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Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor

2019; Springer Nature; Volume: 15; Issue: 12 Linguagem: Inglês

10.15252/msb.20199071

1744-4292

Francisca Monteiro, Georg Hubmann, Vakil Takhaveev, Silke R. Vedelaar, Justin Norder, Johan Hekelaar, Joana Saldida, Athanasios Litsios, Hein J. Wijma, Alexander Schmidt, Matthias Heinemann,

Microbial Metabolic Engineering and Bioproduction

Method19 December 2019Open Access Transparent process Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor Francisca Monteiro Francisca Monteiro Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Georg Hubmann Georg Hubmann Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Vakil Takhaveev Vakil Takhaveev Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Silke R Vedelaar Silke R Vedelaar Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Justin Norder Justin Norder Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Johan Hekelaar Johan Hekelaar Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Joana Saldida Joana Saldida Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Athanasios Litsios Athanasios Litsios Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Hein J Wijma Hein J Wijma Biotechnology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Alexander Schmidt Alexander Schmidt Biozentrum, University of Basel, Basel, Switzerland Search for more papers by this author Matthias Heinemann Corresponding Author Matthias Heinemann [email protected] @HeinemannLab orcid.org/0000-0002-5512-9077 Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Francisca Monteiro Francisca Monteiro Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Georg Hubmann Georg Hubmann Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Vakil Takhaveev Vakil Takhaveev Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Silke R Vedelaar Silke R Vedelaar Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Justin Norder Justin Norder Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Johan Hekelaar Johan Hekelaar Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Joana Saldida Joana Saldida Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Athanasios Litsios Athanasios Litsios Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Hein J Wijma Hein J Wijma Biotechnology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Alexander Schmidt Alexander Schmidt Biozentrum, University of Basel, Basel, Switzerland Search for more papers by this author Matthias Heinemann Corresponding Author Matthias Heinemann [email protected] @HeinemannLab orcid.org/0000-0002-5512-9077 Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands Search for more papers by this author Author Information Francisca Monteiro1,4,‡, Georg Hubmann1,5,‡, Vakil Takhaveev1, Silke R Vedelaar1, Justin Norder1, Johan Hekelaar1, Joana Saldida1, Athanasios Litsios1, Hein J Wijma2, Alexander Schmidt3 and Matthias Heinemann *,1 1Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands 2Biotechnology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands 3Biozentrum, University of Basel, Basel, Switzerland 4Present address: cE3c-Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal 5Present address: Laboratory of Molecular Cell Biology, Department of Biology, Institute of Botany and Microbiology, KU Leuven, & Center for Microbiology, VIB, Heverlee, Flanders, Belgium ‡These authors contributed equally to this work as first, second authors *Corresponding author. Tel: +31 50 363 8146; E-mail: [email protected]; Twitter: @HeinemannLab Molecular Systems Biology (2019)15:e9071https://doi.org/10.15252/msb.20199071 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Metabolic heterogeneity between individual cells of a population harbors significant challenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence require tools to zoom into metabolism of individual cells. While methods exist to measure metabolite levels in single cells, we lack capability to measure metabolic flux, i.e., the ultimate functional output of metabolic activity, on the single-cell level. Here, combining promoter engineering, computational protein design, biochemical methods, proteomics, and metabolomics, we developed a biosensor to measure glycolytic flux in single yeast cells. Therefore, drawing on the robust cell-intrinsic correlation between glycolytic flux and levels of fructose-1,6-bisphosphate (FBP), we transplanted the B. subtilis FBP-binding transcription factor CggR into yeast. With the developed biosensor, we robustly identified cell subpopulations with different FBP levels in mixed cultures, when subjected to flow cytometry and microscopy. Employing microfluidics, we were also able to assess the temporal FBP/glycolytic flux dynamics during the cell cycle. