Computational design of allulose-responsive biosensor toolbox for auto-inducible protein expression and CRISPRi mediated dynamic metabolic regulation

Domain: chemistry
Published: 2025-12-22
Editorial outcome: minor_revision

Abstract
Biosensors based on transcription factors (TFs) have shown extensive applications in synthetic biology. Due to the complex multi-domain structure of effector-TF-DNA, computational design of TFs remains a challenge. Here, we present the successful structure-guided computational design of the access tunnel, ligand binding, allosteric transition process for an allulose-responsive PsiR. It enables a 20-fold increase in sensitivity, reducing the EC50 of PsiR-allulose biosensors (PABs) from 16 mM to 0.8 mM, and delivers a PAB box possessing the detection range from 10 μM to 100 mM. We further validate its broader applicability in enhancing sensitivity of LacI-IPTG biosensor. Based on the developed PABs, we present the inducer-free allulose-mediated auto-inducible protein expression system, and demonstrate an allulose-triggered CRISPR interference circuit for dynamic metabolic regulation. It facilitates a 68% increase in allulose titer and achieves a high yield of 0.43 g/g glucose. This work provides the versatile TF toolbox for developing allulose-triggered regulation circuits in biotechnology application.

Introduction
Transcription factors (TFs) represent a large class of regulators in cells to sense and adapt the changes of environmental and intracellular conditions1. The transcription-factor-based biosensors (TFBs) have shown great promise in biomanufacturing applications, including high-throughput screening2, microbial strain engineering3, metabolic dynamic control4, protein engineering5, and plug-and-play small molecule detection6. Although substantial advances have been made in discovering natural TFs with the aid of gene sequencing and bioinformatics, the TFBs based on wild-type TFs often suffer from limited output and unsuitable detection ranges7. These limitations arise from two main issues: i) the natural elements, such as promoters and RBSs, are often incompatible with non-native hosts; and ii) TFs themselves frequently lack the desired dynamic range, sensitivity, and specificity.

To address these issues, strategies such as promoter and RBS optimization, as well as adjustment of plasmid copy number, have been employed to enhance TF expression in non-native systems8–10. In addition, directed evolution and site-directed mutagenesis have been used to alter effector specificity11–13; however, screening large mutational libraries is time-consuming and labor-intensive. Mechanistically, TF proteins perform the response process through three steps: i) effector access and binding to effector-binding domain (EBD); ii) allosteric transition from EBD to DNA-binding domain (DBD), and iii) conformational changes in the DBD that lead to dissociation from the DNA14. Due to the complex multi-domain structure of the effector-TF-DNA and unclear activation-allostery mechanisms, engineered TFs often suffer from high rates of inactivation (Is phenotype)15–17.

To address these problems, computational design and engineering of TFs offers a promising yet challenging strategy.

Efficient and scalable protein expression systems are indispensable for manufacturing industrial enzymes and protein drugs18–20. Among these, the TF-based inducible expression systems offered a powerful solution. A preferred inducible system should have several key advantages: i) high sensitivity and broad dynamic range; ii) use of inducers that are inexpensive, stable, non-toxic, and minimal interference with cell metabolism; iii) ease of construction and applicability across diverse microbial hosts. To date, the LacI-IPTG system is the most widely used inducible platform in bacteria21–23. However, high cost, cell toxicity, and instability of inducer IPTG impeded its practical application in large-scale fermentation24,25. Other inducible expression systems based on AraC-arabinose26, XylR-xylose27, or MalR-maltose28 have also been developed, yet they still face challenges such as inducer metabolic consumption, media component interference, and glucose-induced carbon catabolite repression. Recently, PsiR, a TF from Agrobacterium tumefaciens that responds to D-allulose, has attracted attention29. Notably, the inducer D-allulose, recognized as a safe sugar in food and dietary supplements, could not be metabolized by several model strains, including Escherichia coli, Bacillus subtilis, Corynebacterium glutamicum, and yeasts30. Large-scale production of D-allulose has already been industrialized31 with a cost of just CNY 20 per kg—~100 times cheaper than IPTG (CNY 2500 per kg). These features make the PsiR–allulose pair an attractive basis for constructing inducible systems. However, the native PsiR suffers from low sensitivity (EC50 ≈ 20 mM) and a limited dynamic range32. This highlights the need for in-depth engineering of the PsiR-allulose system to enhance its response features. However, it remains a challenge due to the limited knowledge of crystal structure and binding mechanism of PsiR, and even scarce methods for rational design.

In this work, we demonstrate the successful rational design of PsiR and established a flexible framework for allulose-mediated auto-inducible protein expression and autonomous dynamic gene regulation (Fig. 1). We first construct a D-allulose-responsive system in Corynebacterium glutamicum and systematically optimize the gene expression of PsiR and reporter enhanced green fluorescent protein (eGFP) to ensure robust signal output. Then, we present the structure-guided computational design and engineering of the access-binding-allostery process of PsiR, enabling decreasing the EC50 of the resulting PsiR-allulose biosensors (PABs) from 16.7 mM (the wild-type) to 0.8 mM. We further uncover the underlying mechanism and validate the feasibility of the computer-guided tunnel engineering approach to enhance the signal output of LacI, highlighting its broader applicability to other LacI-family TFs. Building upon this, by integrating an allulose biosynthetic pathway and the PsiR-allulose sensor in a single cell, we establish an inducer-free allulose-mediated auto-inducible expression (AMAE) system for mannose isomerase production. In parallel, we develop an allulose-triggered CRISPR interference (CRISPRi) circuit (ATCi) to dynamically repress zwf, encoding glucose-6-phosphate dehydrogenase in the pentose phosphate pathway, achieving a higher conversion yield of 0.43 g/g glucose than the traditional enzymatic biotransformation method (0.18 g/g glucose). Collectively, this study provides a versatile allulose-responsive biosensor toolbox and presents a modular framework for developing gene regulation circuits in metabolic engineering and synthetic biology.

Results
Construction of allulose-responsive biosensors in C. glutamicum
Typically, a TF-based biosensor system is composed of two main components: one expression cassette for expression of the TF, and the other containing a TF-binding operator to drive the expression of the reporter gene. In this work, we aimed to construct an allulose-responsive biosensor in C. glutamicum. To this end, a constitutive promoter Ptuf was employed to express the TF PsiR, while a series of hybrid promoters were designed by inserting the PsiR-binding operator sequence PsiO (ATTGCACAATCGATGGTGCAA) into different positions of the Ptuf and PtacM promoters33 to control the expression of eGFP (Fig. 2a and Supplementary Table 1). The fluorescence output of the resulting biosensors (PABs) was measured in response to varying allulose concentrations ranging from 0 to 200 mM. The results showed that inserting the PsiO sequence between the −10 box and the ribosome binding site (RBS) enabled allulose sensing (Fig. 2b and Supplementary Fig. 1). Among these, PAB6, in which the PsiO replaced the lacO site in the PtacM, exhibited the highest signal-to-noise ratio (19-fold at 0/50 mM allulose). However, its overall fluorescence output remained low, likely due to strong repression of eGFP expression by PsiR. To address this, we fine-tuned PsiR expression using a set of eleven promoters with varying strengths34 (Fig. 2c and Supplementary Table 2). Indeed, strong promoters such as Ptuf, PH36, PH9, and PH5 led to the tight repression of eGFP, resulting in low fluorescence even at 200 mM allulose (Fig. 2d). In contrast, weak promoters like PL26 and PL80 generated higher fluorescence but caused undesirable basal leakage. Notably, PAB16, which used a medium-strength promoter PM10 for PsiR expression, increased the maximum fluorescence by 20% compared to PAB6 while maintaining strict repression in the absence of allulose.

To further enhance the dynamic range and signal output, we next optimized PsiR and eGFP expression at the translational level. For eGFP, the RBS of PtacMD in PAB16 was replaced with eleven alternative RBS sequences (Fig. 2e and Supplementary Table 3). PAB27 exhibited a 77% increase in fluorescence output compared to PAB16, while maintaining very low basal leakage expression (Fig. 2f). To refine PsiR translation, we constructed a mutant library by replacing the spacer sequence (TACTT) between the RBS of PM10 and initiation codon (ATG) in PAB27 with five random nucleotides (NNNNN) (Fig. 2g). After screening 1,400 strains under 0/50 mM allulose, 78 strains with improved signal output relative to PAB27 were identified. Four biosensors, named as PAB28-PAB31, were selected based on their superior fluorescence fold-change values (Supplementary Figs. 2, 3) and were further characterized across a gradient of allulose concentrations, with their responses were fitted to the Hill equation. Among them, PAB30 exhibited a 30% increase in maximum signal compared to PAB27 while maintaining low background expression. Overall, by systematically optimizing both PsiR and eGFP expression, PAB30 achieved an 86% improvement in maximum fluorescence relative to the initial PAB6. Furthermore, its fluorescence dynamic range reached 59.3-fold, and the EC50 for allulose was reduced to 16.7 mM (Fig. 2h and Supplementary Table 4).

Overall structure of PsiR and its binding mode
To date, the crystal structure of PsiR and its binding conformations with allulose and the operator sequence PsiO have remained unknown. To better understand the molecular basis of PsiR, we attempted to determine the full-length crystal structure of the PsiR–allulose complex, but were unsuccessful. Notably, several reported crystal structures of LacI family TFs, such as LacI (PDB: 2P9H), RbsR (PDB: 4RK1), SghR (7CDX), and Cra (PDB: 3O75), lack the N-terminal DNA-binding domain (DBD), which is responsible for DNA recognition. Inspired by the truncated construct of LacI structure (PDB: 2P9H), we introduced an N-terminal truncation in PsiR at Ser69. This strategy enabled us to successfully solve the crystal structure of the PsiR–allulose complex at 2.02 Å resolution using X-ray crystallography (PDB: 9KPR; Supplementary Fig. 4 and Table 5). The overall structure forms a homodimer, with each monomer consisting of a C-terminal core domain that governs both ligand binding and allosteric transitions. Although PsiR shares only 24.8% sequence identity with LacI, it adopts a similar three-dimensional structure similar to that of LacI and other members of the LacI-family TFs (Supplementary Fig. 5).

We have also attempted to reveal the binding model between PsiR and PsiO by solving the complex crystal structure of PsiR-PsiO, but failed. As an alternative, we obtained the complete complex structure based on 9KPR with the aid of AlphaFold335 (Fig. 3a). The predicted structural model was quantitatively assessed using two key metrics: i) the root-mean-square deviation (RMSD) of PsiR and DNA converged within 200 ns and remained stable thereafter; and (ii) the center-of-mass (COM) distance between the DBDs and the conserved 20-bp core region of PsiO remained constant (Supplementary Fig. 6). The DBD adopts a helix-turn-helix (HTH) motif and is connected to the core domain via a flexible hinge region (residues 46–66). In the modeled structure, the HTH of PsiR is embedded into the major groove of the PsiO double-stranded DNA. Several polar residues, including S21, S23, R36, R37, R39, R54, and R65, may interact with the PsiO operator sequence (TTGCACCATC) via hydrogen bonds and salt bridges (Fig. 3b). The long-range molecular dynamics (MD) simulation analysis lasting 1 μs revealed that the PsiO operator region maintained high structural stability during the in silico simulation process compared to the flexible HTH domain (Fig. 3c). Upon PsiO binding, PsiR induces a pronounced bend (from 180° to ~130°) in the DNA helix through electrostatic and van der Waals interactions (Fig. 3d and Supplementary Fig. 7), which was consistent with structural features also observed in the LacI-DNA complex36.

The crystal structure of the PsiR-allulose complex revealed that the allulose is stabilized by forming strong hydrogen bond interactions with surrounding residues, which was further supported by residue–ligand interaction energy analysis (Fig. 3e, f). To validate the functional relevance of these residues, we further conducted alanine-scanning mutagenesis on these residues. Although mutations of P77A, N81A, and F84A retained allulose responsiveness and even enhanced the maximum signal output, their basal leakage also significantly increased relative to wild-type (Fig. 3g). Mutations of F83A, D155A, R208A, Y233A, and D288A completely abolished the allulose response, resulting in an Is phenotype. It indicates that these residues are critical for ligand binding or allosteric signal transmission. In particular, D288 appears to play a crucial role in ligand stabilization through strong hydrogen bonding, as confirmed by saturation mutagenesis at this site (Supplementary Fig. 8). Interestingly, the residues D155, R208, and D288 in PsiR are also conserved in the ligand-binding sites of other carbohydrate-responsive LacI-family TFs, including LacI (PDB: 2P9H), RbsR (PDB: 4RK1), Cra (PDB: 3O75), and CelR (PDB: 5YSZ) (Fig. 3h). Previous studies have shown that mutations at homologous residues, such as D149 residue in LacI and R205 in CelR, could disrupt substrate binding and allosteric transitions37,38, This indicates that the conserved D–R–D/R triad may play a crucial role in effector binding and allosteric activation within the LacI family TFs.

Computer-aided tunnel engineering of PsiR
It was noted that TFs perform the response function primarily rely on ligand-induced conformational changes and allosteric transitions. The residues involved in such allosteric processes are often distributed throughout the protein structure16, and untargeted mutagenesis is prone to loss-of-function phenotypes and reduced biosensor performance. Given the currently unknown topological trajectory of the allosteric transition in PsiR, we sought to deconstruct the response process into four sequential steps (Fig. 4a): i) allulose recognition and entry into the EBD; ii) allulose binding to target residues in the EBD and triggering the conformational rearrangement; iii) allosteric transition within EBD and to DBD; iv) dissociation of HTH motif from the PsiO operator, leading to eGFP expression. In this context, the residues acting on those processes may serve as candidates for engineering.

