Biology subjects

Bednar, D.

Publications and source records attributed to Bednar, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Structural mechanism governing radiationless energy transfer in Renilla bioluminescence

The nonradiative transport of electronic excitation from one chromophore to another, known as resonance energy transfer, lies at the root of photochemical processes in biology. Unlike photosynthesis, bioluminescence converts chemical energy into light through an enzymatic oxygenation of an energy-rich luciferin. In glowing cnidarians, the energy is relocated from an excited oxyluciferin to a fluorescent protein, shifting the colour and enhancing the quantum yield of a photogenic reaction. How protein-chromophore complexes assemble during this interplay in real space, and what this association entails for function, are unknown. Here, we report co-crystal structures of a 120-kilodalton energy-transfer complex from the luminescent soft coral Renilla reniformis. We find a heterotetrameric 2:2 assembly composed of two coelenteramide-loaded luciferases (RrLuc) docked at opposite sides of a head-to-tail dimer of green fluorescent protein (RrGFP). The edge-to-edge distance between donor and acceptor chromophores is below 3 nm, favouring the Forster-type radiationless energy transfer. Furthermore, RrGFP serves not only as a colour-switchable antenna and luminescence amplifier but also tunes the efficiency of luciferase catalysis by controlling its inherent dynamics. Our results provide detailed spatial information about intermolecular dipole-dipole coupling in Renilla bioluminescence, including the arrangement of donor-acceptor pairs that secure excited-state energy transfer with exquisite precision.

biochemistry↗

bioRxiv2026

ProtXAI: Explainable AI Reveals Structural Determinants of Protein Dynamics

Molecular dynamics simulations provide atomistic views of protein motions, but conventional analyses often struggle with extracting subtle mechanistic insights from complex trajectories. Here, we present an integrated framework, ProtXAI, combining molecular dynamics and explainable artificial intelligence (XAI), to identify residue-level determinants of conformational change across diverse protein systems. By leveraging inter-residue distance dynamics, deep learning, and sequential relevance propagation, the approach captures both local fluctuations and long-range communication pathways within protein structures. We applied this framework to three mechanistically distinct systems: apolipoprotein E4 (ApoE4), staphylokinase (SAK) variants, and an ancestral luciferase. Across these applications, our XAI-based approach recovered experimentally supported dynamic hotspots: ligand-responsive hinges in ApoE4, mutation-dependent flexibility shifts in SAK, and evolutionary redistribution of motions in the luciferase. ProtXAI also revealed additional long-range couplings not accessible to classical analysis. Together, these findings demonstrate that combining molecular dynamics with XAI provides a general and scalable strategy for dissecting protein dynamics and uncovering structural determinants of function, stability, and evolutionary changes without prior bias. This approach thus advances the current methodological repertoire for analysing proteins and their intrinsic properties. HighlightsMolecular dynamics simulations are increasingly accessible, yet scalable tools for comparative analysis remain limited. We demonstrate that machine learning coupled with explainable AI can automatically extract structural determinants of protein dynamics from trajectories. ProtXAI identifies key dynamic regions across diverse scenarios, including comparison of protein variants, understanding ligand modulation, and single-trajectory analysis. ProtXAI enables scalable, unbiased interpretation of long trajectories, providing an alternative to manual, time-intensive analysis.

bioinformatics↗

bioRxiv2026

Uncovering Functional Distant Mutations by Ultra-High-Throughput Screening of Dehalogenases

Conformational dynamics play a central role in enzyme function by controlling substrate access and productive binding. Yet mutations that beneficially modulate these properties are difficult to identify. Here, we used ultrahigh-throughput fluorescence-activated droplet sorting (FADS) with a bulky fluorogenic substrate derived from coumarin (COU-3) to impose steric selection pressure on the haloalkane dehalogenase LinB. Screening a focused library yielded five single substitutions located 11.5-15.5 [A] from the catalytic centre. Variant I138N showed a fourfold increase in catalytic efficiency toward COU-3 through reduced KM and increased kcat, associated with increased cap-domain flexibility and facilitated substrate entry. In contrast, variant P208S markedly reduced substrate inhibition and shifted specificity toward bulkier iodinated haloalkanes by reshaping its tunnel environment. Integrated kinetic and structural analyses revealed that screening with bulky substrates directs selection toward distal regions controlling substrate access and unproductive binding. These findings demonstrate that ultrahigh-throughput FADS can reveal dynamic mechanisms of enzyme adaptation that remain difficult to predict by rational design. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/713925v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@782038org.highwire.dtl.DTLVardef@8b43f3org.highwire.dtl.DTLVardef@11a403eorg.highwire.dtl.DTLVardef@6fcaea_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

Investigating the Conformational Flexibility of Staphylokinase Across Multiple Time Scales