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations. Synopsis The study presents a biosensor of glycolytic flux in single yeast cells, based on detecting fructose-1,6-phosphate (FBP) as a flux-signalling metabolite, using the bacterial FBP-responsive transcription factor CggR. FBP levels and glycolytic flux can be measured with flow cytometry and microscopy. A synthetic yeast promoter regulated by the FBP-responsive bacterial transcription factor CggR was developed. The FBP binding site of the transcription factor was engineered to increase the sensor's dynamic range. Growth-independent expression levels of the transcription factor were established. The sensor can be used to reveal differences in FBP levels and glycolytic flux. Introduction Increasing evidence suggests that individual cells in a population can be metabolically very different (Nikolic et al, 2013; van Heerden et al, 2014; Solopova et al, 2014; Kotte et al, 2015; Takhaveev & Heinemann, 2018). Metabolic heterogeneity has been found, for instance, not only in microbial cultures used for biotechnological processes (Xiao et al, 2016), but also in cells of human tumors (Strickaert et al, 2017). Because metabolic heterogeneity is connected with productivity and yield losses in biotechnological production processes (Xiao et al, 2016), and in cancer with limited therapeutic successes (Robertson-Tessi et al, 2015), it is key to identify metabolic subpopulations and to understand their emergence. Toward assessing metabolic heterogeneity, several novel experimental tools have recently been developed to measure metabolite levels in single cells (Qiu et al, 2019), e.g., by exploiting the autofluorescence of specific metabolites (Papagiannakis et al, 2016), Förster resonance energy transfer (FRET) (Hou et al, 2011), or metabolite-binding transcription factors (Mahr & Frunzke, 2016). For instance, transcription factor (TF)-based biosensors now exist to detect amino acids (Mustafi et al, 2012), sugars (Raman et al, 2014), succinate and 1-butanol (Dietrich et al, 2013), triacetic acid lactone (Tang et al, 2013), and malonyl CoA (Xu et al, 2014), partly enabled by the transplantation of prokaryotic metabolite-responsive TFs to eukaryotes (Ikushima et al, 2015; Li et al, 2015; Skjoedt et al, 2016; Wang et al, 2016; Ikushima & Boeke, 2017). While measurements of metabolite levels in single cells are already useful, knowledge of metabolic fluxes in individual cells would often be more informative, as metabolic fluxes represent the ultimate functional output of metabolism. Fluxes serve as predictor of productivity in the development of cell factories (Nielsen, 2003) or as indicator of disease (Zamboni et al, 2015). Here, particularly knowing the flux through glycolysis would be valuable, as this flux has been shown to correlate with highly productive phenotypes (Gupta et al, 2017) and cancer (Pavlova & Thompson, 2016). While nowadays metabolic fluxes can be resolved in ensembles of cells, for instance, by means of 13C flux analysis (Antoniewicz, 2015), inference of fluxes in individual cells, however, is not possible until today (Takhaveev & Heinemann, 2018). One possible avenue toward measuring metabolic fluxes in individual cells has recently emerged by the discovery of so-called flux-signaling metabolites (Litsios et al, 2018), which are metabolites, whose levels—by means of particular regulation mechanisms (Kochanowski et al, 2013)—strictly correlate with the flux through the respective metabolic pathway. Such flux signals are used by cells to perform flux-dependent regulation (Kotte et al, 2010; Huberts et al, 2012). Biosensors for such metabolites, such as recently accomplished for E. coli (Lehning et al, 2017), would in principle allow for measurement of metabolic fluxes in single cells, in combination with microscopy or flow cytometry. Here, drawing on the glycolytic flux-signaling metabolite fructose-1,6-bisphosphate (FBP) in yeast (Huberts et al, 2012; Hackett et al, 2016; preprint: Kamrad et al, 2019) and using the B. subtilis FBP-binding transcription factor CggR (Doan & Aymerich, 2003), we developed a biosensor that allows for sensing