This work introduces reverse EXAFS analysis (REA), a novel, ab initio framework that transforms extended X-ray absorption fine structure (EXAFS) spectroscopy from a complementary local probe into a primary tool for de novo crystal structure determination. By integrating iterative FEFF simulations with a reverse-fitting algorithm, REA directly extracts full crystallographic parameters, lattice constants, space group and atomic positions, from EXAFS oscillations, without requiring the user to supply an initial structural model from X-ray diffraction; instead, candidate models are retrieved from crystallographic databases and evaluated against EXAFS data. Validated on LiCrO2 and CuFeO2, REA uncovered unexpected phase complexities in CuFeO2, identifying secondary CuFe2O4 and CuFe5O8 phases undetected by conventional methods. This paradigm shift enables accurate structure solving in disordered, nanostructured and novel materials where diffraction fails, establishing EXAFS as a stand-alone crystallographic technique.
Amyloid fibrils are intrinsically polymorphic protein assemblies that form distinct structural strains linked to diverse biological and pathological outcomes. Yet, the principles governing how sequence encodes diverse fibril architectures, and the extent to which a given fold constrains underlying amino-acid sequence compatibility, remain poorly understood. Here, we apply generative protein design to directly interrogate the sequence-structure relationship of defined fibril architectures, using α-synuclein (αS), a protein known to form highly polymorphic amyloid fibrils, as a model system. Sampling sequence space under structural constraints reveals a continuous compatibility manifold in which diverse sequences encode a common amyloid architecture. De novo designed sequences assemble into fibrils, often with enhanced aggregation efficiency relative to αS. A subset exhibits strain-like behaviour, including similar morphologies, efficient cross-templating, and induction of αS cellular propagation, thereby functionally validating structural compatibility with the native fibril fold. Energetic analysis shows that stability is achieved through distinct but compensatory interactions, supporting a non-unique mapping between sequence and structure. Together, our results define a continuous and constrained compatibility landscape underlying amyloid strains, providing a framework for understanding the determinants of polymorphism and establishing generative protein design as a strategy to access this space, interrogate amyloid sequence-structure relationships, and engineer fibrillar protein assemblies and functional biomaterials.
Discussions on what psychological resilience is, its operationalizations, and approaches to measuring it have occupied the scientific community for decades. This manuscript (1) provides an overview of psychometric methods used to assess resilience, including key characteristics, and (2) evaluates resilience scales through a preregistered, content-analytical evaluation of items based on expert ratings. Items from 83 resilience scales were empirically categorized using a literature-based framework with three main categories (resilience as a process, traits, and environmental factors) and 12 corresponding subcategories. Across all scales, 31.79% of items reflected resilience as a process, 51.95% reflected five-factor model traits, and 16.26% reflected environmental factors. An additional analysis weighted results according to citation frequency, showing how operationalizations influence the current research practice: From this perspective, 60.17% of the items relate to process-related content, 34.59% to trait-related content, and 5.24% to environment-related content. This weighting reflects the prevalence and influence of specific scales in the field. The inter-rater agreement across four raters was κ = .68. Overall, existing questionnaires predominantly cover trait-based content, whereas widely used instruments tend to operationalize resilience as a dynamic process. We derive conceptually grounded recommendations for scale selection to improve clarity and comparability in future research.
The 2D dendritic structure of stream networks within fluvial catchments has not previously been linked to reach-scale channel processes. Existing extensions of 2D network models to 3D landscapes yield only pixelized landscapes, without resolving channels or their properties. At-a-point relations for hydraulic geometry alone provide no insight into how fluvial processes sculpt 3D landscapes in which channels are embedded. We provide this insight by reverse-engineering generic fluvial landscapes with scalable dimensions for all attributes. We do this by coupling a) a 2D network generator, b) dimensionless, physically grounded relations for gravel-bed river hydraulic geometry, and c) a simplified hillslope model. Catchment hypsometric curve and relief, as well as relations between local channel properties and basin structure, cannot be predicted with just one of these components. Our model predicts in specific, dimensioned terms not only how attributes such as hypsometric curve change with changing bed grain size, precipitation and catchment area, but also why, because each model element can be interrogated individually or jointly. Our method offers a tool for studying the effect of varied mean annual precipitation and intensity on catchment structure. Our central application is to tectonically inactive, quasi-equilibrium, low-relief montane landscapes on Earth. We underline the insight provided by our formulation through application to analogous planetary fluvial landscapes. We implement this for Mars and Titan using appropriate values for gravitational acceleration and sediment submerged specific gravity, both freely variable in our model. Our reverse-engineering methodology indicates why and how an analogous class of landscapes should differ in different planetary settings.