From this framework, we prioritized the engineering of effector access (step i), reasoning that modifying allulose entry access is less likely to perturb the delicate intramolecular allosteric network17. Based on the PsiR-allulose complex crystal structure, we analyzed the potential access tunnels using CAVER 3.039. Among the identified tunnels, the tunnel 1 (Fig. 4b), comprising residues R109, V134, T135, D155, and D202, exhibited the widest bottleneck radius, lowest curvature, and highest flux, and was therefore considered the primary tunnel for inducer movement. To investigate the interactions between allulose and the tunnel-lining residues during allulose translocation, steered molecular dynamics (SMD) simulations were conducted by applying a pulling force to guide allulose from the EBD along the predefined tunnel 1. A collective variable, d1, was defined as the COM distance between allulose and the key active-site residue D288. The interaction energies (ΔE (kcal/mol)) between allulose and individual residues along the route were calculated to identify potential hotspot residues (Supplementary Fig. 9). The D155, a conserved component of the D–R–D triad critical for effector recognition in LacI-family TFs (Fig. 3h), was excluded from engineering targets. Residues R109 and D202 displayed strong interaction energies with allulose during the SMD process (Fig. 4c, Supplementary Fig. 9), likely due to their long polar side chains and the formation of salt bridges in the crystal structure (Fig. 4d). Such strong interaction between these two residues would hinder ligand movement through the tunnel. To reduce steric hindrance and electrostatic barriers, we performed in silico mutagenesis of R109 to smaller and less charged residues (Ala, Ser, Gly) and analyzed the interaction energies after SMD simulations (Fig. 4c, Supplementary Fig. 10). To monitor the salt bridge interaction between R109 and D202 during allulose translocation for PsiR-WT and its mutants, the collective variable, d2, was defined as the COM distance between the carboxyl group of D202 and guanidyl group of R109. These substitutions significantly reduced interaction energies and expanded the tunnel bottleneck diameter from 5–6 Å to ~10 Å (Fig. 4d), thereby lowering the free energy barrier and increasing the possibility for allulose entry. Experimental validation revealed that R109A substantially enhanced biosensor responsiveness to 10 mM allulose (Fig. 4e). Notably, mutants R109S and R109G not only improved the maximal fluorescence signal but also reduced basal leakage compared to R109A. Relative to wild-type PsiR, the R109G variant doubled the fluorescence output and increased the dynamic range (fold change) by 64% to 25.7 under 0/10 mM allulose. In contrast, while the D202A mutant exhibited tight repression in the absence of inducer, it failed to respond to allulose, suggesting that D202 may participate in allosteric transition. Collectively, these findings demonstrate that rational remodeling of the ligand entry tunnel—particularly via modulation of salt-bridge-forming residues—offers an effective strategy to fine-tune biosensor sensitivity and dynamic range.

Encouraged by the improved sensor performance of R109G, we next investigated another tunnel-lining residue, V134, which also displayed high interaction energy with allulose in SMD simulations (Fig. 4c). Mutagenesis at this site—V134A, V134S, and V134G—led to a consistent enhancement in both signal output and dynamic range compared to the wild-type PsiR (Fig. 4e). Notably, V134A conferred the highest on/off ratio, while V134G yielded the strongest fluorescence signal under 10 mM allulose. To further amplify sensor sensitivity at lower inducer concentrations, we combined R109G with these beneficial V134 variants. Among the resulting double mutants, the R109G/V134A combination (designated M2) demonstrated very low basal leakage expression in the absence of inducer, alongside improved allulose responsiveness compared with that of R109G under a lower allulose concentration of 5 mM (Fig. 4f). Specifically, M2 achieved a dynamic range of 31.5-fold at 0/5 mM allulose, representing a 2.9-fold enhancement relative to the wild-type.

Further structure analysis revealed that T135 is located at the entrance of the primary access tunnel, functioning as a gating residue that potentially influences ligand recruitment (Supplementary Fig. 11a, b). SMD-based interaction analysis in both WT and M2 backgrounds confirmed strong interaction energies between T135 and allulose (Supplementary Fig. 11c), suggesting its critical role in tunnel accessibility. To alleviate this bottleneck, saturation mutagenesis of T135 was performed in the M2 background. While substitutions with small residues (Ala, Ser, Gly) increased signal output at 5 mM allulose, they also significantly elevated background leakage in the absence of allulose (Supplementary Fig. 12). In contrast, bulkier substitutions (Met, Tyr, His, and Arg) at position 135 in variants M2-T135M, M2-T135Y, M2-T135H, and M2-T135R maintained lower background noise compared to M2. Among them, M2-T135H achieved a dynamic range (fold-change at 0 vs. 5 mM allulose) of 52, surpassing that of M2. However, this enhancement in regulation was accompanied by a 31% reduction in signal output relative to M2 (Supplementary Fig. 12).

To elucidate the evolution of enhanced sensitivity and dynamic range of M2, we employed two-dimensional well-tempered metadynamics-extended adaptive biasing force (WTM-eABF) simulations. This approach is effective for resolving multidimensional and rugged free energy landscapes and has been successfully applied to investigate dynamic processes, including glucan–enzyme interactions and protease catalysis40–42. To characterize the free energy landscape associated with allulose translocation, we introduced the third collective variable, d3, defined as the distance between allulose and residue R109, which reflects the local interaction strength at this key engineering site. Concurrently, the collective variable, d1, was used to monitor the distance between allulose and D288, capturing its dissociation path from the binding site to the solvent-exposed state. Based on these variables, we computed and compared the two-dimensional free energy landscapes (ΔG (kcal/mol)) as functions of d1 and d3 for PsiR-WT, PsiR-M1 and PsiR-M2 (Fig. 4g). Free energy surfaces were derived from >600 ns of simulation per variant. As expected, R109G significantly broadened the low-energy region and reduced the energetic barrier for allulose access by 1.49 kcal/mol (Supplementary Fig. 13). By comparing the highlighted least free-energy routes, the interaction between R109G and allulose decreased, resulting in a rightward shift far away from R109, confirming the reduced local constraint. Further incorporation of V134A in M2 lowered the post-R109 barrier, suggesting a smoother transition from entry to binding. Therefore, the enhanced sensing characteristics of M2 may be due to the reduced interaction barrier in the allulose access. In addition, more access tunnels were discovered for mutant PsiR-M2 (Fig. 4b), allowing the mutated PsiR to be triggered at lower allulose concentrations.

Computer-aided tunnel engineering of LacI
Our results demonstrate the feasibility of computer-aided tunnel engineering for enhancing sensor sensitivity, signal output, and dynamic range of PsiR through the screening of a focused mutational library. To assess the generalizability of this strategy across the LacI transcription factor family, we applied the same approach to engineer LacI for improved signal output and sensitivity. We first constructed a LacI-based eGFP reporter sensor in C. glutamicum. Then, the SMD simulations were performed by pulling IPTG along the most likely tunnel pathway, and interaction energies between IPTG and individual tunnel-lining residues were analyzed (Supplementary Fig. 14). This identified five residues of D149, N125, S101, S69, and S70 exhibiting strong interactions with IPTG. The D149 and N125 were excluded from mutagenesis due to the conserved D–R–D/R triad in ligand binding and allosteric regulation37. To reduce steric and electrostatic hindrance at R101, we substituted it with smaller residues (Ala, Gly, Ser). Among these variants, the R101A and R101S mutants exhibited a 2–3 fold increase in fluorescence output at 10 μM IPTG compared to the wild-type LacI (Supplementary Fig. 15).

Further analysis revealed that residues S69 and S70, located at the entrance of the access tunnel, also interact with IPTG. Since these positions are already occupied by the small amino acid serine, we performed saturation mutagenesis to comprehensively explore potential improvements at these sites. Eighteen mutants increased the signal output by 2.0–4.8 fold (Supplementary Fig. 16). Among these, the mutant S70M yielded the highest output but exhibited elevated basal leakage in the absence of IPTG. In contrast, the S69C, S70T, and S70N maintained a low basal leakage level comparable to that of the wild-type, while still achieving 2–3.2 fold enhancement in signal output. Furthermore, those four beneficial single mutants (R101S, S69C, S70T, and S70N) were further characterized across a gradient of IPTG concentrations, with their responses were fitted to the Hill equation (Supplementary Figs. 15b, 16b, d). The results confirmed the increased dynamical range and sensitivity of engineered mutants relative to wide-type. These findings further validate the utility of computational tunnel engineering as a generalizable strategy for tuning ligand sensitivity in LacI-family transcriptional biosensors.

Rational engineering of binding pocket and allosteric sites
After alleviating the steric restriction in the allulose access tunnel, we then attempted to rationally engineer key residues involved in ligand binding in EBD and allosteric transition from EBD to DBD (Fig. 4a). It was suggested that enhancement of allulose binding via a suitable conformation in the binding pocket would generate more robust triggering response ability. According to our alanine scanning results in EBD, residues P77, N81, F84, and M87 may be less conserved in PsiR (Fig. 3g). Therefore, using Rosetta-based protein design, we computationally redesigned these positions and selected 11 single-point mutants with improved interface binding energies (Fig. 5a, b). Functional assays revealed that seven variants exhibited a 1.2- to 2.4-fold increase in fluorescence response under 10 mM allulose compared to the wild-type sensor (Fig. 5c). Notably, the N81T mutation not only enhanced the signal output by 23% and expanded the regulatory dynamic range (from 15.1 to 18.8), but also preserved a low basal leakage expression level, making it a promising candidate for further biosensor development.

Effector binding at the EBD allosterically triggers the dissociation of PsiR from its operator site (PsiO), yet the structural basis for topological propagation from the EBD to the distant DBD—particularly across the hinge and helix-turn-helix (HTH) motifs—remains unclear but is meaningful to explore. To investigate this long-range communication, we performed long time-scale MD simulations of the PsiR–allulose complex and applied principal component analysis (PCA) to the Cα atom trajectories of the homodimer (Supplementary Fig. 17). The dominant conformational changes centered in the loop between β4–β5 and helices α6–α7 within the EBD’s N-terminal subdomain, the helices α10–α11 in the C-terminal subdomain, as well as the junction regions (166–168) between the N-terminal and C-terminal subdomains. These dynamic regions may serve as structural conduits for allosteric transition, mediating the conformational switch required for PsiR function.

To further elucidate the intramolecular interaction landscape within PsiR, we computed dynamic cross-correlation matrices (DCCMs) for both apo and allulose-bound states43. In the absence of ligand, strong positive correlations were primarily confined within each of the N- and C-terminal subdomains, while weak inter-subdomain anticorrelated motions were observed (Fig. 5d–f). Upon allulose binding, the internal correlations within the N-terminal subdomain were further strengthened, accompanied by a marked increase in dynamic anticorrelations between the N- and C-terminal subdomains. These changes indicate a more coordinated and functionally relevant dynamic shift upon ligand binding. Notably, dynamic correlations for N- and C-terminal subdomains were separated at the junction region, suggesting it serves as a critical conduit for allosteric transmission. Based on this observation, residue N167 at the domain interface was selected for saturation mutagenesis.

In a hydrogen–deuterium exchange mass spectrometry (HDX/MS) study, Glasgow et al. systematically profiled allosteric conformational changes across the full core domain of LacI44–46. Their analysis revealed increased structural rigidity in several regions adjacent to the ligand-binding pocket in the IPTG-bound state compared to the DNA-bound state, suggesting these segments as potential hotspots for allosteric engineering. Through structural alignment of PsiR with LacI, we identified a corresponding segment (residues G199–R206 in PsiR; homologous to G187–R195 in LacI) located near the allulose-binding site within the C-terminal subdomain (Supplementary Fig. 18). Furthermore, root-mean-square fluctuation (RMSF) analysis revealed greater stability in this region under the PsiR–allulose state compared to the PsiR–DNA binding mode (Supplementary Fig. 19). However, residue R206 exhibited increased flexibility relative to surrounding residues. To enhance the local rigidity of this segment, we conducted saturation mutagenesis at position R206.

Experimental screening of these mutants revealed that substitutions N167A and N167S both enhanced signal intensity and dynamic range compared to the wild-type. Several R206 variants also showed improved performance, among which R206K notably doubled the fluorescence signal and improved the regulatory fold by 16%, while maintaining minimal background noise (Fig. 5g). Conversely, substitution of R206 with proline abolished activity, highlighting the importance of maintaining a certain flexibility at this site. The results emphasize the importance of the junction region of the N- and C-terminal subdomains and its role as an allosteric transmission conduit, offering an effective target for enhancing biosensor performance.

Next, we combinatorially introduced the beneficial single-site mutants of N81T, N167A, and R206K into M2 and evaluated its performance at a reduced allulose concentration (2 mM). Among the resulting tri-mutant variants, mutants R109G/V134A/N167A (M3) and R109G/V134A/R206K showed the increased signal output relative to M2 (Fig. 5h). Mutation of R109G/V134A/N81T did not have positive effect in improving the fluorescence intensity. Ultimately, combining R206K and N167A in the M2 background yielded mutant M4 (R109G/V134A/N167A/R206K), which exhibited superior performance in the signal output and dynamic range. Specifically, M4 demonstrated a 10-fold enhancement in fluorescence output—from ~7700 (WT) to ~77,000—and an expanded regulatory fold from 3.6-fold to 29-fold in the absence versus presence of 2 mM allulose. We further fitted the fluorescence responses of the various mutants to the Hill equation to estimate their respective EC50 values (Fig. 5i). Hill curve fitting revealed that PAB30 (PsiR-M4) had a significantly reduced EC50 of 0.8 mM, representing an ~20-fold improvement in sensitivity compared to the wild-type PAB30 (PsiR) (EC50 = 16.6 mM).