FBP levels, and thus glycolytic flux, in single yeast cells. To this end, we used computational protein design, biochemical, proteome, and metabolome analyses (i) to develop a synthetic yeast promoter regulated by the bacterial transcription factor CggR, (ii) to engineer the transcription factor's FBP-binding site toward increasing the sensor's dynamic range, and (iii) to establish growth-independent CggR expression levels. We demonstrate the applicability of the biosensor for flow cytometry and time-lapse fluorescence microscopy. We envision that the biosensor will open new avenues for both fundamental and applied metabolic research, not only for monitoring glycolytic flux in living cells, but also for engineering regulatory circuits with glycolytic flux as input variable. Results Design of biosensor concept For our biosensor, we exploited the fact that the level of the glycolytic intermediate fructose-1,6-biphosphate (FBP) in yeast strongly correlates with the glycolytic flux (Christen & Sauer, 2011; Huberts et al, 2012). Furthermore, we used the transcription factor CggR from B. subtilis, to which FBP binds (Doan & Aymerich, 2003). When bound to its target DNA, CggR forms a tetrameric assembly of two dimers, through which transcription gets inhibited (Zorrilla et al, 2007b). Upon binding of FBP to the CggR–DNA complex, the dimer–dimer contacts of CggR are disrupted (Zorrilla et al, 2007a), which decreases the overall CggR/operator complex stability, leading to increased CggR dissociation, and thus derepression of the promoter (Chaix et al, 2010). Here, we aimed to transplant the B. subtilis CggR to yeast and have it exerting FBP-dependent and thus glycolytic flux-dependent regulation of expression of a fluorescent protein. To this end, a number of challenges had to be addressed. First, a synthetic promoter had to be designed for the foreign transcription factor CggR, involving the identification of ideal positioning and number of operator sequences (Teo & Chang, 2014, 2015), and engineering the nucleosome architecture to allow for maximal promoter activity (Curran et al, 2014). Second, CggR had to be made responsive to FBP in the correct dynamic range, requiring protein engineering efforts (Raman et al, 2014; Rogers et al, 2015). Third, the CggR expression levels needed to be such that together with the metabolite-modulating effect on CggR, the TF can actually exert a regulating effect on the promoter, for which we needed to identify proper CggR expression levels (Fig 1). Figure 1. Illustration of the biosensor concept to measure glycolytic fluxes in single S. cerevisiae cellsExpression of the bacterial transcriptional repressor CggR at constant levels, i.e., independent of growth rate and substrates. Binding of CggR as a dimer of dimers to the operator (CggRO) of the synthetic cis-regulatory region, forming the CggR–DNA complex repressing transcription. At high glycolytic fluxes, fructose-1,6-bisphosphate (FBP) levels are high and FBP binds to CggR disrupting the dimer–dimer contacts, which induces a conformational change in the repressor, such that transcription of the reporter gene (YFP) can occur. The binding of FBP to CggR and consequent transcription is dependent on the FBP concentration, which correlates with glycolytic flux. The activity of the glycolytic flux biosensor is measured by quantifying YFP expression. YFP expression levels are normalized through a second reporter, mCherry, under the control of TEF1 mutant 8 promoter (PTEFmut8), to control for global variation in protein expression activity. Download figure Download PowerPoint In vivo test system for a substrate-independent and growth rate-independent flux sensor For later evaluation of the flux-reporting capacity of the developed sensor, we first established an in vivo test system, through which we could generate a range of glycolytic fluxes at steady-state conditions. To this end, we employed a combination of growth substrates and two different S. cerevisiae strains: the wild type (WT) and a mutant strain (TM6), which only carries a single chimeric hexose transporter and thereby only generates low glucose uptake rates at high glucose levels (Elbing et al, 2004). Metabolome and physiological analyses in combination with a new method for intracellular flux determination (Niebel et al, 2019) showed that this combination of strains and conditions allowed us to generate a broad range of glycolytic fluxes (Fig 2A). Consistent with the earlier