In industrial production, the yield of desired targets derived from carbon sources is frequently diminished by the competitive influence of cellular metabolism within microbial cell factories. The bioproduction of l-valine exemplifies a classic process that is confronted with such a dilemma, substantially hindering its economic industrial-scale production. In this study, we aim to engineer a cell factory capable of efficiently synthesizing l-valine with high yield by minimizing the consumption of its precursor pyruvate through the TCA cycle under oxygen-limited conditions. Metabolic engineering-based adaptive laboratory evolution (ALE) under oxygen-limited conditions resulted in the development of an evolved strain ALE2-40 with better cell growth and enhanced l-valine yield. Through comparative omics analysis and validation experiments, it was uncovered that during the ALE process, both pyruvate dehydrogenase activity and NADH availability were significantly improved. Moreover, beneficial targets have the potential to contribute to the NADH and ATP pools, thereby further promoting l-valine synthesis. Based on these results, reverse engineering of the evolved strain ALE2-40 was further conducted. Ultimately, the final strain VAL19 demonstrated remarkable performance, achieving an impressive l-valine titer of 93.7 g/L within 28 h in a 5-L bioreactor under oxygen-limited conditions, with a remarkable yield of 60.4% from glucose-equivalent to 92.9% of the theoretical yield-and a productivity of 3.35 g/L/h. These results set a new benchmark for the fermentative production of l-valine, with the highest yield and productivity reported so far.
Traditional electrochemical DNA sensors for Hg2+ rely on target-induced conformational changes of Hg2+-binding DNA probes, which often suffer from limited signal output and complex probe design. To address these issues, this study introduces a novel ratiometric electrochemical DNA sensor based on a "reverse-engineering" strategy, where the signal molecule is pre-immobilized on the electrode instead of the Hg2+-binding DNA. The double-helix DNA intercalator Nile blue (NB) with excellent redox activity was covalently grafted onto a gold electrode to generate an intense inherent signal. In addition, in a solution containing target Hg2+, the polythymine DNA modified onto gold nanoparticles (AuNP@polyT) undergoes a conformational transition to a double-helix structure via specific T-Hg2+-T chemistry. Then the AuNP@polyT-Hg2+ is captured onto the electrode surface through intercalation between NB and the Hg2+-induced DNA duplex, leading to a decrease in the NB electrochemical signal. Subsequently, another intercalator, methylene blue (MB), is adsorbed into the remaining base pair space of polyT-Hg2+, generating a new indicating signal. A ratiometric response for Hg2+ is thus achieved through the decrease in the NB signal and the increase in the MB signal. Within the Hg2+ concentration range of 0.1 nM to 100 μM, log(INB/IMB) exhibits a favorable double logarithmic linear relationship with the logarithm of Hg2+ concentration, achieving a detection limit as low as 40 pM. The sensor was successfully applied to Hg2+ analysis in actual water and tea samples. By combining T-Hg2+-T coordination chemistry with a dual-intercalator ratiometric readout, the proposed reverse-engineered ratiometric electrochemical sensor eliminates complex DNA probe immobilization, improves accuracy and anti-interference capability, and shows great promise for on-site monitoring of Hg2+ in environmental water and tea samples.
Developing additive manufacturing (AM) aluminum alloys with high temperature strength remains a formidable scientific challenge, primarily due to the strengthening precipitates coarsening above 200°C. Conventional heat-resistant alloy design strategies aim to hinder the precipitate coarsening by incorporating low diffusive alloying elements. However, such approaches remain ineffective against thermally driven defect mobilization, especially for vacancy diffusion and dislocation climbing, which are dominant drivers of high temperature weakening. As a result, most AM Al alloys exhibit a rapid decline in strength within this critical temperature range. Through reverse-engineering of intrinsic atom-defect/atom attraction, we employ an intrinsic attraction (IA) strategy to trigger multi-dimensional defect confinement mechanisms. This approach achieves: divacancy clusters anchoring free vacancies; solute atmospheres capturing mobile dislocations and suppressing creep deformation; specific segregation forming nanostructures at precipitate interfaces and interiors to inhibit coarsening. The AM heat-resistant Al alloy demonstrates satisfactory high temperature performance, exhibiting yield strengths of ~305 MPa at 300°C, ~190 MPa at 400°C, coupled with creep resistance at 200-400°C ( ε °  < 10-7/s) and prominent processability for large-size bladed disk. This strategy transcends the conventional empirical paradigm by engineering elemental segregation tendencies at specific sites, provides a universal design approach for the development of aluminum alloys or other high temperature structural materials.