Cardiovascular diseases, including ischemic stroke, necessitate improved thrombolytic agents. A microbe-encoded plasminogen activator staphylokinase (SAK) is a promising alternative to the widely used tissue plasminogen activator (tPA) due to its high fibrin specificity and low production cost. To overcome potential immunogenicity hampering its use in clinical settings, the low-immunogenic variants SAK SY155 and SAK THR174 were previously engineered. However, the molecular basis underlying their reduced immunogenicity is not understood and requires detailed elucidation. Here, we determine molecular structures and compare flexibility between low-immunogenic and immunogenic SAK variants, using a combination of experimental and computational structural techniques. Our analyses show that all variants share the canonical SAK fold and retain similar plasminogen activation kinetics, despite the number of introduced substitutions. Crucially, the low-immunogenic variants exhibit distinct flexibility profiles, with SAK THR174 showing substantially increased flexibility in the H1 helix and B3 region. SAK SY155 exhibits an increased flexibility in the H1-B3 loop and propensity to homodimerize. These flexibility changes are found in the known immunogenic epitopes. Our multi-scale flexibility analysis provides the molecular explanation for the reduced immunogenicity, altered thermostability, and retained fibrinolytic function of the engineered variants. This information is critical for the design of next-generation thrombolytics.

biochemistry↗

bioRxiv2026

Biochemical and Immunological Properties of Engineered Low-Immunogenic Staphylokinases for Next-Generation Thrombolytic Therapy

Staphylokinase (SAK) is a highly fibrin-specific plasminogen activator with significant potential as a safe and affordable thrombolytic. Yet, its clinical translation can be limited by potential immunogenicity. To accelerate the development of improved thrombolytics, a critical step is identifying the most suitable molecular template. Therefore, we performed a comparative analysis of biochemical and immunological properties of three engineered low-immunogenic variants (SAK SY155, SAK THR174, and SAK STAR FRIDA) and two wild-types (SAK STAR and SAK 42D), using a newly established panel of assays. All variants retained potent thrombolytic activity, with SAK SY155 displaying the highest catalytic efficiency and fibrin-clot permeability. However, this advantage did not fully translate into improved clot reduction under flow conditions. Comprehensive immunological profiling, including T lymphocyte proliferation, dendritic cell maturation, mouse immunization models, and human serum reactivity tests, confirmed decreased immunogenicity for two low-immunogenic variants. Overall, low-immunogenic SAK SY155 emerged as the most promising template for rational engineering of next-generation thrombolytics.

biochemistry↗

bioRxiv2025

Molecular trick to reverse SN2 mechanism in hydrolytic enzyme

Hydrolytic haloalkane dehalogenase enzymes catalyze an SN2 nucleophilic substitution to erase halogen substituents in organohalogen compounds. The acid-base-nucleophile triad secures irreversible SN2 displacement of the halogen for the hydroxyl derived from the water. Catalysis relies on the protonatable imidazole ring of the histidine base, and its substitution with an asparagine traps the enzyme in a covalently bound intermediate state, a principle exploited in the widely used HaloTag technology. In contrast, the histidine-to-phenylalanine substitution triggers reversibility of the SN2 mechanism, but the molecular trick by which it reprograms the catalytic pathway remains unknown. Here, we show that the phenylalanine at the site of the histidine base spatially disturbs the adjacent residues, leading to the remodeling of surrounding active-site loops. Consequently, rerouting of the access tunnels imparts distinctive kinetic behavior, featuring a reversible SN2 chemical step that facilitates transhalogenation reactions. This information is crucial for engineering next-generation biocatalysts for sustainable chemistry. HighlightsO_LICatalytic triad (glutamate-histidine-aspartate) secures irreversible SN2 mechanism C_LIO_LIHistidine-to-phenylalanine substitution in the triad leads to a reversible SN2 reaction C_LIO_LIEnzyme active site rerouting is a hallmark of the catalytic reprogramming C_LIO_LIBasis for designing biocatalysts for sustainable transhalogenation chemistry C_LI The bigger pictureHaloalkane dehalogenases catalyze the hydrolytic cleavage of the carbon-halogen bond in halogenated hydrocarbons, a feature used in a wide variety of industrial and biotechnological processes. In HaloTag technology, the catalytic histidine is replaced by an asparagine to enable a stable covalent bonding between a probe and a protein for biological imaging, affinity purification, etc. Remarkably, this originally irreversible SN2 process becomes fully reversible in a histidine-to-phenylalanine mutant. Moreover, halogen ion product is released at this stage, offering a greener alternative for transhalogenation reactions compared to the conventional synthetic approach and a way to recycle environmentally harmful halogenated compounds. However, the optimization of enzymes is needed for applications on an industrial scale. Therefore, we investigated the kinetics of the SN2 step and structural features of these two enzyme mutants, providing a basis for subsequent optimization efforts.