reported correlation between FBP levels and glycolytic flux (Huberts et al, 2012), also here the FBP levels had a strong linear correlation with the flux [r = 0.97, (0.95, 0.99) 95% confidence interval] (Fig 2A), but not with growth rate (Fig 2B). This set of conditions and strains served as test system for the to-be-developed glycolytic flux sensor. Figure 2. FBP concentration linearly correlates with glycolytic flux, stronger than with growth rate Glycolytic flux of wild type (WT) and TM6 strains strongly correlates with fructose-1,6-bisphosphate (FBP) concentration. The glycolytic flux is reported here as the flux between the metabolites fructose 6-phosphate (F6P) and FBP. Glycolytic fluxes were obtained on the basis of physiological and metabolome data, and via a novel method to estimate intracellular fluxes (Niebel et al, 2019). While on high glucose, the WT strain accomplishes a high glucose uptake rate (and thus glycolytic flux), the mutant strain (TM6) only generates a low glucose uptake (and thus glycolytic flux). On maltose, also the mutant strain achieves a high glycolytic flux, since maltose is transported by a separate transporter (Chang et al, 1989). FBP concentration as a function of cellular growth rate shows weaker correlation. Data information: For metabolite levels and growth rates, error bars correspond to the standard deviation between three independent experiments, for glycolytic fluxes to the mean and standard deviations of the sampled flux solution space (cf. Materials and Methods). The carbon sources were used at a final concentration of 10 g/l and are indicated: glucose (GLU); galactose (GAL); maltose (MAL); and pyruvate (PYR). To assess the linear correlation between the FBP concentration and the glycolytic flux (A) or growth rate (B) across the studied conditions, we implemented Pearson's correlation analysis assisted by bootstrapping. Specifically, we used in total 53 FBP concentration measurements corresponding to six different metabolic conditions (combinations of strains and carbon sources), biological and technical replicates. We paired each of these FBP measurements with the mean and standard deviation of the model-derived glycolytic flux (A) or of the growth rate (B) in the corresponding metabolic condition. We assumed the normal distribution of the flux and growth rate with the given mean and standard deviation in every condition, and implemented ordinary non-parametric bootstrapping with 100,000 iterations by randomly sampling values with replacement from the 53 FBP measurements and flux or growth rate distributions to calculate the correlation statistics. In (A), Pearson's coefficient was found to be 0.97 with [0.95, 0.99] as the 95% confidence interval, and a P-value smaller than 2.23e-308 (normal bootstrap). In (B), Pearson's coefficient was found to be 0.73 with [0.64, 0.80] as the 95% confidence interval, and P-value equal to 2.28e-77 (normal bootstrap). Download figure Download PowerPoint Development of the synthetic CggR cis-regulatory element First, we designed a synthetic CggR cis-regulatory element for yeast (CggRO) based on the CYC1 promoter, which was previously successfully re-designed (Curran et al, 2014). To accomplish repression of the promoter by CggR, we aimed to shield the TATA boxes by the binding and tetramerization of the CggR dimers. The CYC1 core promoter has three TATA boxes at the positions −221, −169, and −117, upstream of the open reading frame (Fig 3—upper part). We flanked the two TATA boxes at positions −221 and −117 up- and downstream with a CggR operator site. To conserve the geometry of the CYC1 core promoter as much as possible, we removed the TATA box at position −169, because this TATA box was exactly located where we integrated the CggR operator sites flanking the other TATA boxes, and we did not want to make the sequence longer. The 5′UTR of the CYC1 promoter, which also included the transcriptional start site, was kept. To allow for sole binding and regulation through CggR, we removed the part further upstream of the TATA box at the position −221 where, according to YEASTRACT (Teixeira et al, 2014), the endogenous transcriptional binding sites of the CYC1 promoter are located. Figure 3. Design of the synthetic CggR cis-regulatory elementThe promoter design is based on the CYC1 core promoter. The relevant structural elements of the CYC1 core promoter elements, which are required for transcription, were conserved in the synthetic promoter