational, and more economically and environmentally sustainable design process. Its broadly compatible computational requirements ensure accessibility across the scientific community. Experimental Section Computational insertion site identification For the application of the Constraint Network Analysis (CNA) 20 , 21 , 45 approach in the retrospective analysis on the molecular recognition elements (MREs) of the single fluorophore (FP) biosensors, X-ray crystal structures, AlphaFold models 31 , 32 or available models from the UniProtKB 46 were used. Here, a deliberate mix of experimentally determined crystal structures and AlphaFold models was used, although an emphasis was put on crystal structures with a high sequence coverage and low resolution. For HyPer3, PercevalHR, and MalB2 AlphaFold models were used and for 30Rex, QUE7µ, and Tre-CO4 the PDBs entry 2DT5 47 , 2E5Y 48 , and 1EU8 49 were used, respectively. The structures were prepared using the protein preparation wizard in Maestro and protonated to a pH of 7.4 using PROPKA 50 . Subsequently, the structures were analyzed using the CAN approach 45 , 51 . For analyzing the rigid cluster decomposition of all MREs, a constraint dilution simulation was performed using CNA on an ensemble of network topologies generated via fuzzy noncovalent constraints (ENT FNC ) 20 , 21 . Subsequently, the unfolding trajectory was visually inspected using VisualCNA 45 for regions that were determined to be flexible, preferably at the beginning of the thermal unfolding simulation, and that are either near to the metabolite binding site or, preferably, that connect rigid domains that are addressed by the metabolite. VisualCNA is an easy-to-use PyMOL 52 plugin that allows setting up CNA runs and analyzing CNA results, linking data plots with molecular graphic representations 45 . For the generation of the novel IronSenseR, an AlphaFold model of the DtxR iron-binding protein originating from Corynebacterium glutamicum strain (Uniprot ID: Q8NP95) was treated as described above and utilized as MRE for subsequent investigations. Molecular cloning The dtxR gene was amplified by PCR from gDNA of Corynebacterium glutamicum strain ATCC13032 and sub-cloned into pRSET B based vector and subsequently the correctness was verified by sequencing (Microsynth Seqlab). This vector was linearized by PCR to be used for Gibson assembly. The Matryoshka cassette 9 , encoding for the circular permutated superfolder GFP 53 (cpsfGFP) and nested large stokes shift mApple (LSSmApple) 9 , was amplified by PCR with corresponding primers possessing suitable ends for Gibson assembly and inserted into the respective insertion sites of DtxR (I138-V150). After PCR, samples were treated with DpnI (NEB) and analyzed by agarose gel electrophoresis. The corresponding amplicons were isolated by gel extraction (MN, gel clean up kit) and utilized for Gibson assembly (NEB). Binding deficient mutants of the biosensor were created by site-directed mutagenesis using KLD mix (NEB). The cloning constructs were used to transform chemically competent Escherichia coli DH5α (NEB) by heat shock method. Positive clones were obtained upon cultivation of LB (lysogeny broth, Luria/Miller, Roth) agar plates containing 100 µg/ml ampicillin. Single colonies were used for cultivation in LB supplied with the same antibiotics and were utilized for plasmid isolation (MN Plasmid isolation kit). The plasmids were subjected to DNA sequencing (Microsynth) to confirm successful cloning. Primers for DNA amplification were conducted with corresponding primers listed in supplementary information (Table S2). All kits and protocols followed the manufacturers guidelines. To apply IronSensR in vivo in Pseudomonas putida , the corresponding encoding gene-cassette of IronSenseR was optimized using the galaxy codon harmonizer 54 . The resulting biosensor gene was cloned via Gibson assembly 55 into a miniTn7 vector under the control of a P tac promoter regulated by the lac repressor LacI 56 . To analyze if the loss of siderophore production results in a detectable change of the intracellular pool of free iron ions, the pyoverdine biosynthesis gene pvdD was deleted using the pQure system 57 . Using tri-parental conjugation the biosensor expression cassette located on plasmid (pAZ191_GA) was transferred to P. putida