To biochemically validate these findings, we performed electrophoretic mobility shift assays (EMSAs)47 and microscale thermophoresis (MST)48 to analyze the DNA–PsiR and allulose–PsiR binding interactions. After purifying PsiR-WT, M2, and M4 from E. coli BL21(DE3) via His-tag affinity chromatography (Supplementary Fig. 20), both EMSA and MST confirmed specific binding between PsiR and its ligands (Supplementary Fig. 21). Notably, PsiR-M4 exhibited allulose binding at lower concentrations than the WT and M2 (Supplementary Fig. 22), corroborating its enhanced sensitivity. Substrate specificity profiling of the PsiR-WT and PsiR-M4-based sensors against 12 sugars—including seven ketoses (D-allulose, D-fructose, D-sorbose, L-fructose, D-tagatose, D-ribulose, D-xylulose), four aldoses (D-allose, L-rhamnose, L-arabinose, D-xylose), and one sugar alcohol (D-allitol)—demonstrated that the WT sensor responded exclusively to D-allulose at 20 mM substrate concentration (Supplementary Fig. 23). In contrast, the PsiR-M4-based system not only showed enhanced response to D-allulose but also exhibited increased sensitivity to D-sorbose, D-ribulose, D-allose, and D-allitol. Nevertheless, the signals elicited by these non-cognate substrates remained substantially lower than that induced by the native effector, D-allulose.

Design of allulose-responsive gene circuit for enzyme preparation
In this work, we constructed a versatile allulose-responsive genetic circuit platform in C. glutamicum using eGFP as a reporter, exhibiting tunable sensitivity, dynamic range, and signal output. To extend its utility beyond biosensing toward practical applications in biomanufacturing, we developed an allulose-inducible expression system for recombinant enzyme production (Fig. 6a). As a proof of concept, we selected D-mannose isomerase (MI), an enzyme that catalyzes the isomerization of D-fructose to D-mannose49, due to its industrial relevance in mannose biosynthesis. Then, the MI gene from Thermobifida halotolerans (ThMI) was used to replace the eGFP in both PAB30-WT and PAB30-M4 circuits (Supplementary Table 6). The resulting strain, designated strain CgWT-MI and CgM4-MI, were cultivated in CGXII minimal medium supplemented with 10 g/L glucose and induced with 5 mM allulose (Fig. 6b). After 24 h of fermentation, strain CgM4-MI achieved an MI activity of 38 U/mL at the shake flask level, representing a fivefold increase over the wild-type circuit (7.6 U/mL), underscoring the potential of the improved PAB30-M4 circuit for high-efficiency enzyme production.

Recently, autoinduction protein expression systems that do not require costly or toxic external inducers have attracted great attention50. Inspired by this concept, we proposed a self-regulated expression circuit that leverages intracellularly synthesized allulose as an endogenous trigger. As the cell grows, intracellular allulose levels would gradually increase, thereby activating PsiR-mediated gene expression and enabling allulose-mediated auto-inducible protein expression (AMAE) with no need to supplement of inducer allulose (Fig. 6c). To realize this strategy, we introduced a heterologous allulose biosynthetic pathway branching from the glycolytic intermediate fructose-6-phosphate (F6P). The pathway consisted of allulose-6-phosphate 3-epimerase (AlsE), which catalyzes the reversible epimerization of F6P to allulose-6-phosphate (A6P), and allulose-6-phosphate phosphatase (A6PP), which dephosphorylates A6P to yield free allulose. Thus, microbial synthesis of allulose from glucose could be achieved. For pathway construction, the AlsE and one phosphatase YniC from E. coli were co-expressed in C. glutamicum. The resulting strain produced 10 mM (1.8 g/L) allulose from 10 g/L glucose within 12 h (Fig. 6d). Notably, the intracellular allulose concentration reached ~5 mM by 6 h of cultivation, aligning with the optimal activation range of PsiR-M4. Based on this observation, we introduced this allulose synthetic pathway into strain CgM4-MI, resulting in AutoCgM4-MI. This strain successfully achieved an MI activity of 25 U/mL after 18 h of cultivation in CGXII medium without the addition of any external inducer (Fig. 6d). The results demonstrate the feasibility of the demonstrated ALAE system for dynamic, self-regulated production of proteins.

Application of PsiR biosensor for dynamic metabolic regulation
Encouraged by the established allulose-inducible expression systems, we next sought to expand the functionality of the PsiR-based biosensor toward dual-directional gene regulation. Specifically, we designed an allulose-triggered CRISPR interference (CRISPRi) circuit (ATCi) to enable programmable gene repression in response to intracellular allulose accumulation (Fig. 7a). In this system, the PsiR-M2 variant instead of PsiR-M4 was employed to tightly control the expression of catalytically inactive dCpf1 in designed ATCi circuit, ensuring minimal leaky expression in the absence of allulose and robust induction upon its presence. To implement this architecture, we initially replaced the eGFP with dCpf1 in PAB30-M2, and then chromosomally integrated the expression cassettes containing PtacMD-dCpf1 and PM10-PsiR-M2 into the C. glutamicum 13032 genome, yielding the engineered strain CgdC-M2 (Supplementary Table 7). In this strain, dCpf1 expression is activated by allulose to regulate target gene expression with the aid of the designed gRNAs. To validate the functionality of ATCi, we constructed a dual-reporter system in CgdC-M2 by integrating constitutively expressed eGFP and mRFP1 under the strong PH9 promoter, resulting in strain CgdC-M2-GR. Plasmids carrying gRNAs targeting various regions of eGFP and mRFP1 were introduced separately into strain CgdC-M2-GR. Upon addition of 5 mM allulose, fluorescence signals from both reporters decreased by over 80% (Fig. 7b), confirming efficient and specific gene repression. Furthermore, simultaneous repression of both targets was achieved by co-expression of dual gRNAs. These results demonstrate the feasibility of ATCi for tunable gene silencing in microbial systems.

To explore its utility in metabolic engineering, we applied ATCi to the development of an allulose-producing cell factory (Fig. 7c). We have constructed an allulose synthetic pathway in C. glutamicum; however, the resulting strain CgAll1 achieved low titer (8.7 g/L) and yield (0.17 g/g glucose) (Fig. 7d–g), primarily due to competition for carbon flux between cell growth and allulose synthesis. Therefore, we adopted a systematic metabolic engineering strategy to enhance F6P accumulation and enable dynamic regulation of central metabolism. To achieve this end, we constructed a strain CgPIG via the following steps (Fig. 7c): (i) knockout of pfkA, encoding 6-phosphofructokinase responsible for converting F6P to fructose-1,6-bisphosphate, thus reducing F6P consumption by the cell; (ii) mutation of the IolR-binding site in the promoter of glucose/myo-inositol permease IolT1 to enhance glucose uptake51; and (iii) simultaneous knockout of the ldhA gene and chromosomal integration of glucokinase (GlcK) and glucose-6-phosphate isomerase (Pgi) from Bacillus subtilis at the ldhA locus to boost the supply of glucose-6-phosphate (G6P) and F6P. After introducing the synthetic plasmid (pEC-AlsE-YniC) into strain CgPIG, the resulting strain CgAll2 produced 16.7 g/L allulose, a 1.9-fold improvement over CgAll1 (Fig. 7f).

Previous transcriptome analysis showed that inactivation of pfkA would shift carbon flux toward the pentose phosphate pathway (PPP), leading to the upregulation of multiple enzymes in this pathway, including zwf (introducing metabolic flow into the PPP)52. Since the PPP plays a critical role in NADPH generation and redox balance, the knockout of zwf is not feasible. To redirect the carbon flux to allulose synthesis while minimizing the impact on initial cell growth, we employed our designed ATCi system to dynamically regulate zwf expression in CgAll2. To this end, the expression cassette of PtacMD-dCpf1 and PM10-PsiR-M2 and zwf-targeting crRNA were integrated into the genome of CgPIG, resulting in strain CgPIG-dC-M2. Subsequent transformation with pEC-AlsE-YniC generated the production strain, CgAll3. During fermentation, the gradually accumulated endogenous allulose along with cell growth would activate dCpf1 expression, which in turn repressed zwf expression, thereby shifting carbon flux toward allulose synthesis. According to the mRNA concentration data (Supplementary Fig. 24), the gene zwf expression level was repressed in CgAll3 relative to that in CgAll2. As a result, CgAll3 produced 21.4 g/L allulose after 30 h of fermentation, which was 68% higher than that of the CgAll2 strain (Fig. 7d–g). Also, a conversion yield of 0.43 g/g glucose was achieved for strain CgAll3. The post-30-h decrease in allulose yield (Fig. 7c) is likely due to its reverse isomerization to fructose by the E. coli AlsE53, with the fructose being consumed for growth (Fig. 7a). Addressing this requires AlsE variants with high F6P-to-A6P conversion activity but low reverse activity toward allulose.

Methods
Materials and strains
The chemicals including allulose, glucose, and isopropyl-β-D-1-thiogalactopyranoside (IPTG), were purchased from Sigma-Aldrich (St. Louis, MO, USA). Kanamycin and chloramphenicol for strain selection and growth were obtained from Solarbio (Beijing, China). Strains and plasmids used in this study are listed in Supplementary Tables 6, 7. The primers and gene sequences were shown in Supplementary Data 1. The E. coli DH5α (Invitrogen Life Technologies, U.S.A.) strain was used as a cloning host for plasmid construction. The recombinant E. coli strains were cultivated in Luria-Bertani (LB) broth at 37°C with constant shaking at 200 rpm. The C. glutamicum ATCC 13032 strain served as the host for constructing biosensor library, enzyme expression, and metabolic engineering for allulose synthesis. The C. glutamicum ATCC 13032 and its derivatives were cultivated in brain heart infusion (BHI) medium or CGXII minimal medium66. Where appropriate, a final concentration of 50 μg/mL kanamycin and 10 μg/mL chloramphenicol was added to culture medium.

Construction of biosensor in C. glutamicum
The plasmid pEC-XK99E served as the template to construct the allulose-responsive biosensor in C. glutamicum. Firstly, we replaced the LacI operon in pEC-XK99E with H36-PsiR operon, in which the expression of PsiR was controlled by the constitutive promoter of PH36, resulting in a recombinant plasmid pEC-H36-PsiR. Two widely used Ptuf and PtacM promoters were further introduced into pEC-H36-PsiR, and the PsiR binding site PsiO sequence (ATTGCACAATCGATGGTGCAA) was inserted into different positions, namely the upstream of the −35 box, between the −35 and −10 box, and between the −10 box and RBS, of Ptuf and PtacM. The eGFP gene was then constructed at the downstream of the hybrid promoters. The obtained plasmids were transferred into C. glutamicum 13032, which resulted in six biosensors, named as PAB1-6. Secondly, we optimized the promoters of PsiR in pEC-PAB6 by replacing PH36 with low-strength promoters of PL26 and PL80, medium-strength promoters of Psod, PM5, PM6, and PM10, high-strength promoters of P11F, Ptuf, PH9, and PH5, resulting in the PAB7-PAB16. Thirdly, we optimized the RBS sequence between eGFP and the PsiO operator by using eleven other RBS sequences (Supplementary Data 2), resulting in the PAB17-27. Ultimately, the spacer sequence (TACTT) of PM10 of PsiR in pEC-PAB27 was replaced with five random nucleotides (NNNNN). A variant library was constructed and transferred into C. glutamicum 13032. After high-throughput screening of 1400 strains by measuring the fluorescence change under the induction concentration of 50 mM allulose, 78 strains with enhanced fluorescence output relative to PAB27 were obtained.

To measure the green fluorescent of recombinant C. glutamicum strains, the cells were first cultivated in 1 mL BHI medium with 25 μg/mL kanamycin in a 24-well plate at 700 rpm and 30°C overnight. Then, the cells as the seed cultures were transferred into 1 mL of CGXII medium with 10 g/L glucose and 0–200 mM allulose in a 48-well plate to an initial OD600 of 0.5. After further cultivation for 12 h, the cells were diluted 10 times in sterile PBS buffer for the fluorescence analysis. The fluorescence intensities of eGFP (excitation, 488 nm; emission 528 nm) and mRFP1 (excitation, 575 nm; emission 610 nm) were determined using a microplate reader (Biotek, Synergy Neo2)67.

Construction and characterization of PsiR mutants
Site-directed mutations were conducted using PCR methods. The primers used for mutations were listed in Supplementary Data 3. Recombinant plasmid pEC-PAB30 served as the template. The PCR products were digested with DpnI at 37°C for 2 h and followed by transformation into E. coli DH5α cells for plasmid construction. And then, the desired mutations were confirmed by PCR sequencing. To conduct the combinatorial mutation of PsiR, the pEC-PAB30-M1, pEC-PAB30-M2, and pEC-PAB30-M3 served as the template to integrate other beneficial mutations. The obtained plasmids containing genes of PsiR mutants were then transformed into C. glutamicum by electroporation. To measure the influence of PsiR mutations on green fluorescence, 0–20 mM allulose was added into the medium to induce eGFP expression. We also measured the green fluorescence values of our designed system PAB30 and PAB30-M4 to 12 sugars, such as seven ketoses like D-allulose, D-fructose, D-sorbose, L-fructose, D-tagatose, D-ribulose, D-xylulose, four aldoses of D-allose, L-rhamnose, L-arabinose, D-xylose, and one sugar alcohol (D-allitol), at the substrate concentration of 20 mM in CGXII buffer.