Food composition databases are fundamental for rigorous dietary assessment, yet they often include information only for generic foods. This study aimed to estimate the full nutrient composition of packaged foods using natural language processing (NLP) and optimization modeling. Nutrition Facts tables (NFTs) and ingredient lists for 5371 packaged foods collected by the food quality observatory across 17 food categories available in Québec, Canada, were used. First, an NLP algorithm matched individual ingredients from packaged foods to the closest equivalents in the Canadian Nutrient File 2015, which contains full nutrient profiles for over 5690 ingredients and foods in Canada. Match quality was assessed using cosine similarity scores. Second, an optimization model estimated the proportion of all ingredients (grams per 100 g) from the packaged foods, enabling the reverse-engineering of nutrient composition data found on the NFT. Model performance was assessed using relative errors comparing estimated with known nutrient values reported on NFTs. Over 55% of ingredients were matched to the Canadian Nutrient File with cosine similarity scores ≥0.9, indicating high-quality matches. Across all food categories combined, the median relative error for the estimates of energy and the 10 nutrients reported on NFTs was <|20%|, consistent with Health Canada's accepted variance for NFT declarations, suggesting reliable estimations. Six food categories showed strong results, with all nutrient estimates having median relative errors <|20%|. Eight food categories obtained moderate results, with all nutrient estimates having median relative errors <|20%|, but with a broader range of error values. Three food categories obtained poor results, with several nutrient estimates having relative errors beyond the |20%| threshold. A method based on NLP and optimization modeling can reliably estimate ingredient proportions of a wide variety of packaged foods, allowing for the generation of complete nutrient profiles.
Raman spectroscopy offers a uniquely rich window into molecular structure and composition, making it a powerful tool across fields ranging from materials science to biology. However, the reproducibility of Raman data analysis remains a fundamental bottleneck. In practice, transforming raw spectra into meaningful results is far from standardized: workflows are often complex, fragmented, and implemented through highly customized, case-specific code. This challenge is compounded by the lack of unified open-source pipelines and the diversity of acquisition systems, each introducing its own file formats, calibration schemes, and correction requirements. Consequently, researchers must frequently rely on manual, ad hoc reconciliation of processing steps. To address this gap, we introduce TRaP (Toolbox for Reproducible Raman Processing), an open-source, GUI-based Python toolkit designed to bring reproducibility, transparency, and portability to Raman spectral analysis. TRaP unifies the entire preprocessing-to-analysis pipeline within a single, coherent framework that operates consistently across heterogeneous instrument platforms (e.g., Clinical Fiber-optic Raman System, Commercial Portable System and Commercial Raman Microscope). Central to its design is the concept of fully shareable, declarative workflows: users can encode complete processing pipelines into a single configuration file (e.g., JSON), enabling others to reproduce results instantly without reimplementing code or reverse-engineering undocumented steps. Beyond convenience, TRaP integrates configuration management, X-axis calibration, spectral response correction, interactive processing, and batch execution into a workflow-driven architecture that enforces deterministic, repeatable operations. Every transformation is explicitly recorded, making the full processing history transparent, inspectable, and reproducible. This eliminates ambiguity in how results are generated and ensures that identical protocols can be applied consistently across datasets and experimental contexts. Through representative use cases, we show that TRaP enables seamless, reproducible preprocessing of Raman spectra acquired from diverse platforms within a unified environment. We hope TRaP can empower Raman data processing as a reproducible, shareable, and systematized scientific practice, aligning it with modern standards for computational research. TRaP is released as an open-source software at https://github.com/hrlblab/TRaP.
Metamaterials can achieve extraordinary properties unattainable in natural materials through sophisticated artificial structural design. This study constructs novel tri-periodic minimal surface configurations based on fundamental structures. By employing surface boundary capture techniques to capture infinitely continuous and smooth surfaces, corresponding fusion functions are derived. New configurations are then established: one fused with rod elements and another featuring smooth fusion of multiple fundamental structures. By integrating the advantages of these configurations, novel metamaterials with both excellent load-bearing and heat transfer properties can be designed. Transition fusion functions are employed to derive implicit function construction methods for each new configuration. Through homogenisation analysis of all novel configurations, this study obtains their mechanical vector data. Combined with the implicit function expressions, this configuration and design methodology establishes a novel theoretical approach for reverse-engineering mechanical metamaterials according to specific requirements.