and subsequently integrated into the genome (Tn7 insertion site) of both the P. putida WT strain KT2440 and the ΔpvdD, as described previously 58 . For the in vivo usage of IronSenseR in Corynebacterium glutamicum , the plasmid vector was exchanged to the shuttle vector pPREx2 59 , including an IPTG-inducible tac -promoter and a kanamycin resistance cassette, while protein-tags were removed. Molecular methods were performed according to standard protocols 60 . Plasmids were enzymatically assembled using Gibson Assembly 55 , resulting in pPREx2-MDtxR G149 GA amplified and stored in E. coli DH5α. Expression and purification Biosensor encoding gene cassettes were expressed in Escherichia coli BL21 (DE3) and the corresponding proteins were purified by affinity chromatography. In brief, each biosensor variant was encoded by an pRSET B -based expression system containing a directly fused N-terminal deca-histidine tag. The expression was conducted in E. coli BL21(DE3) (NEB) upon the utilization of autoinduction media 61 . Therefore, chemically competent bacteria were transformed with the respective plasmids by heat shock method and single colonies plated for selective growth on LB agar (lysogeny broth, Luria/Miller, Roth) containing ampicillin (amp, 100 µg/ml) at 37°C for 17 h. A single colony was utilized for expression and used for the inoculation of a pre-culture of 5 ml LB containing ampicillin (100µg/ml). The pre-cultures were cultivated at 37°C, 220 rpm under darkened conditions and the OD 600 was monitored. Upon reaching OD 600 of ∼ 0.6, 1.25 ml of pre-cultures were used for inoculation of 50 ml of LB media supplemented with ampicillin (100µg/ml) and containing 0.05% (w/v) glucose as well as 0.2% (w/v) lactose, and put for cultivation at 21°C, 220 rpm for 48 h. After expression, cells were harvested by centrifugation at 4000 x g, 4°C for 40 min. The supernatant was discarded, and the cell pellets were resuspended with 25 ml ice-cold 20mM MOPS pH 7.0 and stored on ice in darkened conditions. An additional centrifugation step at 4000 x g, 4°C for 20 min allowed to remove excess of the supernatant solution, thereby removing putative remaining media compounds. The pellets were again resuspended in 15 ml of 20 mM MOPS pH 7.0 and flash-frozen in liquid nitrogen prior to storage at -80°C. For purification of the expressed biosensors, the cells were thawed on ice in darkened conditions and 5 ml of the cell suspension was used for cell lysis. Therefore, the cells were pelleted by centrifugation at 11000 x g, 4°C for 1 min and suspended in 2 ml 20 mM MOPS pH 7.0. The cell lysis was conducted by sonification for 2 rounds (Qsonica sonicators) using an amplitude of 50 and 45 pulse cycles with 3 seconds pulse-ON and 8 seconds pulse-OFF. After sonication, the cell lysates were clarified by centrifugation for 20 min at 20830 x g, 4°C to remove cell debris. The clarified lysate containing the histidine-tagged biosensors was applied to NiNTA-based affinity chromatography (Protino, Macherey-Nagel). Therefore, 750 µl NiNTA beads were applied to a gravity flow column (Poly-Prep, Bio-Rad) and rinsed with a total volume of 30 ml deionized water. Subsequently, the beads were equilibrated with 5 ml of 20 mM MOPS pH 7.0 prior to the application of the clarified lysate for immobilization of the histidine-tagged biosensors by gravity flow. The loaded NiNTA beads were washed with 8 ml of 20 mM MOPS pH 7.0, 500 mM KCl and 20 mM imidazole to remove weakly bound impurities. To elute the immobilized biosensors from the NiNTA beads, 3 ml of 20 mM MOPS pH 7.0, 300 mM imidazole were applied, and elution was collected in two 1.5 ml fractions. The buffer of eluted protein was exchanged upon utilization of desalting columns (Cytiva) or size exclusion chromatography via gel filtration on Superdex 200 Increase 10/300 GL column (Cytiva), suiting further structural and biochemical characterization of the biosensor. The protein concentration was determined by UV/Vis spectrometry (NanoDrop, Thermo Fisher Scientific). The purity of the elution fractions was analyzed by SDS-PAGE by conventional Coomassie staining and prior in-gel fluorescence (λ ex 460 nm and λ em 525 nm, Amersham imageQuant 800, GE/Cytiva). The samples were stored for maturation at 4°C for at least 24 h, prior further usage for