Strain construction
To construct strain CgM4-MI, we replaced the eGFP gene with ThMI in pEC-PAB31-M4, and introduced the generated plasmid into C. glutamicum 13032. To obtain the strain AutoCgM4-MI, we initially constructed the genes of alsE and yniC from E. coli under the control of strong promoters PH36 and Ptuf into pXMJ19, resulting in the recombinant plasmid pXM-AlsE-YniC. Then, the pXM-AlsE-YniC and pEC-PAB31-M4-ThMI were introduced together into strain C. glutamicum 13032 to generate AutoCgM4-MI.

To construct strain CgdC-M2, the eGFP gene in pEC-PAB31-M2 was replaced with dCpf1 gene68. Then, the whole expression cassette containing PtacMD-dCpf1 and PM10-PsiR-M2 was cloned and inserted into the plasmid of pK18-Cgl2297, which comprised the upstream and downstream regions (~1 kb) of gene Cgl2297 in C. glutamicum chromosome. The resulting plasmid pK18-Cgl2297-dCpf1-PsiR-M2 was further introduced into C. glutamicum 13032 and integrated the PsiR-dCpf1 expression cassette into the Cgl2297 site with the aid of homologous recombination method69, resulting in the strain CgdC-M2. Furthermore, we constructed the gene-integrating vector plasmids pK18-Cgl0331-eGFP and pK18-Cgl2911-mRFP1, and integrated the eGFP and mRFP1 genes into the Cgl0331 and Cgl2911 sites, respectively, resulting in strain CgdC-M2-GR.

To repress the gene expression of eGFP and mRFP1 in strain CgdC-M2-GR, we first constructed a ccdB-containing template plasmid by inserting the ccdB gene under the control of the P11F promoter into the pXMJ1966. Based on this template, a series of crRNA expression plasmids, pXMJ19-crRNA-eGFP218, pXMJ19-crRNA-eGFP663, pXMJ19-crRNA-mRFP169, pXMJ19-crRNA-mRFP359, and pXMJ19-crRNA-eGFP218-mRFP169, were generated using the Golden Gate assembly method70 with crRNAs targeting eGFP and mRFP1. These plasmids were transformed into strain CgdC-M2-GR, respectively, resulting in strains of CgdC-M2-GR (pXMJ19-crRNA-eGFP218), CgdC-M2-GR (pXMJ19-crRNA-eGFP663), CgdC-M2-GR (pXMJ19-crRNA-mRFP169), CgdC-M2-GR (pXMJ19-crRNA-mRFP359), CgdC-M2-GR (pXMJ19-crRNA-eGFP218-mRFP169).

To develop the allulose synthetic cells, we initially constructed the plasmid pEC-AlsE-YniC by introducing the alsE and yniC genes into pEC-XK99E, and transformed pEC-AlsE-YniC into strain C. glutamicum 13032, resulting in strain CgAll1. To obtain the strain CgAll2, we conducted several genetic modifications in C. glutamicum 13032 as follows: i) we constructed the plasmid of pK18-pfkA for gene knockout of pfkA in C. glutamicum; ii) we constructed the plasmid of pK18-O6-iolT1 for replacement of the native operator sequence (GATTGACGGGACATAT) of gene iolT1 with the mutant sequence (GATTGACGGGGGATAT); iii) we constructed the plasmid of pK18-Cgl2911-BsGlcK-BsPgi for integrating the genes of glcK and pgi from Bacillus subtilis into the Cgl2911 site, respectively; iv) we sequentially transformed those plasmids into C. glutamicum 13032, and obtained the strain CgPIG; v) we introduced the plasmid of pEC-AlsE-YniC into strain CgPIG and obtained strain CgAll2. To construct the strain CgAll3, we transformed the pK18-Cgl2297-dCpf1-PsiR-M2 into strain CgPIG and integrated the PsiR-dCpf1 expression cassette into the Cgl2297 site in CgPIG, resulting in strain CgPIG-dC-M2. We also constructed the plasmid of pK18-Cgl2303-crRNA-zwf for integrating the crRNA target to zwf into the Cgl2303 site in CgPIG-dC-M2, resulting in strain CgPIG-dC-M2-zwf. And then, the plasmid pEC-AlsE-YniC was together introduced into strain CgPIG-dC-M2-zwf to obtain CgAll3.

Cell cultivation for enzyme expression
The strain CgM4-MI was cultured in CGXII buffer at 30°C with an initial glucose concentration of 10 g/L. When the cell density OD600 reached 0.6–1.0, 5 mM allulose was added into the medium to induce MI expression. After 24 h of fermentation, the cells were collected and resuspended with Tris-HCl buffer (50 mM, pH 8.0). Then, the cells were lysed by ultrasonic treatment at 4°C, and centrifuged at 17,000 × g for 45 min. The crude enzyme activity of MI was measured in the reaction medium (1 mL), which contained 500 mM fructose, 5 mM Mg2+, Tris-HCl buffer (50 mM, pH 8.0), and 5–150 μL of crude enzyme. The reactions were conducted at 65°C for 10 min. The samples were then heated at boiling temperature for 10 min and then centrifuged at 17,000 × g for 30 min for HPLC analysis. One unit of enzyme activity was defined as the enzyme amount (mL) catalyzing the formation of 1 μmol mannose per min. For the MI expression in strain AutoCgM4-MI, the strain CgM4-MI was cultured in CGXII buffer with 10 g/L glucose at 30°C for 24 h. And then, the cells were collected for crude enzyme activity analysis.

Cell cultivation for gene regulation and allulose synthesis
To perform the gene repression of eGFP and mRFP1, the cells were initially cultivated in 1 mL BHI medium, and then transferred into CGXII medium containing 5 mM allulose with 0.5% inoculum in 48-well plates. The green and red fluorescence of recombinant strains were measured at different culture times. The CGXII medium without allulose was also used as the control. To perform the allulose synthesis from glucose, the cells of CgAll1, CgAll2, and CgAll3 were cultured in BHI medium, and then transferred into a 250 mL flask containing 20 mL fermentation medium. This medium comprised of 40 g/L corn steep liquor, 20 g/L (NH4)2SO4, 50 g/L glucose, 2 g/L urea, 2 g/L KH2PO4, 2 g/L K2HPO4, 10 mg/L CaCl2.2H2O, 10 mg/L FeSO4.7H2O, 10 mg/L MnSO4.7H2O, 0.1 g/L MgSO4.7H2O. The cultivation was performed at 250 rpm, 30°C for 24–72 h. When necessary, 10 mg/L chloramphenicol and 25 mg/L kanamycin were supplemented into the culture medium.