design. These elements comprised two TATA boxes at positions −221 and −117 (relative to the start of the CYC1 ORF), and the 5′UTR of the CYC1 core promoter (including transcriptional start site, TSS). In the promoter design, three CggR operator sites were inserted adjacent to the two TATA boxes. All functional elements were conserved (blue colored region) during the optimization of the promoter sequence. Nucleotide sequences between the functional elements (gray colored region) were allowed to be optimized by the algorithm. Nucleotides that got optimized are indicated with a black line. A total of 75 sequence versions were generated, where each sequence differed in one mutation from the progenitor sequence. The sequences were optimized for low nucleosome affinity. After optimization, all sequences were checked for synthesis feasibility. The synthesis of the sequences was feasible (green) until the 46th round. After this round, the sequences (not feasible in red) reached a GC content insufficient for proper synthesis. The promoter sequence, which was generated in round 38 (black), showed the best compromise between minimal nucleosome affinity and the possibility to synthesize the sequence. Download figure Download PowerPoint Using a computational method (Curran et al, 2014), we further optimized this designed sequence of the CggR cis-regulatory element to minimize nucleosome binding. Functional elements (e.g., the CggR operator sites, the TATA boxes, and the 5′UTR; cf. Appendix Tables S1 and S2) were excluded from the sequence optimization (Fig 3—lower part). A total of 75 computational optimization rounds were applied. As the CggR cis-regulatory element resembled a repetitive DNA sequence with a high AT content, sequence variants were checked for DNA synthesis feasibility. The cis-regulatory element of round 38 was the variant with the lowest nucleosome affinity but with retained feasibility for DNA synthesis. The synthesized synthetic promoter was integrated upstream of the fluorescent reporter protein YFP (eCitrine) in a centromeric plasmid ensuring a stable copy number. Establishing a substrate-independent and growth rate-independent CggR expression Next, to drive expression of CggR, we needed a promoter that would lead to condition-independent (i.e., constant) intracellular CggR levels in order to ensure that the flux sensor only reports altered FBP levels (i.e., glycolytic fluxes), and not altered CggR levels. To this end, we tested the PCMV promoter, which is widely used as a strong constitutive promoter in mammalian cells (Boshart et al, 1985), and two mutant variants of the endogenous TEF1 promoter, i.e., mutant 2 (PTEFmut2) with low, and mutant 7 (PTEFmut7) with medium-to-high expression strength (Nevoigt et al, 2006). Each promoter and the CggR gene were cloned into the HO genomic locus of both yeast strains. To quantify the CggR protein levels, we performed proteome analyses with the different strains, promoters, and growth conditions. Overall, the three promoters yielded largely different CggR abundances on glucose (Fig 4A). Across conditions and growth rates, we found that the CggR levels when expressed from the PCMV and PTEFmut2 promoters showed significant variations, while the PTEFmut7 promoter generated more comparable CggR levels across growth rates (Fig 4B), as established through the different carbon sources and strains. Because of its more condition-independent expression level, we selected the PTEFmut7 promoter to drive the CggR expression. Figure 4. CggR intracellular levels and expression profile with different promoters, strains, and conditions CggR intracellular abundance in the wild-type (WT) strain on glucose strongly varies with the promoter used. The CggR intracellular levels were quantified by proteomics in steady-state cultures grown in minimal media with glucose as carbon source at a final concentration of 10 g/l. Error bars represent the standard deviation of at least three replicate experiments. The relative abundance of CggR (normalized to the abundance measured on glucose and the same promoter) is almost constant with PTEFmut7 across multiple growth rates in WT and TM6 cells, but not with PTEFmut2 and PCMV. The CggR intracellular levels were quantified by proteomics in steady-state cultures grown in minimal media with glucose, galactose, maltose, or pyruvate as carbon sources at a final concentration of 10 g/l. WT data include all three promoters, whereas TM6 only includes