biochemical, biophysical or structural analysis. Functional characterization The screenings for optimal biochemical conditions of the Matryoshka biosensors for iron were inspired by previous studies 61 . Here, MOPS-based buffer systems were utilized 61 . Fluorescence emission spectra of the biosensors were analyzed on multimode microplate reader (Infinite M Plex, Tecan) inspired by previous protocols 9 , 10 , 61 . Therefore, the purified biosensors were diluted to 0.1-0.2 mg/ml in 20 mM MOPS pH 7.0. To assess the binding response of the biosensors to various divalent cations, titrations were conducted in micro titer plates (96 WP, flat bottom, Greiner). Therefore, 50 mM stock solutions of the corresponding ions were generated in deionized water and utilized for subsequent stepwise dilutions to reach 50 µM stocks in 20 mM MOPS pH 7.0. Therefore, 200 µl of the 50 µM stock solution were added to well of lane 12 of each row (A-H) of the micro titer plate, and in all other wells 100 µl of 20 mM MOPS pH 7.0 were added. A serial dilution was performed reaching from 50 to 0.04 µM (from wells 12 to 2) using a multi-channel pipette. Lan1 1 contained only 100 µl of 20 mM MOPS pH 7.0 and equals the 0 control. Afterwards, 100 µl of the corresponding biosensor solution were added to each row of the 96-WP using a multi-channel pipette. An incubation for 15 min at ambient temperature under darkened conditions ensured binding of the ions to the biosensor. The steady-state fluorescence spectra were recorded at 25°C in top reading mode with a bandwidth of 20 nm, 30 flashes, and manual gain of 100 for both, excitation and emission wavelengths. The excitation wavelength (λ ex ) was 453 nm and the emission spectra were recorded from 490-700 nm in 5 nm steps. The autofluorescence of the buffer was negligibly low. The reporter FP (cpsfGFP) indicated an emission maximum at 505-510 nm and the reference FP at 600 nm. For the data evaluation, the maximal value of the reference FP at 600 nm was used for normalization of each measured data point within the recorded spectra. Furthermore, values of emission maxima were used for calculated relative dynamic range changes (ΔR/R 0 ) in response to analyte binding by the respective biosensor as suggested previously 9 , 10 . To analyze in vivo if in Pseudomonas putida the loss of siderophore production results in a detectable change of the intracellular pool of free iron ions in pyoverdine biosynthesis deletion background IronSenseR was integrated into the genome) of both the P. putida WT strain KT2440 and the ΔpvdD, as described previously 58 . The resulting strains including the WT and ΔpvdD strains without intergration were pre-cultivated in 1 ml LB at 30° C at 1200 rpm in Flowerplates for 24 hours. For biosensor-based analysis of intracellular iron-levels, the cells were subsequently inoculated in 1 ml LB with an OD 600 of 0.05. To decrease or increase the iron availability, DIP (0.5 and 7.5 mM) and FeSO 4 (3.6, 10 and 50 µM) were added to the LB medium, respectively. Cells were cultivated for 4 hours at 30° C (1200 rpm). To induce the expression of the IronSensR gene, 1mM IPTG was added and cell were further incubated for 48 hours at 20° C (1200 rpm). Electrocompetent Corynebacterium glutamicum ATCC13032 WT or Δ dtxR cells were transformed with isolated pPREx2-MDtxR G149 GA plasmid via electroporation 62 . Single colonies (n=4) were cultivated in 5 ml BHI in reaction tubes at 30°C for 5 h. From this pre-culture, a main culture with a starting OD 600 of 0.01 was inoculated in 15 ml CGXII media containing 2% (w/v) glucose and respective amount of FeSO 4 as iron source (1 g L −1 K 2 HPO 4 , 1 g L −1 KH 2 PO 4 , 5 g L −1 urea, 42 g L −1 MOPS, 13.25 mg L −1 CaCl 2 · 2 H 2 O, 0.25 g L −1 MgSO 4 ·7 H 2 O, 0.27/1/10 mg L −1 FeSO 4 · 7 H 2 O, 10 mg L −1 MnSO 4 · H 2 O, 0.02 mg L −1 NiCl 2 · 6 H 2 O, 0.313 mg L −1 CuSO 4 · 5 H 2 O, 1 mg L −1 ZnSO 4 · 7 H 2 O, 0.2 mg L −1 biotin, 30 mg L −1 3,4-dihydroxybenzoate (PCA), 20 g L −1 D-glucose, pH 7.0) 63 supplemented with kanamycin (25 µg mL -1 ) and 15 µM IPTG in 100 ml shaking flasks on a rotary shaker at 21°C for 48 h. Consequently, cells according to an OD 600 of 0.5 in 1 ml were harvested via centrifugation (5,000 rpm, 4°C, 5 min) and washed twice with PBS (phosphate buffered saline, 137 mM NaCl, 2.7 mM KCl, 10 mM