Statistics and reproducibility
For all quantitative experiments, data are presented as mean ± SD unless otherwise stated. For bar and line plots, n refers to the number of independent biological experiments, which were typically n = 3 unless otherwise specified, and are indicated in the figure legends. The statistical tests used (two-sided t test with Welch’s correction, and one-way ANOVA followed by Dunnett’s post hoc test) are specified in the corresponding figure legends together with exact P values.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The transcription factors have shown extensive applications in synthetic biology and metabolic engineering. Indeed, due to the complex multi-domain structure of effector-TF-DNA and unclear activation-allostery mechanism, computational design and engineering of TFs remains a challenge. This manuscript describes the successful computational design of one PsiR and obtained several beneficial PsiR mutants only through engineering less than 100 mutants. The best mutant increased the sensitivity by 20-fold. They also presented a PsiR-based sensors box possessing different response property. The feasibility of this computational design strategy in engineering LacI was also confirmed. Based on the developed sensor, the author also constructed an inducer-free allulose-mediated auto-inducible protein expression. This design is cute for protein expression without addition of inducer. They also constructed an allulose-triggered CRISPR interference circuit (ATCi) for dynamic metabolic regulation. It facilitated a 68% increase in allulose titer. Overall, this is a very informative and meaningful work. The computational design strategy and demonstrated allulose-responsive biosensors would have broader application in TF engineering and synthetic biology. This reviewer recommends acceptance following minor revision:\n(1) The author obtained one PsiR-M4 mutant possessing high sensitivity. They constructed the protein expression system based on PsiR-M4. The inducer concentration was 5 mM. As we know, L-arabinose also has been used as inducer to replace IPTG. The advantages of the developed allulose response system relative to L-arabinose system should be discussed.\n(2) The author demonstrated the beneficial effect of saturation mutagenesis of T135 in PsiR-M2, such as M2-T135H; however, they combined the beneficial single-site mutants of residues N81T, N167A, and R206K into M2 instead of M2-T135H. The reason should be discussed.\n(3) The author developed the allulose-triggered CRISPR interference circuit to regulate gene expression in genome. However, they used PsiR-M2 to construct this circuit, why not using PsiR-M4. The reason for chose PsiR-M2?\n(4) The author confirmed the gene depression using ATCi; however, they only detected the cell growth and allulose production. The mRNA concentration decrease should also be given.\n(5) In Figure 7f, after fermentation for 30 h, the allulose production decreased, the probable reason?\nOther minor concerns:\nLine 65: “D-allulose” to “D-allulose”.\nLine 151: Figure 3f showed the interactions between allulose with surrounding residues (P77, N81, F83, F84, M87, D155, L156, R208, Y233, S260, S261, I262, D288). However, in figure 3g, the data for S261A and I262A were not shown.\nLine 224: “likely due to excessive tunnel expansion.” could be deleted. No data to support this conclusion.", "reviewer_2": "The development of allulose-responsive biosensor toolbox for auto-inducible protein expression and CRISPRi mediated dynamic metabolic regulation in this study is a great piece of work to expand the TF-based biosensors for protein expression and metabolic engineering. This work is well-designed and the tools developed were successfully applied. The manuscript is relatively well written and easy to follow. I have a few comments though for the authors to consider during the revision.\nThe writing should be checked through the whole manuscript to avoid long paragraphs and grammar errors. The tools developed in this work are functional for C. glutamicum. Is it functional in other hosts such as E. coli used for molecular cloning? Are there any cross-talks for other sugar inducers with similar structure as allulose or homologous proteins of PsiR? What will the results be in media containing mixed sugars? The specificity of the biosensor besides its sensitivity can be discussed more.", "reviewer_3": "Review for Dong et al.,\n“Rational design of allulose-responsive biosensor toolbox for auto-inducible protein expression and CRISPRi mediated dynamic metabolic regulation”\nDong et al. describe how they used a “tunnel” strategy to rationally redesign PsiR to change its sensitivity and response level to allulose in a cell-based assay. They used engineered PsiR as a control element in a genetic circuit to create a useful allulose-triggered auto-inducible protein expression system. This required a decent amount of strain engineering.\nIt’s an interesting paper with several issues.\n1. It is very annoying that the author-provided PsiR crystal structure is numbered incorrectly. The structure is incomplete, but the residues should still be indexed correctly (according to the wild-type numbering). Because of the lack of DBD in the PsI structure and their decision not to follow the WT numbering, the numbering in the PDB file is off. This makes it ridiculously difficult to follow, understand or check the claims in their paper. The authors should fix this.\n2. To make matters worse, it looks like some of the residues are labeled wrong, because the difference in numbering in the paper vs. structure is inconsistent. For example, R109 in Fig. 4: I think the residue they are trying to point out is actually R45 in their structure (64 res difference). But in the same figure, D202 and D288 correspond to D138 and D224 in the structure (86 res difference). And, again in the same figure, it seems that V134 and T135 are actually V70 and V71 (64 residue difference)! This is sloppy.\n3. The authors should make it clear in the main text of their paper when they introduce the PsiR structure that it does not include the DNA-binding domain. It is currently written to sound as if it does include this domain.\n4. The authors should make it clear in the main text of the paper how they are performing their simulations with a brief 1-2 sentence description, particularly for the steered MD simulations, and with which regions of the proteins (e.g., not including the DBD), and how these input models were produced. It is not standard practice to perform MD simulations with AlphaFold structures and especially AF structures with ligands bound. The authors should make it easier for the reader to follow what they did, to be able to decide how much to trust those data.\n5. When the authors use collective variables to cluster frames from MD simulations, they should briefly explain the methods and demonstrate the robustness of the result by showing that it holds up when they try other, similar, redundant CVs.\n6. The LacI engineering is weak. It has been known for decades that LacI R101 is amenable to mutation (originally by work in the Miller group: Suckow et al., Markiewicz et al., JMB, 1990s); it is not shocking that a couple of mutations at this position would increase the fluorescence by 2-3X, and this could even just be due to increased cell expression. Further, the small increase in fluorescence without a predictable shift in the IPTG sensitivity at 10 µM IPTG is not a good demonstration of generalizing the much more thorough results from PsiR engineering. If the authors want to claim that their engineering strategy is generalizable, they should engineer a panel of LacI mutants (or even just 1-2 mutants) that have a variety of IPTG sensitivities, as in Fig. 5i for PsiR, and compare an expected response concentration to the measured one using a titration. Maybe the R101 mutants do this, but the authors do not show it. This is more difficult than the result for LacI mutants in Fig. S12, but if the engineering strategy is truly generalizable then the authors should be able to do it.\n7. Can the authors provide more context for the “rigid element” in LacI (line 290)? Do they mean more rigid in the IPTG-bound state as compared to the DNA-bound state? There is also a more recent, higher-resolution LacI study: see Fig. 6 in Lu et al.: https://www.biorxiv.org/content/10.1101/2024.08.04.606547v1.full. The authors can cross-check this new data to see if whichever residue corresponds to PsiR R206 is also more flexible than nearby regions as they see in their RMSF analysis.\n8. Most of these data are from cell-based fluorescence measurements, but it would build confidence in the engineering strategy and results if the authors could show the results of an in vitro binding assay for WT PsiR and an engineered mutant, like M2, M3 or M4, for both the allulose and PsiO ligands. The dynamic range of the WT and the mutants is hopefully comparable in such an experiment."}, "author_response": "Authors’ point-by-point response\nManuscript ID: NCOMMS-25-30053\nTitle: “Computational design of allulose-responsive biosensor toolbox for auto-inducible protein\nexpression and CRISPRi mediated dynamic metabolic regulation”\n[Response]: We are grateful for the editor’s supports as well as the reviewers’ positive evaluations\nand their valuable suggestions. Here, we prepared this point-to-point response and highlight changes\nin red in the manuscript.\nReviewer #1 (Remarks to the Author):\nThe transcription factors have shown extensive applications in synthetic biology and metabolic\nengineering. Indeed, due to the complex multi-domain structure of effector-TF-DNA and unclear\nactivation-allostery mechanism, computational design and engineering of TFs remains a challenge.\nThis manuscript describes the successful computational design of one PsiR and obtained several\nbeneficial PsiR mutants only through engineering less than 100 mutants. The best mutant increased\nthe sensitivity by 20-fold. They also presented a PsiR-based sensors box possessing different\nresponse property. The feasibility of this computational design strategy in engineering LacI was also\nconfirmed. Based on the developed sensor, the author also constructed an inducer-free allulose-\nmediated auto-inducible protein expression. This design is cute for protein expression without\naddition of inducer. They also constructed an allulose-triggered CRISPR interference circuit (ATCi)\nfor dynamic metabolic regulation. It facilitated a 68% increase in allulose titer. Overall, this is a\nvery informative and meaningful work. The computational design strategy and demonstrated\nallulose-responsive biosensors would have broader application in TF engineering and synthetic\nbiology. This reviewer recommends acceptance following minor revision:\nResponse: It is our honor to have your kind commendation. We greatly appreciate your constructive\ncomments and suggestions, which are very helpful for us to revise the manuscript. We seriously\nconsidered the issues and concerns which you mentioned, and carefully addressed them as below.\n(1) The author obtained one PsiR-M4 mutant possessing high sensitivity. They constructed the\nprotein expression system based on PsiR-M4. The inducer concentration was 5 mM. As we know,\nL-arabinose also has been used as inducer to replace IPTG. The advantages of the developed allulose\nresponse system relative to L-arabinose system should be discussed.\n[Response]: We really appreciated the reviewer’s suggestions.\nIndeed, as mentioned by reviewer, the AraC-arabinose pair have been employed to constructed\nprotein expression system (Agata, et al. Modular plasmid design for autonomous multi-protein\nexpression in Escherichia coli. Journal of Biological Engineering, 2025, 19:14; Zhang et al.\nDevelopment and application of an arabinose-inducible expression system by facilitating inducer\nuptake in Corynebacterium glutamicum. Applied and Environmental Microbiology, 2012, 16, 5831-\n5838). The used arabinose concentration (1.5-10 g/L) as inducer was comparable with our designed\nPsiR-allulose system (0.5-0.9 g/L). However, arabinose as the inducer would be metabolized by cell,\nand also exhibited the glucose-induced carbon catabolite repression effect. Compared to arabinose,\nallulose is non-metabolizable and free from carbon catabolite repression effect, enabling robust and\nsustained induction in the long-term fermentation phase.\nWe also gave more discussions in the manuscript:\nLine 485-492: “In this work, we established a tightly regulated allulose-inducible protein expression\nsystem using the engineered PsiR, functioning at low inducer concentrations (0.5-0.9 g/L), which\nwas comparable with the reported L-arabinose and rhamnose-inducible expression system (0.8-1.5\ng/L) but lower than that of maltose (5-20 g/L) and xylose (10-20 g/L) activated system26-28,57. This\nsystem was successfully applied to MI production, confirming its practical utility. Unlike\nconventional inducers, allulose is non-metabolizable, food-safe, low-cost, and free from carbon\ncatabolite repression effect30, enabling robust and sustained induction in the long-term fermentation\nphase.”\n(2) The author demonstrated the beneficial effect of saturation mutagenesis of T135 in PsiR-M2,\nsuch as M2-T135H; however, they combined the beneficial single-site mutants of residues N81T,\nN167A, and R206K into M2 instead of M2-T135H. The reason should be discussed.\n[Response]: We really appreciated the reviewer’s comments.\nIn the mutation experiment at T135 site, bulkier substitutions (Met, Tyr, His, and Arg) in variants\nM2-T135M, M2-T135Y, M2-T135H, and M2-T135R maintained lower background noise relative\nto that of M2. Especially, mutant M2-T135H reached a regulatory fold change of 52 at 0/5 mM\nallulose—surpassing that of M2. However, the signal output of this mutant decreased by 31%\ncompared with that of M2. Therefore, the mutant M2 served as template for combination of\nbeneficial single-site mutants of residues N81T, N167A, and R206K.\nWe also revised the manuscript to give more clear information.\nLine 238-243: “In contrast, bulkier substitutions (Met, Tyr, His, and Arg) at position 135 in variants\nM2-T135M, M2-T135Y, M2-T135H, and M2-T135R maintained lower background noise\ncompared to M2. Among them, M2-T135H achieved a dynamic range (fold-change at 0 vs. 5 mM\nallulose) of 52, surpassing that of M2. However, this enhancement in regulation was accompanied\nby a 31% reduction in signal output relative to M2 (Supplementary Fig. 11).”\n(3) The author developed the allulose-triggered CRISPR interference circuit to regulate gene\nexpression in genome. However, they used PsiR-M2 to construct this circuit, why not using PsiR-\nM4. The reason for chose PsiR-M2?\n[Response]: We really appreciated the reviewer’s comments.\nIn this work, we obtained several PsiR mutants exhibited varied sensitivity. Especially, the mutant\nPsiR-M4 displayed higher signal output and sensitivity compared with PsiR-M2. To expand the\nfunctionality of the PsiR-based biosensor box, we designed an allulose-triggered CRISPR\ninterference (CRISPRi) circuit (ATCi) to enable programmable gene repression in response to\nintracellular allulose accumulation. To achieve tightly controlling the expression of catalytically\ninactive dCpf1 or dCas9, the PsiR mutant possessing minimal leaky expression in the absence of\nallulose and robust induction upon its presence was required. By comparing with PsiR-M4, the\nmutant PsiR-M2 exhibited comparable signal output and lower leaky expression. Therefore, this\nmutant was selected to construct the designed ATCi system.\nWe also revised the manuscript as followed:\nLine 409-412: “In this system, the PsiR-M2 variant instead of PsiR-M4 was employed to tightly\ncontrol the expression of catalytically inactive dCpf1 in designed ATCi circuit, ensuring minimal\nleaky expression in the absence of allulose and robust induction upon its presence.”\n(4) The author confirmed the gene depression using ATCi; however, they only detected the cell\ngrowth and allulose production. The mRNA concentration decrease should also be given.\n[Response]: We really appreciated the reviewer’s suggestions.\nAccording to the reviewer’s suggestions, we further detected the mRNA concentration change after\nperforming ATCi in CgAll3. The corresponding results were shown in Supplementary figure 23.\nThe resulted showed that the zwf expression was indeed repressed.