the PTEFmut2 and PTEFmut7 data. Error bars represent the standard deviation of at least three replicate experiments. Download figure Download PowerPoint Engineering the FBP affinity of CggR Next, we needed to engineer the FBP binding to CggR, such that it matches with the physiological range of FBP levels. FBP levels in yeast range from 0.2 mM to around 8 mM (Fig 2A). As the wild-type CggR has an affinity for FBP of around 1 mM (Bley Folly et al, 2018), we needed to generate a CggR mutant with a slightly lower affinity for FBP, and with ideally a graded interaction between CggR and FBP toward accomplishing a broad dynamic response range of the sensor. Importantly, the engineered CggR would still need to bind to the DNA, and furthermore, the protein should be stable to not affect its cellular abundance. To obtain such a CggR mutant, supported by computational protein design methods, we identified mutations at the CggR–FBP-binding site that could lead to the desired decrease in affinity. Specifically, as in the CggR structure (3BXF) (Rezácová et al, 2008) CggR binds to FBP through hydrogen bonds, and we designed mutations to weaken or disrupt H-bonding interactions (Table 1, Appendix Table S3), with the aim to decrease binding affinity. The X-ray structure further showed that FBP binding causes a conformational change in CggR (Rezácová et al, 2008), where a loop between residues G177 and Q185 moves away from the FBP-binding site toward another subunit. On the basis of this, we conjectured that mutations might not only influence FBP binding, but also alter the equilibrium between the normal and activated conformation, even in the absence of FBP. To predict the effect of the mutations on this equilibrium, and on overall protein stability, we used FoldX (Guerois et al, 2002), where we found that a E269Q mutation could decrease overall stability while R175K could permanently shift CggR to its activated conformation (Table 1). Four mutations (i.e., T151S, T151V, T152S, and R250A) were thus identified as promising candidates for decreasing the FBP binding to CggR without otherwise negative effects (Table 1). Table 1. List of predicted mutations to alter CggR-FBP-binding affinity and stability Mutation Expected effects on affinity for FBP (and if relevant on equilibrium and stability)a FoldX predicted stability changes (kJ/mol)b ∆∆Gfold for the normal conformation ∆∆Gfold for the activated conformation ∆∆∆Gfold (between the conformations) T151S Negligible to mild affinity decrease −1.4 −1.3 0.1 T151V Mild to strong affinity decrease 2.5 3.1 0.5 T152S Negligible to mild affinity decrease 7.3 4.6 −2.7 R175K Negligible to strong affinity decrease and possibly a shift of equilibrium to the activated conformation 16.5c −0.7 −17.2c R250A Mild to strong affinity decrease 2.2 2.7 0.5 E269Q Mild to strong affinity decrease for FBP in combination with overall destabilization 16.4c 11.4c −5.0 a A detailed justification for the expected effects of the mutations on binding affinity is given in Appendix Table S4. b A downshift in ∆∆Gfold predicts stabilization of the protein, while a downshift in ∆∆∆Gfold predicts that the FBP conformation becomes more favorable. ∆∆∆Gfold represents the difference between the ∆∆Gfold values for the two conformations. c Values are significantly higher than the standard deviation of FoldX predictions, which equals 3.4 kJ/mol (Guerois et al, 2002). We generated these CggR mutants with site-directed mutagenesis, expressed in E. coli, purified, and biochemically characterized them. To this end, we used thermal shift assays to assess protein stability and ligand binding. Most of the engineered CggR variants maintained wild-type stability, with the exception of E269Q (consistent with the above analysis) and T151V, which were less stable as indicated by decreased melting temperatures (Fig 5A). While the wild type had a KD of 1 mM FBP, the mutants T151S, E269Q, and T152S showed a 1.1-, 1.5-, and 1.6-fold lower KD values, respectively, while the KD values of the R250A and T151V mutants increased 1.5- and 2.6-fold (Fig 5B). Figure 5. Biochemical characterization of CggR and respective mutants Thermal shift assays were used to determine the melting curves of wild-type CggR and mutants. Error bars correspond to the standard deviation of at least five replicates. CggR-FBP affinity constants (KD's) of the wild type and mutant variants, determined by fitting a simple cooperative binding model to the melting curv