This study applies a forensic-science-based approach combined with three-dimensional CFD simulations to reconstruct a large-scale explosion at a chemical plant and to evaluate the plausibility of candidate accident scenarios. A joint on-site investigation and document review were first used to reconstruct a baseline scenario for butane leakage, vapor cloud formation, and ignition. FLACS simulations were then conducted to examine whether a range of assumed leak volumes could reproduce the observed damage pattern, with particular emphasis on overpressure at a reference building located 93 m from the explosion center. The National Disaster Management Research Institute (NDMI) led the investigation, which included on-site inspections and document analysis. Based on physical evidence such as major structural damage, fragment distribution, and gas detector activation data, the location and dispersion range of the leak were estimated. A 3D simulation of the incident process was performed using the FLACS software. Alongside theoretical leak estimations, a reverse-engineering simulation approach considering actual damage patterns confirmed the possibility of a butane release exceeding 11,000 kg. The simulation results quantitatively matched the measured damage radius and verified that the calculated overpressure levels met established damage thresholds. This study scientifically reconstructed the accident scenario using quantitative simulation methods, demonstrating the effectiveness and applicability of forensic-based disaster investigations. The proposed methodology may serve as foundational data not only for identifying the root causes of disasters but also for informing prevention policies, institutional reforms, and legal liability assessments.
Recent methods have been developed to map single-cell lineage statistics to population growth. Because population growth selects for exponentially rare phenotypes, these methods inherently depend on sampling large deviations from finite data, which introduces systematic errors. A comprehensive understanding of these errors in the context of finite data remains elusive. To address this gap, we study the error in growth rate estimates across different models. We show that under the usual bias-variance decomposition, the bias can be decomposed into a finite-time bias and nonlinear averaging bias. We demonstrate that finite-time bias, which dominates at short times, can be mitigated by fitting its monotonic behavior. In contrast, at longer times, nonlinear averaging bias becomes the predominant source of error, leading to a phase transition. This transition can be understood through the Random Energy Model, a mean-field model of disordered systems, where a few lineages dominate the estimator. Applying these methods to experimental data demonstrates that correcting for biases in lineage-based approaches yields consistent results for the long-term growth rate across multiple methods and enables the reverse-engineering of dynamic models. This new framework provides a quantitative understanding of growth rate estimators, clarifies the conditions under which they can be effectively applied to finite data, and introduces model-free approaches for studying the connections between physiology and cell growth.
Acute kidney injury (AKI) is a complex disease driven by the dynamic coupling of multiple pathological mechanisms. Current treatments remain primarily supportive, making it difficult to achieve precise regulation at key pathological junctures. In recent years, cell-derived nanomaterials (CdNMs) have demonstrated unique advantages in renal-targeted delivery, biocompatibility, and multi-mechanism synergistic regulation, due to their retention of the native biological properties of the source cells. This offers a novel technical pathway to overcome the limitations of traditional nanodelivery systems in AKI treatment. This review systematically summarises the construction strategies and application progress of CdNMs - including exosomes, cell membrane-coated nanoparticles (CM-NPs), and engineered extracellular vesicles (eEVs) - with a focus on core AKI pathological processes. It further discusses their potential applications in multimodal synergistic therapy and integrated diagnosis-treatment approaches. More importantly, this review proposes a research paradigm in which the design principles of CdNMs are systematically coupled with AKI's pathological dynamics, therapeutic timing, and efficacy evaluation dimensions. This paradigm, driven by a reverse-engineering strategy from pathological mechanisms to material construction and functional assessment, provides methodological references for achieving precise intervention, predictable efficacy evaluation, and optimised clinical translation pathways for AKI.
Internal states such as motivation and task engagement influence cognitive functions. Working memory, which maintains information over time, is an essential component of cognition and is modulated by motivation. Here, we show motivational states modulated attractor dynamics that supported working memory. Combining population recordings from mouse medial prefrontal cortex (mPFC) with data-constrained recurrent neural network (RNN) modeling, we found task engagement selectively modulated attractor dynamics within a memory-maintenance subspace, while stimulus-evoked responses remained intact. Reverse-engineering the RNNs revealed that task engagement reorganized the dynamical landscape by stabilizing memory-specific attractors. Specifically, task engagement modulated interactions between neurons to change the attractor dynamics. Finally, gradual changes in behavioral engagement were predicted by continuous modulation of attractor geometry in RNNs and mPFC. Together, these results suggest that internal state modulate working memory function by controlling the dynamical regime of mPFC circuits, providing a mechanistic link between internal state, neural dynamics, and cognitive function.