\nWe also revised the manuscript as followed:\nLine 449-451: “According to the mRNA concentration data (Supplementary Fig. 23), the gene zwf\nexpression level was repressed in CgAll3 relative to that in CgAll2.”\nLine 196-202 in Supplementary information: Real-Time Quantitative PCR (RT-qPCR)\n“The recombinant C. glutamicum strains were grown to the exponential phase, and then harvested\nby centrifugation at 14,000 g and 4 °C. Total RNA was extracted by using RNA Extraction Kits\n(QIAGEN, Germany). Firstly, 500 ng of total RNA was transcribed into cDNA using Quant Reverse\nTranscriptase with random primers (Toyota). Samples were then analyzed using a Light CyclerH\nused as reference for normalization.”\n(5) In Figure 7f, after fermentation for 30 h, the allulose production decreased, the probable reason?\n[Response]: We really appreciated the reviewer’s comments.\nIndeed, it was noted that the allulose production increased along with the cell growth but decreased\nafter fermentation for 30h (Fig. 7c). We also discovered that the glucose in the medium completely\nconsumed at 30h (Fig.7b). Therefore, the decreased allulose concentration was probably due to\nisomerization of allulose to fructose by the used AlsE from E. coli (Yang J, et al. De novo artificial\nsynthesis of hexoses from carbon dioxide. Science Bulletin, 2023.68(20), 2370-2381). The\ngenerated fructose could be metabolized by cell for cell growth (Fig.7a). Therefore, candidate AlsEs\nwith high isomerization ability of fructose-6-phosphate to allulose-6-phosphate but low activity in\nconversion allulose to fructose was required to solve this problem.\nWe also gave more clear information in the manuscript as followed:\nLine 453-456: “The post-30-hour decrease in allulose yield (Fig. 7c) is likely due to its reverse\nisomerization to fructose by the E. coli AlsE52, with the fructose being consumed for growth (Fig.\n7a). Addressing this requires AlsE variants with high F6P-to-A6P conversion activity but low\nreverse activity toward allulose.”\n(6) Line 65: “D-allulose” to “D-allulose”.\nLine 224: “likely due to excessive tunnel expansion.” could be deleted. No data to support this\nconclusion.\n[Response]: We really appreciated the reviewer’s comments.\nWe have revised the manuscript as followed:\nLine 65: “D-allulose”\nLine 238: the phrase of “likely due to excessive tunnel expansion.” was deleted.\n(7) Line 151: Figure 3f showed the interactions between allulose with surrounding residues (P77,\nN81, F83, F84, M87, D155, L156, R208, Y233, S260, S261, I262, D288). However, in figure 3g,\nthe data for S261A and I262A were not shown.\n[Response]: We really appreciated the reviewer’s comments.\nAccording to the reviewer’s suggestions, we have added the alanine scanning data for those residues\nin Fig.3g.\nReviewer #2 (Remarks to the Author)：\nThe development of allulose-responsive biosensor toolbox for auto-inducible protein expression and\nCRISPRi mediated dynamic metabolic regulation in this study is a great piece of work to expand\nthe TF-based biosensors for protein expression and metabolic engineering. This work is well-\ndesigned and the tools developed were successfully applied. The manuscript is relatively well\nwritten and easy to follow. I have a few comments though for the authors to consider during the\nrevision.\nResponse: It is our honor to have your kind commendation. We greatly appreciate your constructive\ncomments and suggestions, which are very helpful for us to revise the manuscript. We seriously\nconsidered the issues and concerns which you mentioned, and carefully addressed them as below.\nThe writing should be checked through the whole manuscript to avoid long paragraphs and grammar\nerrors. The tools developed in this work are functional for C. glutamicum. Is it functional in other\nhosts such as E. coli used for molecular cloning? Are there any cross-talks for other sugar inducers\nwith similar structure as allulose or homologous proteins of PsiR? What will the results be in media\ncontaining mixed sugars? The specificity of the biosensor besides its sensitivity can be discussed\nmore.\n[Response]: We really appreciated the reviewer’s comments.\n(1) According to the reviewer’s suggestions, we have thoroughly corrected the paragraphs and\ngrammar errors for the whole manuscript.\n(2) In this work, allulose-responsive biosensor toolbox was constructed and demonstrated with C.\nglutamicum as model host. To extend the application of designed PsiR mutants, we have also\nconstructed the allulose-responsive biosensor in E. coli and confirmed the response sensitivity to\nallulose. The corresponding data was shown in Supplementary Fig. 24. In fact, the engineered PsiR\nmutants possessing varied sensitivity and dynamical range could easily constructed in other hosts,\nsuch as E. coli, Bacillus subtilis, even yeast. However, the PsiR and eGFP expression should be\nfurther optimized to fit the host requirement.\nWe also revised the manuscript as followed:\nLine 504-509: “To extend the application of designed PsiR mutants, we have also constructed the\nallulose-responsive biosensor in E. coli and confirmed the response sensitivity to allulose\n(Supplementary Fig. 24). In future, the demonstrated allulose-mediated expression system could be\neasily constructed in other model strains, such as E. coli, Bacillus subtilis, and Yeast, for broad\napplication in enzyme and protein production in the biomanufacturing area. Moreover, the PsiR\nexpression should be further optimized to fit the host requirement.”\nSupplementary Figure 24 Construction the PsiR-based biosensor in E. coli. a) The schematic\ndiagram of the designed PsiR-based biosensor in E. coli. The promoter for PsiR expression was\nP and the promoter for eGFP expression was P derived from Agrobacterium tumefaciens\nJ23100 psiA\nPsiR regulon. For sensor systems were constructed based on PsiR-WT, PsiR-M1, PsiR-M2 and\nPsiR-M4. b) The fluorescence value of designed sensor systems to allulose at 0 and 5 mM allulose.\nThe sensor system based on PsiR-M4 showed the highest signal output among constructed system.\n(3) In this work, to measure the green fluorescent of allulose-responsive biosensor based on C.\nglutamicum strains, the cells were first cultivated in 1 mL BHI medium at 700 rpm and 30 ℃\novernight. Then, the cells as the seed cultures were transferred into 1 mL of CGXII medium with\n10 g/L glucose and 0-200 mM allulose in a 48-well plate to an initial OD of 0.5. After further\n600\ncultivation for 12 h, the cells were diluted 10 times in sterile PBS buffer for the fluorescence analysis.\nTherefore, glucose and allulose would be the only sugars in the media.\nAccording to the reviewer’s suggestions, we have also measured the response sensitivity of our\ndesigned system based on PsiR-WT and PsiR-M4 to 12 sugars, such as 7 ketoses like D-allulose, D-\nfructose, D-sorbose, L-fructose, D-tagatose, D-ribulose, D-xylulose, 4 aldoses of D-allose, L-\nrhamnose, L-arabinose, D-xylose, and 1 D-allitol, which possessing similar structure with allulose.\nWe noted that the sensor system based on PsiR-WT only showed the signal output to allulose under\nthe 20 mM substrates. The sensor system based on PsiR-M4 not only increased the signal output to\nallulose but also increased the sensitivity to D-sorbose, D-ribulose, D-allose, and D-allitol. However,\nthe signal output to those substrates were still significantly lower than its native effector D-allulose.\nWe also revised the manuscript as followed:\nLine 364-371: “Substrate specificity profiling of the PsiR-WT and PsiR-M4-based sensors against\n12 sugars—including seven ketoses (D-allulose, D-fructose, D-sorbose, L-fructose, D-tagatose, D-\nribulose, D-xylulose), four aldoses (D-allose, L-rhamnose, L-arabinose, D-xylose), and one sugar\nalcohol (D-allitol)—demonstrated that the WT sensor responded exclusively to D-allulose at 20 mM\nsubstrate concentration (Supplementary Fig. 22). In contrast, the PsiR-M4-based system not only\nshowed enhanced response to D-allulose but also exhibited increased sensitivity to D-sorbose, D- D-\nribulose, D-allose, and D-allitol. Nevertheless, the signals elicited by these non-cognate substrates\nremained substantially lower than that induced by the native effector, D-allulose.”\nSupplementary Figure 22 Substrate specificity of developed sensor system. a) Chemical structures\nof D-allulose and 11 structure-like monosaccharides. b) The fluorescence values of developed sensor\nsystem based on PsiR-WT and PsiR-M4 to 12 sugars with the substrate concentration of 20 mM.\nThe control experiment was conducted only with glucose in the CGXII medium.\nLine 582-586: “We also measured the green fluorescence values of our designed system PAB30\nand PAB30-M4 to 12 sugars, such as seven ketoses like D-allulose, D-fructose, D-sorbose, L-fructose,\nD-tagatose, D-ribulose, D-xylulose, four aldoses of D-allose, L-rhamnose, L-arabinose, D-xylose, and\none sugar alcohol (D-allitol), at the substrate concentration of 20 mM in CGXII buffer.”\nReviewer #3 (Remarks to the Author):\nDong et al. describe how they used a “tunnel” strategy to rationally redesign PsiR to change its\nsensitivity and response level to allulose in a cell-based assay. They used engineered PsiR as a\ncontrol element in a genetic circuit to create a useful allulose-triggered auto-inducible protein\nexpression system. This required a decent amount of strain engineering. It’s an interesting paper\nwith several issues.\n1. It is very annoying that the author-provided PsiR crystal structure is numbered incorrectly. The\nstructure is incomplete, but the residues should still be indexed correctly (according to the wild-type\nnumbering). Because of the lack of DBD in the PsI structure and their decision not to follow the\nWT numbering, the numbering in the PDB file is off. This makes it ridiculously difficult to follow,\nunderstand or check the claims in their paper. The authors should fix this.\n[Response]: We really appreciated the reviewer’s comments.\nWe were so sorry for our mistake in numbering the residues of provided PsiR crystal structure.\nAccording to the reviewer’s suggestions, we have renumbered residues in given PsiR structure and\nupdated the data in PDB database (9KPR).\nThe corrected PDB file was also attached.\n2. To make matters worse, it looks like some of the residues are labeled wrong, because the\ndifference in numbering in the paper vs. structure is inconsistent. For example, R109 in Fig. 4: I\nthink the residue they are trying to point out is actually R45 in their structure (64 res difference).\nBut in the same figure, D202 and D288 correspond to D138 and D224 in the structure (86 res\ndifference). And, again in the same figure, it seems that V134 and T135 are actually V70 and V71\n(64 residue difference)! This is sloppy.\n[Response]: We really appreciated the reviewer’s comments.\nWe were so sorry for the misunderstanding label. In the revised manuscript, we have provided\ncorrected crystal structure and revised the residue label. We confirmed that the R109, D202, D288,\nV134, T135 and other residues mentioned in the manuscript were consistent with the crystal\nstructure number.\n3. The authors should make it clear in the main text of their paper when they introduce the PsiR\nstructure that it does not include the DNA-binding domain. It is currently written to sound as if it\ndoes include this domain.\n[Response]: We really appreciated the reviewer’s comments.\nWe were so sorry for the misunderstanding structure information. According to the reviewer’s\nsuggestions, we have revised the manuscript and given more clear information for the PsiR structure.\nLine 129-141: “To date, the crystal structure of PsiR and its binding conformations with allulose\nand the operator sequence PsiO have remained unknown. To better understand the molecular basis\nof PsiR, we attempted to determine the full-length crystal structure of the PsiR–allulose complex\nbut were unsuccessful. Notably, several reported crystal structures of LacI family TFs, such as LacI\n(PDB: 2P9H), RbsR (PDB: 4RK1), SghR (7CDX), and Cra (PDB: 3O75), lack the N-terminal\nDNA-binding domain (DBD), which is responsible for DNA recognition. Inspired by the truncated\nconstruct of LacI structure (PDB: 2P9H), we introduced a N-terminal truncation in PsiR at Ser69.\nThis strategy enabled us to successfully solve the crystal structure of the PsiR–allulose complex at\n2.02 Å resolution using X-ray crystallography (PDB: 9KPR; Supplementary Table 5). The overall\nstructure forms a homodimer, with each monomer consisting of a C-terminal core domain that\ngoverns both ligand binding and allosteric transitions. Although PsiR shares only 24.8% sequence\nidentity with LacI, it adopts a similar three-dimensional structure similar to that of LacI and other\nmembers of the LacI-family TFs (Supplementary Fig. 4).”\n4. The authors should make it clear in the main text of the paper how they are performing their\nsimulations with a brief 1-2 sentence description, particularly for the steered MD simulations, and\nwith which regions of the proteins (e.g., not including the DBD), and how these input models were\nproduced. It is not standard practice to perform MD simulations with AlphaFold structures and\nespecially AF structures with ligands bound. The authors should make it easier for the reader to\nfollow what they did, to be able to decide how much to trust those data.\n[Response]: We really appreciated the reviewer’s comments.\n(1) In our study, we employed steered molecular dynamics (SMD) simulations to analyze the\neffector dissociation from effector binding site through the access tunnel. The utility of SMD for\ninvestigating ligand-protein dissociation and translocation processes has been well-established in\nthe literature (Bu L, et al. Probing carbohydrate product expulsion from a processive cellulase with\nmultiple absolute binding free energy methods. J. Biol. Chem., 2011, 286(20), 18161-18169). This\nenhanced-sampling technique allowed us to identify key residues that interact strongly with the\neffector during its entrance, thereby pinpointing potential mutation hotspots.\nAccording to the reviewer’s comments, we have revised the manuscript and provide more clear\ndetails to for the computational methodology.\nIn order to specify our SMD configurations, we also revised the main text as followed:\nLine 192-197: “To investigate the interactions between allulose and the tunnel-lining residues\nduring allulose translocation, steered molecular dynamics (SMD) simulations were conducted by\napplying a pulling force to guide allulose from the EBD along the predefined tunnel 1. A collective\nvariable, d was defined as the COM distance between allulose and the key active-site residue D288.\n1,\nThe interaction energies (ΔE(kcal/mol)) between allulose and individual residues along the route\nwere calculated to identify potential hotspot residues (Supplementary Fig. 8).”\nThe more detailed SMD procedures were provided in supplementary information part (Line\n152-168):\n“Steered molecular dynamics (SMD) simulations were performed to investigate the egress pathway\nof the inducer from the protein's access tunnel. For the PsiR-allulose and LacI-IPTG systems, the\ninitial structures were based on their respective crystal structures (PDB: 9KPR for PsiR-allulose;\nPDB: 2P9H for LacI-IPTG). In both cases, the DNA-binding domains (DBDs) were truncated, as\nour focus was on the inducer-binding domain and the associated translocation tunnel. All input files\nand system topologies were generated using the CHARMM-GUI web server. Following the\nstandard minimization and equilibration procedures described in the Classical MD section, each\nsystem was subjected to a 5 ns NPT equilibration run. An equilibrated snapshot from the end of this\nrun was selected as the starting point for the production SMD simulation. For both systems, a\nharmonic constraint with a spring constant of 200 kcal/mol·Å² was applied to the center-of-mass\n(COM) of the effector (allulose or IPTG). The constraint's reference point was moved at a constant\nvelocity of 0.0025 Å/ps, effectively pulling the effector out along a defined collective variable (CV)\nfrom an initial distance of ~7 Å to a final distance of 25 Å over a 7 ns simulation period. For the\nPsiR-allulose system, the CV was defined as the COM distance between allulose and the side chain\nof D288, a key residue deep within the active site. For the LacI-IPTG system, the CV was similarly\ndefined as the COM distance between the COM of IPTG and D274. Each different SMD assembly\nwas carried out for 10 replicase.”