Na 2 HPO 4 , 1.8 mM KH 2 PO 4 ). To ensure full maturation of the reference fluorescence protein LSSmApple, samples were stored overnight in the dark at 4 °C as described previsouly 61 . A volume of 100 µl was analyzed using a multimode microplate reader (Infinite M1000Pro, Tecan) by recording emission spectra from 490 nm to 700 nm in 5 nm increments, with single wavelength excitation at 453 nm and using the maximal value of the reference FP at 600 nm for normalization as described above in the methods section. Analysis of biosensor kinetics To determine the sensing mode of the proposed biosensors and to obtain comparable data, the dynamic range [ΔR/R 0 ] 9 , dynamic range in percent [ΔR/R 0 , %] and the Signal to Reference correlation [ΔR/F R ] were calculated by the following equations: F GFPmax : Fluorescence emission of cpsfGFP at 510 nm at highest metabolite concentration F GFPmin : Fluorescence emission of cpsfGFP at 510 nm without metabolite F LSSmApple : Highest emission of the reference FP at 600 nm throughout the assay The binding affinity was determined by fitting the obtained titration data with the sigmoidal, 4PL, X is log (concentration) equitation of GraphPad Prism 10.4.0 (527) (GraphPad Software, LLC). The representative LogIC50 equals to the Kd values. Fluorescence Microscopy Escherichia coli BL21(DE3) were transformed with a plasmid encoding for IronSenseR (MDtxR G149 GA, WT) or binding deficient mutants of IronSenseR (H79A, H98A and C102A) and cultivated as described above for 24 h in a volume of 20 ml. Prior to imaging, cells were washed with PBS buffer ( P hosphate B uffered S aline, 137 mM NaCl, 2.7 mM KCl, 10 mM Na 2 HPO 4 , 1.8 mM KH 2 PO 4 ) and incubated for 20 min with PBS buffer containing 0 µM, 20 µM or 250 µM 2,2’-Bipyridine (BPD). Cells were immobilized on Poly-L-Lysine coated 8 Well µ-Slides (ibidi). Imaging was performed using an Olympus Fluoview 3000 confocal laser scanning microscope equipped with a 60x UPLSAPO water objective (NA 1.2). Samples containing IronSenseR were excited with a 488 nm laser (0.5% laser power). Pixel dwell time was set to 2 µs with a line averaging of 2 and a pinhole diameter of 1 AU. Detector range was set to 500-530 nm for cpsfGFP. Fluorescence of LSSmApple was collected at 600-700 nm. Six biological replicates (n=6) for the WT and three biological replicates (n=3) for the binding deficient mutants were analyzed. Image processing and image data analysis For the ratiometric intensity analysis a segmentation was performed using Cellpose 2.0 (Version 2.2) 64 with the pre-trained “cyto2” model and quantified as well as plotted using a custom python script. In short, the mean fluorescence. Total intensity for the green channel (cpsfGFP, 500-530nm) and red channel (LSSmApple, 600-700 nm) were extracted from the resulting masks and used to calculate the respective ratios. Low and non-expressing cells with an excessively low intensity in either channel were excluded from the analysis using an intensity threshold of 200. This threshold separated best the two peaks (expressing VS non-expressing) of the distribution of individual bacteria mean fluorescence intensities. Fluorescence ratios for all conditions of each biological replicate were normalized to the PBS measurement of the respective replicate. The fluorescence ratios were plotted using a boxplot and a stripplot. For visualization of fluorescence ratios, exemplary images were processed in FIJI 65 . The transmitted light channel was thresholded using either the “Otsu” or “Triangle” method followed by a “Dilate” and an “Open” step. The resulting mask was used for segmentation of fluorescence channels while objects smaller than 0.3 µm² were excluded. A fluorescence ratio channel was created by dividing the pixel intensities of the green channel by the pixel intensities of the red channel. The resulting image was smoothed using a median filter with a radius of 2. Afther that, the normalization factor calculated before was applied to the ratio image. Resulting ratios were displayed using a rainbow-colored lookup table (“physics” in FIJI) ranging from 0.6 to 1.1. Structural modelling The structural models were created upon utilization of AlphaFold 3 prediction algorithm 31 , 32 . Structural analysis by small angle X-ray scattering SEC-SAXS data was acquired on