Biochar from biomass residues has emerged as a sustainable alternative to conventional nanocarbon materials for electrochemical biosensors. Due to its low cost and adjustable physicochemical properties, significant progress has been made in developing biochar-based electrochemical sensors. However, the lack of a systematic mechanistic understanding of how interconnected biochar properties influence electrochemical performance limits rational electrode design. This study evaluates the correlation between physicochemical properties of different biomass feedstocks and their electrochemical performance, using quantitative structure-performance relationships to provide an in-depth mechanistic understanding. It was observed that electrochemical performance is governed by the collective interplay of multiple biochar physicochemical properties. With a reverse-engineering approach, it is demonstrated that biochars derived from Grass biomass feedstocks achieve superior electrochemical performance by balancing properties from moderate height particle irregularity (Ra = 0.79-3.31 nm), controlled microporosity (< 25.78%), sufficient hydrophilicity (low C/O < 4.19, moderate inorganic content 4.29-6.84%), and good carbon crystallinity (high sp2 > 10.76% and low d002 < 3.80 Å). This enables smooth-to-moderate interfacial electrode surface roughness for efficient electrode-electrolyte contact and rapid electron-transfer pathways. Therefore, this understanding shows that intentional feedstock selection based on grass classification can serve as a controllable strategy to engineer the electrode-electrolyte interface for advanced biosensing.
Reconstructing editable CAD models from mesh data remains a fundamental yet challenging problem in reverse engineering. Existing methods often struggle to achieve both geometric accuracy and topological consistency, especially when recovering analytic design intent from complex meshes. We present an interactive CAD reverse modeling framework based on variational curve approximation, which bridges mesh segmentation and parametric reconstruction in a unified manner. Our method extracts surface-aligned cutting curves from segmented regions and refines them through a variational approximation process that encodes primitive attributes and geometric constraints. This enables the automatic recovery of closed loops, constraint relations, and operation parameters, leading to a compact and topologically consistent CAD representation. In addition, a coplanar profile detection and voxel-similarity-based Boolean inference are developed to restore the topological order of the original modeling sequence. Extensive experiments demonstrate that our approach achieves higher geometric fidelity, better structural consistency, and improved automation compared with state-of-the-art techniques. The reconstructed models are watertight, topologically consistent, and readily applicable to downstream CAD design tasks.
Reconstruction of advanced vertical and combined alveolar ridge defects still remains a challenge in implant dentistry. Digital technologies and virtual planning may potentially improve the predictability of guided bone regeneration (GBR). This study aimed to evaluate a fully digital, reverse-planning workflow for vertical ridge augmentation using membrane-cutting guides. This retrospective case series included 15 surgical sites presenting with vertical or combined alveolar ridge defects. A digital workflow integrating cone-beam computed tomography (CBCT), intraoral scanning, and virtual prosthetic planning was used to simulate ideal implant positions and corresponding hard tissue augmentation. Membrane-cutting guides were designed and fabricated using additive manufacturing to shape dense polytetrafluoroethylene membranes. Vertical GBR was performed using a split-thickness flap design and a tent-pole approach. Linear and volumetric hard tissue changes were assessed by comparing baseline and 9-month postoperative CBCT scans. Significant vertical bone gain was observed at all measurement points (p = 0.007), with mean increases from 15.70 mm ± 4.34 mm to 19.96 mm ± 3.83 mm at the central site. The mean volumetric hard tissue gain was 755.33 mm3 ± 411.22 mm3, closely matching the planned volume (757.50 mm3 ± 417.78 mm³), with no significant difference (p = 0.649). Using Spearman's correlation, a strong positive correlation was found between planned and achieved volumes (Spearman's ρ = 0.825, p = 0.0004). The mean augmentation efficacy was 20.13 ± 15.21 mm3/mm. The proposed 3D-driven reverse-planning workflow enabled predictable vertical ridge augmentation with high agreement between planned and achieved outcomes. This approach represents a feasible and accessible alternative to fully customized systems; however, further prospective controlled studies are required to validate these findings.