\n(2) The reviewer also mentioned “It is not standard practice to perform MD simulations with\nAlphaFold structures and especially AF structures with ligands bound”. We want to give some\nexplanations as followed.\n1) The release of AlphaFold3 represents a major breakthrough in the high-accuracy prediction of\nbiomolecular complexes, including nucleic acid–protein interactions. Notably, AF-predicted models\nof transcription factors—such as the p53–DNA complex—demonstrate sufficient structural quality\nto serve as reliable initial configurations for molecular dynamics (MD) and steered molecular\ndynamics (SMD) simulations aimed at investigating mutational effects on binding stability\n(Rustamov et al. Investigation of structural mechanisms underlying p53 dysfunction caused by 148\nmissense mutations using AlphaFold3 and molecular dynamics simulations. J. Chem. Inf. Model.\n2025, 65, 6322−6330). Given these precedents and the demonstrated capabilities of the tool, we\nconsidered AlphaFold3 to be a state-of-the-art approach for addressing our specific biological\nquestion. In this study, we employed AlphaFold3 to predict the full-length structure of PsiR as well\nas the PsiR–PsiO complex, where PsiO refers to the DNA operator sequence of PsiR. The resulting\nallulose-free model served as the starting structure for conventional molecular dynamics simulations,\nwhich were used to evaluate the interaction between PsiR and its DNA operator.\n2) We further provided a high-quality structural model for molecular dynamics (MD) simulations.\nThe predicted PsiR–PsiO complex exhibited high confidence, with an average predicted Local\nDistance Difference Test (pLDDT) score exceeding 90 for the protein domains. Importantly, the\nprotein–DNA interface showed high reliability, as indicated by an interface Prediction Template\nModeling (ipTM) score of 0.85—above the confidence threshold of 0.8 recommended in\nAlphaFold3 guidelines (https://alphafoldserver.com/guides).\nTo further validate the stability of the AlphaFold3-predicted PsiR–PsiO complex, we carried out\nthree independent classical MD simulations, each lasting 1 μs (aggregate 3 μs). Throughout all\nsimulations, the complex remained structurally stable with no signs of conformational drift,\ninterface disruption, or dissociation. We assessed stability using two quantitative metrics: (i) the\nroot-mean-square deviation (RMSD) of PsiR and double-stranded DNA converged within 200 ns\nand remained stable thereafter; and (ii) the center-of-mass (COM) distance between the DBDs and\nthe conserved 20-bp core region of PsiO remained constant at approximately 10 Å. We believe these\nclarifications and supporting data adequately address the reviewer’s concerns, and more detailed\ninformation has been included in the revised manuscript.\nSupplementary Figure 5 Molecular dynamic of the PsiR–PsiO complex predicted by AlphaFold3.\na) RMSD over three independent 1 µs classical MD trajectories (3×1 µs). (b) RMSD of the\nconserved 20-bp core dsDNA over the same trajectories. (c) The PsiR–DNA complex. The CV, d ,\n0\nwas defined as the centre-of-mass (COM) distance between the PsiR DNA-binding domain (DBD)\nand the 20-bp PsiO core. (d) Time evolution of d across the three trajectories.\n0\n3) In this study, we aimed to develop a rational engineering strategy for transcription factor (TF)\nengineering to enhance signal output and sensitivity. To this end, we focused on engineering key\nfunctional aspects of PsiR, including the allulose access tunnel, binding site, and allosteric\nmechanism. These efforts primarily targeted residues located within the core domain of PsiR.\nAlthough we attempted to solve the full-length crystal structure of the PsiR–allulose complex,\nwe were unsuccessful. It is noteworthy that several reported crystal structures of LacI-family\ntranscription factors—such as LacI (PDB: 2P9H), RbsR (PDB: 4RK1), SghR (7CDX), and\nCra (PDB: 3O75)—also lack the N-terminal DNA-binding domain (DBD), which is essential for\nDNA recognition. Inspired by the truncated structure of LacI (PDB: 2P9H), we introduced a C-\nterminal truncation in PsiR at Ser69. This modified construct enabled us to successfully determine\nthe crystal structure of the PsiR–allulose complex at 2.02 Å resolution using X-ray crystallography\n(PDB: 9KPR). All subsequent molecular dynamics (MD) and steered molecular dynamics (SMD)\nsimulations were performed based on this resolved structure.\nWe also provided the detailed information in the manuscript.\nLine 144-149: “The predicted structural model was quantitatively assessed using two key metrics:\ni) the root-mean-square deviation (RMSD) of PsiR and DNA converged within 200 ns and remained\nstable thereafter; and (ii) the center-of-mass (COM) distance between the DBDs and the conserved\n20-bp core region of PsiO remained constant (Supplementary Fig. 5). The DBD adopts a helix-turn-\nhelix (HTH) motif and is connected to the core domain via a flexible hinge region (residues 46–\n66).”\nLine 116-125 in Supplementary Information: “The predicted PsiR-PsiO complex exhibited high\nconfidence scores, with an average predicted Local Distance Difference Test (pLDDT) score of >90\nfor the protein domains and, critically, high confidence in the protein-DNA interface as assessed by\nthe interface Predicted Template Modeling (ipTM), is 0.85, higher than the 0.8 confident standard\ndefined in AF3 guides (https://alphafoldserver.com/guides). To further validate the stability of the\nAlphaFold3-predicted PsiR-PsiO complex, we conducted three independent 3×1 μs classical MD\nsimulations. Throughout all simulations, the complex maintained a stable conformation, with no\nevidence of conformational drift, interfacial disruption, or dissociation.”\n5. When the authors use collective variables to cluster frames from MD simulations, they should\nbriefly explain the methods and demonstrate the robustness of the result by showing that it holds up\nwhen they try other, similar, redundant CVs.\n[Response]: We really appreciated the reviewer’s comments.\n(1) In this work, we employed the two-dimensional well-tempered metadynamics with extended\nadaptive biasing force (WTM-eABF) method to investigate the evolutionary enhancement in\nsensitivity and dynamic range of PsiR-M2. The WTM-eABF approach integrates the strengths of\nwell-tempered metadynamics and the extended Lagrangian adaptive biasing force technique,\nmaking it particularly suitable for characterizing multidimensional and rugged free energy\nlandscapes (Fu, H., et al. Taming rugged free energy landscapes using an average force. Acc. Chem.\nRes. 2020, 53, 531–541). This method has been successfully applied in studies of dynamic\nconformational processes, such as glucan–enzyme interactions (Zong, et al. Elucidation of the\nnoncovalent interactions driving enzyme activity guides branching enzyme engineering for α-glucan\nmodification. Nat Commun, 2024, 15, 8760) and protease catalytic mechanisms (Zhou, Z., et al.\nStructural basis for the acylation reaction of alphacoronavirus 3C-like protease. ACS Catal. 2024,\n14, 8330–8342). Notably, Zong et al. utilized WTM-eABF to reconstruct the free energy landscape\nassociated with cellobiose translocation in cellobiohydrolases (Zong, Z., et al. Lysine mutation of\nthe Claw-Arm-Like loop accelerates catalysis by cellobiohydrolases. J. Am. Chem. Soc. 2019, 141,\n36, 14451–14459). The computational procedures adopted in their study are analogous to those used\nin our investigation of allulose translocation within PsiR. In the aforementioned work, a 500 ns\nsimulation was sufficient to achieve convergence in the local free energy landscape. In our study,\nthe two-dimensional free energy landscapes were derived from extended simulations of 700–800 ns,\nensuring robust convergence and reliability.\n(2) In this study, to quantitatively characterize the interaction between PsiR and allulose throughout\nmolecular dynamics simulations, a total of three collective variables (CVs) were designed. Below,\nwe provide further justification for the rationale behind the selection and construction of these CVs.\ni) The first CV, d , was defined as the center-of-mass (COM) distance between key residue D288\n1\nand allulose in PsiR-allulose complex. Structural analysis reveals that D288 is a key residue located\nwithin the binding pocket, forming a critical hydrogen bond with allulose. Structural analysis\nindicates that D288 is a critical residue within the binding pocket, forming a stable hydrogen bond\nwith allulose. Thus, the distance between allulose and D288 provides a relevant reaction coordinate\nfor characterizing the dissociation process of allulose from the binding site. Accordingly, changes\nin d reflect the trajectory of allulose as it transitions from the bound state to a solvent-exposed state.\nbetween IPTG and the equivalent residue D274.\nii) In the wild-type PsiR, we identified a salt bridge interaction between D202 and R109 within the\naccess tunnel, which restricts the entry of allulose into the effector-binding domain (EBD), such as\nthe primary binding residue D288. To disrupt this interaction, we engineered mutations at R109 with\nthe aim of facilitating allulose access. To quantitatively evaluate the effect of these mutations on the\nsalt bridge stability, the second collective variable (d ) was defined as the COM distance between\n2\nthe carboxyl group of D202 and the guanidinium group of R109.\niii) To quantify the interaction energy between allulose and residue 109 during its translocation\nacross different PsiR mutants, we introduced a third collective variable, d , defined as the COM\n3\ndistance between allulose and the amino acid at position 109. While d captures the progression of\n1\nallulose along the access tunnel, d , represents the local interaction strength between allulose and\n3\nresidue R109. A two-dimensional free energy landscape (ΔG, in kcal/mol) was then constructed as\na function of d and d . Comparative analysis of these landscapes among PsiR-WT, PsiR-M1\n1 3\n(R109G), and PsiR-M2 (R109G/V134A) was conducted to elucidate the mechanistic basis for the\nenhanced sensitivity and broadened dynamic range observed in the evolved variants.\nThe rationale and design principles for the collective variables used in this work are consistent with\nthose established in prior studies (Zong, Z., et al. Lysine mutation of the Claw-Arm-Like loop\naccelerates catalysis by cellobiohydrolases. J. Am. Chem. Soc. 2019, 141, 36, 14451–14459; Zong,\net al. Elucidation of the noncovalent interactions driving enzyme activity guides branching enzyme\nengineering for α-glucan modification. Nat Commun, 2024, 15, 8760).\nWe also gave more clear information for the reasonability for CVs design in the revised\nmanuscript.\nLine 194-195: “A collective variable, d was defined as the COM distance between allulose and\n1,\nthe key active-site residue D288.”\nLine 205-207: “To monitor the salt bridge interaction between R109 and D202 during allulose\ntranslocation for PsiR-WT and its mutants, the collective variable, d , was defined as the COM\n2\ndistance between the carboxyl group of D202 and guanidyl group of R109.”\nLine 244-254: “To elucidate the evolution of enhanced sensitivity and dynamic range of M2, we\nemployed two-dimensional well-tempered metadynamics-extended adaptive biasing force (WTM-\neABF) simulations. This approach is effective for resolving multidimensional and rugged free\nenergy landscapes and has been successfully applied to investigate dynamic processes, including\nglucan–enzyme interactions and protease catalysis40-42. To characterize the free energy landscape\nassociated with allulose translocation, we introduced the third collective variable, d , defined as the\n3\ndistance between allulose and residue R109, which reflects the local interaction strength at this key\nengineering site. Concurrently, the collective variable, d , was used to monitor the distance between\n1\nallulose and D288, capturing its dissociation path from the binding site to the solvent-exposed state.\nBased on these variables, we computed and compared the two-dimensional free energy landscapes\n(ΔG(kcal/mol)) as functions of d and d for PsiR-WT, PsiR-M1 and PsiR-M2 (Fig. 4g).”\n1 3\n6. The LacI engineering is weak. It has been known for decades that LacI R101 is amenable to\nmutation (originally by work in the Miller group: Suckow et al., Markiewicz et al., JMB, 1990s); it\nis not shocking that a couple of mutations at this position would increase the fluorescence by 2-3X,\nand this could even just be due to increased cell expression. Further, the small increase in\nfluorescence without a predictable shift in the IPTG sensitivity at 10 µM IPTG is not a good\ndemonstration of generalizing the much more thorough results from PsiR engineering. If the authors\nwant to claim that their engineering strategy is generalizable, they should engineer a panel of LacI\nmutants (or even just 1-2 mutants) that have a variety of IPTG sensitivities, as in Fig. 5i for PsiR,\nand compare an expected response concentration to the measured one using a titration. Maybe the\nR101 mutants do this, but the authors do not show it. This is more difficult than the result for LacI\nmutants in Fig. S12, but if the engineering strategy is truly generalizable then the authors should be\nable to do it.\n[Response]: We really appreciated the reviewer’s comments and suggestions.\nIn this study, we aimed to develop a computational design strategy for engineering transcription\nfactors (TFs). Rational engineering of the effector access tunnel proved to be an effective approach\nfor enhancing the sensitivity and signal output of PsiR. To assess the generalizability of this strategy,\nwe extended its application to another LacI-family TF, LacI, with the goal of improving its signal\noutput and sensitivity. We first constructed a LacI-based GFP reporter sensor in Corynebacterium\nglutamicum.\nPotential access tunnels were identified using CAVER 3.0, and steered molecular dynamics (SMD)\nsimulations were performed by pulling IPTG along the most likely tunnel pathway. Interaction\nenergies between IPTG and individual residue were calculated to pinpoint potential hotspot residues.\nFive residues—D149, N125, R101, S69, and S70—exhibited strong interactions with IPTG. Among\nthese, D149 and N125 were excluded from mutagenesis due to their roles in the conserved D–R–\nD/R triad essential for ligand binding and allosteric regulation.\nSupplementary Figure 13 Tunnel-guided SMD pinpoints hotspots along the IPTG translocation in\nLacI. a) CAVER analysis of the crystal structure of LacI (PDB 2P9H) for potential access tunnel.\n(b) Interaction-energy traces for additional tunnel-lining residues (L73, P76, N125, Y126, P127,\nD149, P188, S191, W220).\nTo reduce steric and electrostatic hindrance at R101, we substituted this residue with smaller amino\nacids (Ala, Gly, Ser). The R101A and R101S mutants exhibited a 2–3 fold increase in fluorescence\noutput at 10 μM IPTG compared to the wild type. Additionally, residues S69 and S70, located at the\nentrance of the access tunnel, also showed interactions with IPTG. Since these positions are already\noccupied by serine, we performed saturation mutagenesis to comprehensively evaluate potential\nimprovements. Therefore, eighteen mutants exhibited a 2.0–4.8 fold increase in signal output.\nThe S70M mutant showed the highest output but also elevated basal leakage in the absence of IPTG.\nIn contrast, mutants S69C, S70T, and S70N maintained low basal leakage levels comparable to the\nwild type, while still achieving 2.0–3.2 fold enhancements in signal output. These four beneficial\nsingle mutants (R101S, S69C, S70T, and S70N) were further characterized across a range of IPTG\nconcentrations, and their dose-response curves were fitted to the Hill equation. The results\nconfirmed that the engineered mutants exhibit increased dynamic range and sensitivity compared to\nthe wild-type LacI.\nSupplementary Figure 14 The influence of R101 mutations in LacI on signal output. a) The\nfluorescence for LacI-WT, R101G, R101S, R101A at 0 and 10 μM. b) The IPTG dose-response\nprofiles for WT-LacI and R101S fitted with Hill equation.\nSupplementary Figure 15 The influence of S69 and S70 mutations in LacI on signal output. a,c)\nSaturation mutagenesis at residue S69 and S70; b, d) Dose-response of variant S69C, S70T and\nS70N fitted with the Hill equation.\nCollectively, we constructed and evaluated 41 single-point mutants of LacI, among which 20\nexhibited enhanced signal output, including several mutants with improved dynamic range.