beamline BM29 at the ESRF Grenoble 66 . The BM29 beamline was equipped with a PILATUS 2M detector (Dectris) at a fixed distance of 2.827 m. The SEC-SAXS runs were performed at 20°C on a Superdex200 increase 10/300 GL column (100 µl inject, Buffer: 20 mM MOPS pH 7.0, 250 mM KCl) with a flowrate of 0.6 ml/min. We collected 1200 frames with an exposer time of 2 sec/frame. Data were scaled to absolute intensity against water. All used programs for data processing were part of the ATSAS Software package (Version 3.0.5) 67 . Primary data reduction was performed with the programs CHROMIXS 68 and PRIMUS 69 . With the Guinier approximation 70 , we determine the forward scattering I(0) and the radius of gyration ( R g ). The program GNOM 71 was used to estimate the maximum particle dimension ( D max ) with the pair-distribution function p(r) . We created a model of MDtxR G149 GA with AlphaFold3 31 , 32 and compared the theoretical scattering intensity of the resulting models against the experimental data with CRYSOL 72 . Associated content Supporting Information The supporting Information is available free of charge at: Link to follow Author contributions AP, CG and SS initiated this study, AP conducted the biosensor design, molecular bioengineering, protein characterization. CG, AP, JR, and HG conducted the computational analysis (CNA and structure prediction) and AP and CG initiated the idea of CoBiSe, MA, TB and SWP conducted the microscopy and imaging evaluation. SP and TD conducted in vivo experiments in P. putida . AK, BL and JF conducted in vivo experiments in C. glutamicum . AP, CG and SS supervised the study. AP and SS wrote the initial manuscript. All authors contributed to fruitful discussions and lively development of the manuscript. Correspondence and requests for materials should be addressed to Athanasios Papadopoulos, Sander Smits or Christoph G.W. Gertzen. Funding The research was supported by the German Reseach Foundation (DFG) through the Collaborative Research Center 1535 Microbial Networking (MibiNet, CRC/SFB 1535) Project ID 45809666, (Z01 to SWP and SS, B01 to JF and TD). The Center for Structural Studies (CSS) is funded by DFG, projects 417919780 and INST 208/761-1 FUGG. Center of Advanced imaging is funded by DFG, project 284074525 and project I3D:bio, DFG Grant Number: 462231789. Notes The authors declare no competing financial interest. Abbreviations MRE, Molecular recognition element FP, fluorescent protein FRET, Förster resonance energy transfer cpFP, circular-permutated fluorophore sfGFP, super-folder green fluorescent protein LSS, large stokes shift Single-FP, single-fluorescent protein or fluorophore CAN, constraint network analysis ENT FNC , ensembles of network topologies and fuzzy noncovalent constraints DtxR, Diphtheria toxin regulator protein BPD, 2,2’-Bipyridine ROS, reactive oxygen species Acknowledgement We would like to thank Wolf W. Frommer for inspiring and very valuable discussions and on sharing with us research objectives on Matryoshka biosensor design. Our special thanks to the Institute for Biochemistry for their friendly support in laboratory equipment. Further, we thank the European Synchrotron Radiation Facility for provision of synchrotron radiation facilities, and we would like to thank Dihia Moussaoui for assistance in using beamline BM29. Computational support and infrastructure were provided by the “Zentrum für Informations- und Medientechnologie” (ZIM) at Heinrich Heine University Düsseldorf. Funder Information Declared Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64 , CRC/SFB 1535 Project ID 45809666 , 417919780 , INST 208/761-1 FUGG , 284074525 , I3D:bio DFG Grant Number: 462231789 References 1. ↵ Frei , M.S. , Mehta , S. & Zhang , J . Next-Generation Genetically Encoded Fluorescent Biosensors Illuminate Cell Signaling and Metabolism . Annu Rev Biophys 53 , 275 – 297 ( 2024 ). OpenUrl CrossRef PubMed 2. Kim , H. , Ju , J. , Lee , H.N. , Chun , H. & Seong , J . Genetically Encoded Biosensors Based on Fluorescent Proteins . Sensors (Basel ) 21 ( 2021 ). 3. ↵ Wang , M. , Da , Y. & Tian , Y . Fluorescent proteins and genetically encoded biosensors . Chem Soc Rev 52 , 1189 – 1214 ( 2023 ). OpenUrl CrossRef PubMed 4. ↵ Greenwald , E.C. , Mehta , S. & Zhang , J . 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