Integrating the model scanning (MS) and intraoral scanning (IOS) techniques, a new digital technique, MS+IOS, was proposed. To evaluate the accuracy among the MS, IOS, and MS+IOS techniques in vivo. Three techniques were used to obtain full-arch digital maxillomandibular relationships in maximum intercuspal position (MIP) on 15 healthy participants: (1) IOS group: digital dental casts were automatically registered by using an intraoral scanner; (2) MS group: stone casts were digitized and registered by using a laboratory scanner; (3) MS+IOS group: digital dental casts from the MS group were registered with buccal occlusal records captured through the IOS group in reverse engineering software. Trueness was assessed using multiple outcomes, with deviation value of the posterior occlusal surfaces serving as the primary outcome. Additionally, the occlusal clearance value was quantified for each group to support the outcome measures. Precision was evaluated in the MS and MS+IOS groups. The posterior region of the IOS group exhibited significantly greater deviations (-203.80 [-246.90, -146.20] μm) compared with the MS+IOS group (-110.04 [-221.58, -51.80] μm, P=.047) in the occlusal direction; the MS group deviated 3.35(-42.05, 185.98) μm toward the gingival direction. Corresponding to occlusal clearance, the IOS group had the highest average negative value (-128.20 [-199.90, -104.60] μm, P<.025); the MS group had the highest average positive value (544.00 [503.60, 571.70] μm, P<.025). In canine region, the IOS group showed significantly lowest distance (138.26 [83.52, 198.24] μm, P<.025) and angular deviations (1.04 [0.51, 1.35]°, P<.025). Precision was 12.2 ± 17.4 μm in the MS group, whereas it was 48.9 ± 29.2 μm in the MS+IOS group. The full-arch maxillomandibular relationship obtained by the MS+IOS technique achieved the best trueness in the posterior region, less occlusal penetration, and clinically acceptable precision. IOS showed the best trueness in the canine region. The MS technique exhibited a trend of occlusal separation. Clinical trial number: ChiCTR2400084020. Date of registration: 2024-05-09.
Recent advances in mitochondrial network dynamic and signalling highlight mitochondria as key therapeutic targets across diverse diseases. Yet, high drug development failure rates reflect an incomplete understanding of upstream molecular regulators of mitochondrial fate. Here, we address this gap by reverse engineering of the BH3-only protein BNIP3. Structural modelling and sequence-function analyses of its N-terminus identify a critical functional domain and amino acid hotspots that directly activate BCL-2 executioner proteins, triggering mitochondrial cell death. Leveraging these insights, we develop a BNIP3 antagonist peptide (B-017) that disrupts interactions between BNIP3 and BCL-2 executioner proteins, preserving mitochondrial integrity. B-017 demonstrates target specificity, a favourable safety profile, and robust suppression of cell death signalling in human cells. In clinically relevant animal models, it reduces tissue damage in the heart, brain, and liver. Together, these findings position B-017 as a promising therapeutic candidate targeting mitochondrial dysfunction.
Copper-containing nitrite reductases (CuNiRs) catalyse the reduction of nitrite to nitric oxide and are a key enzyme in the anaerobic ammonium oxidation and denitrification steps of the nitrogen cycle. The recent recognition of the widespread distribution of three-domain CuNiRs where cognate redox partners are fused to the core NiR enzyme offered the possibility of studying coordinated events (e.g. proton-coupled electron transfer) in a conformationally stable donor-acceptor complex. The C-terminal cytochrome c tethered domain of the CuNiR from Ralstonia pickettii (RpNiR) has been well studied. Reverse engineering of RpNiR undertaken to remove the cognate partner domain showed that the presence of the additional domain resulted in significant differences in the apparent Km for nitrite and the reduction potentials of the Cu centres when compared with the core enzyme. The oxidation state of the haem centre and the position of the tethering linker have also been shown to control access of substrate to the active site. A key feature of this control is a conserved tyrosine residue (Tyr323 in RpNiR) located in the tethering linker between the fused domain and the core enzyme. To gain insight into this control, we have undertaken targeted mutations of RpNiR to probe the so-called primary proton channel and perturb putative electron transfer routes from the haem to the `gatekeeper' Tyr323 and to the T1Cu centre. The resolution of our crystallographic data to better than 1.2 Å enabled us to apply unrestrained SHELXL refinement of the structures. Our data provide a significant advance in our understanding of catalysis and modulation of electron transfer in these tethered systems, with wider implications for these fundamental processes in other protein complexes.