\nAlthough the sites of R101, S69 and S70 have been screened with large-scale mutation method\n(Tack, et al, The genotype-phenotype landscape of an allosteric protein, Mol Syst Biol. 2021,\n17(3):e10179), the beneficial mutants of R101S, S69C, S70T, and S70N identified here have not\nbeen reported previously. These results further demonstrate the value of computational tunnel\nengineering as a generalizable strategy for refining ligand sensitivity in LacI-family transcriptional\nbiosensors.\nWe also revised the manuscript and provided more clear information for this part:\nLine 264-290:\n“Computer-aided tunnel engineering of LacI\nOur results demonstrate the feasibility of computer-aided tunnel engineering for enhancing sensor\nsensitivity, signal output, and dynamic range of PsiR through the screening of a focused mutational\nlibrary. To assess the generalizability of this strategy across the LacI transcription factor family, we\napplied the same approach to engineer LacI for improved signal output and sensitivity. We first\nconstructed a LacI-based GFP reporter sensor in C. glutamicum. Then, the SMD simulations were\nperformed by pulling IPTG along the most likely tunnel pathway, and interaction energies between\nIPTG and individual tunnel-lining residues were analyzed (Supplementary Fig. 13). This identified\nfive residues of D149, N125, S101, S69, and S70 exhibiting strong interactions with IPTG. The\nD149 and N125 were excluded from mutagenesis due to the conserved D–R–D/R triad in ligand\nbinding and allosteric regulation37. To reduce steric and electrostatic hindrance at R101, we\nsubstituted it with smaller residues (Ala, Gly, Ser). Among these variants, the R101A and R101S\nmutants exhibited a 2–3 fold increase in fluorescence output at 10 μM IPTG compared to the wild-\ntype LacI (Supplementary Fig. 14).\nFurther analysis revealed that residues S69 and S70, located at the entrance of the access tunnel,\nalso interact with IPTG. Since these positions are already occupied by the small amino acid serine,\nwe performed saturation mutagenesis to comprehensively explore potential improvements at these\nsites. Eighteen mutants increased the signal output by 2.0-4.8 fold (Supplementary Fig. 15). Among\nthese, the mutant S70M yielded the highest output but exhibited elevated basal leakage in the\nabsence of IPTG. In contrast, the S69C, S70T, and S70N maintained a low basal leakage level\ncomparable to that of the wild-type, while still achieving 2-3.2 fold enhancement in signal output.\nFurthermore, those four beneficial single mutants (R101S, S69C, S70T, and S70N) were further\ncharacterized across a gradient of IPTG concentrations, with their responses were fitted to the Hill\nequation (Supplementary Figs. 14b, 15b, and 15d). The results confirmed the increased dynamical\nrange and sensitivity of engineered mutants relative to wide-type. These findings further validate\nthe utility of computational tunnel engineering as a generalizable strategy for tuning ligand\nsensitivity in LacI-family transcriptional biosensors. ”\n7. Can the authors provide more context for the “rigid element” in LacI (line 290)? Do they mean\nmore rigid in the IPTG-bound state as compared to the DNA-bound state? There is also a more\nrecent, higher-resolution LacI study: see Fig. 6 in Lu et al.:\nhttps://www.biorxiv.org/content/10.1101/2024.08.04.606547v1.full. The authors can cross-check\nthis new data to see if whichever residue corresponds to PsiR R206 is also more flexible than nearby\nregions as they see in their RMSF analysis.\n[Response]: We really appreciated the reviewer’s comments.\nIn recent hydrogen-deuterium exchange mass spectrometry (HDX/MS) studies on LacI,\nGlasgow et al. have systematically characterized conformational flexibility across functional states\n(apo, IPTG-bound, ONPF-bound, and DNA-bound) (Glasgow, A. et al. Ligand-specific changes in\nconformational flexibility mediate long-range allostery in the lac repressor. Nat. Commun. 14, 1179\n(2023)). These analyses revealed significant state-dependent flexibility fluctuations and implicated\nspecific structural segments in allosteric communication. Notably, three regions exhibited increased\nrigidity in the IPTG-LacI state compared to the DNA-LacI state:\ni) Segment 72-ALHAP (residues Ala72–Pro76), which stabilizes the H74–H74′ π-stacking\ninteraction critical for DNA-binding affinity. Mutational scanning confirms this region's functional\nsensitivity (Flynn, T. C. et al. Allosteric transition pathways in the lactose repressor protein core\ndomains: asymmetric motions in a homodimer. Protein Sci. 12, 2523–2541 (2003)).\nii) Segment 94-VVVSM (residues Val94–Met98), located at the N-subdomain monomer-monomer\ninterface. This region undergoes substantial conformational rearrangement during allostery and\ndisplays high mutational risk (Flynn, T. C. et al. Allosteric transition pathways in the lactose\nrepressor protein core domains: asymmetric motions in a homodimer. Protein Sci. 12, 2523–2541\n(2003)).\niii) Segment 187-GPLSSVSAR (residues Gly187–Arg195), a previously underexplored region\nsuggested to play a potential role in allostery. We aligned the LacI-IPTG structure (PDB: 2P9H) to\nour PsiR-allulose complex. Structural alignment identified Arg195 in LacI (equivalent to Arg206 in\nPsiR) as a key residue within segment (iii). The high-throughput mutational scanning results (Tack,\nD. S. et al. The genotype-phenotype landscape of an allosteric protein. Mol. Syst. Biol. 17, e10179\n(2021)) found a series R195 variants exhibited a different phenotype. Especially, R195H exhibited\nthe \"band-stop\" phenotype (high G , and initially decreasing gene expression as ligand\n0\nconcentration increases). Our results showed that the RMSF value under LacI-IPTG was 1.08,\nwhich was lower than that of LacI-IPTG (Supplementary Figure 18), indicating that this region\n(G187–R195) was more stable under LacI-IPTG state. The similar result was also observed for\nG199-R206 in PsiR-allulose state relative to PsiR-DNA state.\nSupplementary Figure 17 The structure alignment of PsiR and LacI. a) Crystal structure of LacI\nin complex with IPTG. Three regions previously reported to be more rigid in the IPTG-bound than\nin the DNA-bound state are highlighted in teal21; residue ranges are annotated next to each segment.\nb) Structure of the PsiR-allulose complex. Based on a structure-based alignment between LacI and\nPsiR, the PsiR segments corresponding to the LacI rigid regions are likewise highlighted in teal,\nwith the covered residues indicated.\nSupplementary Figure 18 The RMSF data of structural rigid regions for LacI and PsiR. (a) LacI–\nDNA bound state; (b) LacI–IPTG bound state; (c) PsiR–DNA bound state; (d) PsiR–allulose bound\nstate. The RMSF_Avg (Å) values were calculated from two independent 1 µs MD simulations PsiR.\nThe results represent the average of four-RMSF values. The G187–R195 region in LacI and G199-R206 region in PsiR were marked and compared in different binding state. Our results showed that\nthe RMSF value under LacI-IPTG was 1.08, which was lower than that of LacI-DNA (1.51),\nindicating that this region (G187–R195) was more stable under LacI-IPTG state. The similar result\nwas also observed for G199-R206 in PsiR-allulose state (1.17) relative to PsiR-DNA state (1.57).\nTo give more clear information for rational engineering this region, we also revised the manuscript\nas followed:\nLine 326-336: “In a hydrogen–deuterium exchange mass spectrometry (HDX/MS) study, Anum\net al. systematically profiled allosteric conformational changes across the full core domain of LacI44,\n45. Their analysis revealed increased structural rigidity in several regions adjacent to the ligand-\nbinding pocket in the IPTG-bound state compared to the DNA-bound state, suggesting these\nsegments as potential hotspots for allosteric engineering. Through structural alignment of PsiR with\nLacI, we identified a corresponding segment (residues G199–R206 in PsiR; homologous to G187–\nR195 in LacI) located near the allulose-binding site within the N-terminal subdomain\n(Supplementary Fig. 17). Furthermore, root-mean-square fluctuation (RMSF) analysis revealed\ngreater stability in this region under the PsiR–allulose state compared to the PsiR–DNA binding\nmode (Supplementary Fig. 18). However, residue R206 exhibited increased flexibility relative to\nsurrounding residues. To enhance the local rigidity of this segment, we conducted saturation\nmutagenesis at position R206.”\n8. Most of these data are from cell-based fluorescence measurements, but it would build confidence\nin the engineering strategy and results if the authors could show the results of an in vitro binding\nassay for WT PsiR and an engineered mutant, like M2, M3 or M4, for both the allulose and PsiO\nligands. The dynamic range of the WT and the mutants is hopefully comparable in such an\nexperiment.\n[Response]: We really appreciated the reviewer’s comments and suggestions.\nIndeed, in this study, the DNA–PsiR and allulose–PsiR binding interactions were derived from cell-\nbased fluorescence assays. In response to the reviewer’s comments, we constructed pET21 plasmids\ncarrying PsiR-WT, PsiR-M2, and PsiR-M4, and transformed them into E. coli BL21(DE3) for\nprotein expression (Supplementary Fig. 19). The resulting PsiR proteins were purified using His-\ntag affinity chromatography. We then performed electrophoretic mobility shift assays (EMSAs) to\nevaluate the binding between PsiO and each PsiR variant (WT, M2, and M4). Additionally, we used\nmicroscale thermophoresis (MST) to quantify the binding affinities between allulose and the PsiR\nproteins. The corresponding results are presented in Supplementary Figs. 20 and 21. The EMSA\nanalyses confirmed specific binding between PsiO and each PsiR variant compared to the PsiO-only\ncontrol. Notably, in the presence of allulose, the PsiO–PsiR complex dissociated more readily for\nthe M4 variant at the same allulose concentration, indicating its enhanced sensitivity. Consistently,\nMST measurements verified allulose binding to all three PsiR proteins, with PsiR-M4 exhibiting\nbinding at lower allulose concentrations compared to WT and M2. These in vitro results further\ncorroborate the superior sensitivity of the engineered PsiR-M4 variant.\nSupplementary Figure 19 SDS–PAGE analysis of PsiRs. The PsiR-WT, PsiR-M2, and PsiR-M4\nwere expressed in E. coli and purified for in vitro assays. Lane C: cell lysate; Lane S: supernatant\nafter centrifugation; Lane E: eluted protein; Lane P: purified protein after ultrafiltration to remove\nimidazole; Lane M: markers. The PsiR has an expected whole-length molecular mass of ~39.5 kDa.\nSupplementary Figure 20 EMSA assays of PsiR–DNA binding. a) Schematic diagram for EMSA\nassays in this study. Three experiments were conducted. The labelled probe is a 3′-biotin DNA (40\nbp) from the P promoter containing the PsiO site. b) The EMSA results of PsiR-DNA binding\ntacMD\nby optimizing PsiR concentration. Lane 1, free probe. Lanes 2–5, binding reactions with increasing\nPsiR (0.1-1 μg, as indicated). c) The effect of code probe on PsiR-DNA binding during EMSA assays.\nThe code probe was the unlabelled specific competitor possessing a 40-bp PsiO-containing fragment\nfrom the P promoter. Lane 5, free probe; lane 6, binding reaction without code probe; lanes 7–\npsiA\n12, binding reactions supplemented with code probe at 50×, 100×, 200×, 400×, 600× and 800×\nrelative to the labelled probe. d) The effect of allulose supplement on PsiR-DNA binding during\nEMSA assays. Lane 13, free probe; lanes 14–16, binding reactions with PsiR-WT, PsiR-M2 and\nPsiR-M4 (no effector); lanes 17–19, the same proteins in the presence of allulose.\nSupplementary Figure 21 The MST assays of PsiR–allulose binding. The ΔFnorm (‰) of PsiR-\nWT, PsiR-M2, and PsiR-M4 were measured in label-free mode at 25 °C. The normalized ΔFnorm\n(‰) values were fitted to a 1:1 binding model using the Monolith Analysis Software to determine\nthe dissociation constants (K ).\nd\nWe also revised the manuscript and gave more clear information as followed:\nLine 358-364: “To biochemically validate these findings, we performed electrophoretic mobility\nshift assays (EMSAs)46 and microscale thermophoresis (MST)47 to analyze the DNA–PsiR and\nallulose–PsiR binding interactions. After purifying PsiR-WT, M2, and M4 from E. coli BL21(DE3)\nvia His-tag affinity chromatography (Supplementary Fig. 19), both EMSA and MST confirmed\nspecific binding between PsiR and its ligands (Supplementary Fig. 20). Notably, PsiR-M4 exhibited\nallulose binding at lower concentrations than the WT and M2 (Supplementary Fig. 21),\ncorroborating its enhanced sensitivity.”\nLine 67-100 in Supplementary information part:\n“Electrophoretic mobility shift assays\nThe Electrophoretic mobility shift assays (EMSAs) were performed using a 40-bp\noligonucleotide derived from P (5′-\ntacMD\nCATGTGTGGATTGCACAATCGATGGTGCAAAAGGAAAGGA-3′), which contains a 20-bp\nPsiO operator sequence flanked by 10-bp extensions at both ends. The single-stranded probe was\nsynthesized with a 3′-biotin modification (Tsingke Biotechnology Co., Ltd., Beijing, China) and\nsubsequently annealed prior to use. For competition assays, an unlabeled 40-bp oligonucleotide (5′-\nGATATGGTTATTGCACCATCGATTGTGCAGATTGGCAATA-3′) corresponding to the P\npsiA\nregion from the Agrobacterium tumefaciens genome was employed as a specific competitor. The\nbinding reactions (10 μL total volume) contained 10 nM annealed probes and purified His-tagged\nPsiR at concentrations ranging from 100 ng to 2 μg. Samples were resolved on 6% native\npolyacrylamide gels in 0.5× TBE buffer. Then, the DNA–protein complexes were transferred onto\nnylon membranes and cross-linked using a UV cross-linker. Detection was performed with a\nchemiluminescent EMSA kit (GS009, Beyotime, Shanghai, China) according to the manufacturer’s\ninstructions, and signals were visualized using a Tanon 5200 Chemiluminescent Imaging System\n(Shanghai, China).\nMicroscale thermophoresis assays\nThe purified proteins (WT, M2, and M4, with ≥95% purity as determined by SDS-PAGE) were\ndiluted to 500 nM using storage buffer (25 mM Tris-HCl, pH 7.5, 500 mM NaCl). Allulose was\ndissolved in the same buffer. All samples were centrifuged at 20,000 × g for 10 min and kept on ice\nuntil use. A 16-point two-fold serial dilution of allulose was prepared to cover concentration ranges\nof 7.63 μM to 250 mM for WT, 488 nM to 16 mM for M2, and 763 nM to 100 mM for M4. For\neach protein variant, 10 μL of protein (500 nM) was mixed with 10 μL of each allulose dilution (16\nsamples per protein) in a 96-well low-protein-binding plate via gentle pipetting to avoid bubble\nformation. The mixtures were incubated at room temperature for 20 min to allow protein–ligand\nbinding equilibrium. Measurements were performed on a Monolith instrument operated in Label-\nFree Capillary mode. The instrument was preheated to 25 °C and equilibrated for 15 min prior to\nmeasurements. Using low-retention pipette tips, 10 μL of each sample was loaded into label-free\nMST capillaries. The following instrument settings were applied: excitation power = 1% (WT) or\n8% (M2 and M4); MST power = 40%; pre-MST time = 3 s; MST time = 20 s; post-MST time = 1\ns. All measurements were performed in triplicate to ensure reproducibility. For data analysis, ΔF\nand A_norm values were exported from the Monolith Analysis Software (v3.0.5, NanoTemper\nTechnologies). The normalized ΔF values were fitted to a 1:1 binding model using the same software\nto determine the dissociation constants (Kd).”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The manuscript was revised and improved very well according to reviewers' suggestion and comments.I suggest accept.", "reviewer_2": "The authors have performed additional experiments and my comments were addressed, I recommend it for publication.", "reviewer_3": "Thank you to the authors for such an excellent revision. They addressed all of my concerns, and the revised paper is both much easier to follow, and also a lot more exciting. I appreciate the updated PDB files and expanded methods. My favorite addition to the manuscript is the in vitro binding data from the new MST experiments.\nThe authors may also be interested in some other recent work related to shared structural changes/engineering in this TF family:\nhttps://pubs.acs.org/doi/full/10.1021/acssynbio.4c00694\nhttps://www.biorxiv.org/content/10.1101/2025.04.02.646877v1"}, "author_response": ""}]

Decision letter