bioengineering↗

bioRxiv2025

Structural insights into the evolution of ABH-fold luciferases

The alpha/beta-hydrolase (ABH) superfamily is a widespread and functionally versatile protein fold recognized for its ability to adapt to diverse molecular functions across all three domains of life. One such spectacular example of evolutionary adaptation at the ABH-fold represents an acquisition of oxygenolytic luciferase reaction that occurred within the hydrolytic haloalkane dehalogenase family. The molecular details of this evolution remain puzzling. In this work, we determine crystal structures and explore dynamical behavior of a bifunctional ancestral ABH-fold enzyme, highlighting molecular features associated with the transition from hydrolytic to oxygenolytic catalysis at this fold. Structures showed a canonical {beta}-sandwich shielded with a helical cap domain. The catalytic pocket is voluminous enough to accommodate a bulky substrate. Molecular docking showed productive binding of the coelenterazine substrate, as well as the product coelenteramide. Molecular dynamics simulations demonstrated that the coelenterazine entry does not present a major energetic barrier and identified a preferred binding orientation important for oxygenolytic catalysis. Comparisons between ancestral and extant enzymes highlighted specific amino acids and sequence motifs characteristic for oxygenolytic luciferases. Collectively, our results provide an expanded view of the evolutionary transition in which ABH-fold enzymes, originally using water to cleave chemical bonds, adapted to utilize dioxygen for bioluminescence.

evolutionary biology↗

bioRxiv2025

Taurine Inhibits Apolipoprotein E4 Aggregation

Apolipoprotein E4 (ApoE4) is a major genetic risk factor in many neurodegenerative diseases, yet effective therapeutic strategies targeting its associated pathologies remain unresolved. The aggregation of ApoE4, a key pathological feature, can be attenuated by tramiprosate and its metabolite 3-sulfopropanoic acid. In this study, we investigated the potential of taurine, a close chemical analogue of tramiprosate, to modulate ApoE4-mediated pathological processes. Using an integrated approach--including molecular dynamics simulations, static light scattering, mass spectrometry, and cerebral organoid models--we investigated taurines effects on ApoE4 aggregation. We found that taurine effectively inhibits ApoE4 aggregation. Notably, taurine significantly ameliorates the pathophysiological characteristics of ApoE4, bringing its phenotype closer to the more benign ApoE3 variant. By leveraging its neuroprotective properties, taurine may offer effects comparable to tramiprosate and 3-sulfopropanoic acid, positioning it as an accessible and promising candidate for mitigating neurodegeneration, particularly in individuals with the high-risk ApoE4/E4 genotype.

biochemistry↗

bioRxiv2024

Analysis of Mutations in Precision Oncology using The Automated, Accurate, and User-Friendly Web Tool PredictONCO

Next-generation sequencing technology has created many new opportunities for clinical diagnostics, but it faces the challenge of functional annotation of identified mutations. Various algorithms have been developed to predict the impact of missense variants that influence oncogenic drivers. However, computational pipelines that handle biological data must integrate multiple software tools, which can add complexity and hinder non-specialist users from accessing the pipeline. Here, we have developed an online user-friendly web server tool PredictONCO that is fully automated and has a low barrier to access. The tool models the structure of the mutant protein in the first step. Next, it calculates the protein stability change, pocket level information, evolutionary conservation, and changes in ionisation of catalytic amino acid residues, and uses them as the features in the machine-learning predictor. The XGBoost-based predictor was validated on an independent subset of held-out data, demonstrating areas under the receiver operating characteristic curve (ROC) of 0.95 and 0.94, and the average precision from the precision-recall curve 0.98 and 0.94 for structure-based and sequence-based predictions, respectively. Finally, PredictONCO calculates the docking results of small molecules approved by regulatory authorities. We demonstrate the applicability of the tool by presenting its usage for variants in two cancer-associated proteins, cellular tumour antigen p53 and fibroblast growth factor receptor FGFR1. Our free web tool will assist with the interpretation of data from next-generation sequencing and navigate treatment strategies in clinical oncology: https://loschmidt.chemi.muni.cz/predictonco/.

bioinformatics↗

bioRxiv2024

A Computational Workflow for Analysis of Missense Mutations in Precision Oncology

Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individuals tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutations effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving the balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/.

cancer biology↗

bioRxiv2024

Anticipating Protein Evolution with Successor Sequence Predictor

The quest to predict and understand protein evolution has been hindered by limitations on both the theoretical and the experimental fronts. Most existing theoretical models of evolution are descriptive, rather than predictive, leaving the final modifications in the hands of researchers. Existing experimental techniques to help probe the evolutionary sequence space of proteins, such as directed evolution, are resource-intensive and require specialised skills. We present the Successor Sequence Predictor (SSP) as an innovative solution. Successor Sequence Predictor is an in silico method that mimics laboratory-based protein evolution by reconstructing a proteins evolutionary history and suggesting future amino acid substitutions based on trends observed in that history through carefully selected physicochemical descriptors. This approach enhances specialised proteins by predicting mutations that improve desired properties, such as thermostability, activity, and solubility. Successor Sequence Predictor can thus be used as a general protein engineering tool to develop practically useful proteins. The code of the Successor Sequence Predictor is provided, and the design of mutations will be also possible via an easy-to-use web server https://loschmidt.chemi.muni.cz/fireprotasr/. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/598054v1_ufig1.gif" ALT="Figure 1"> View larger version (70K): org.highwire.dtl.DTLVardef@c0cc9forg.highwire.dtl.DTLVardef@1ba19aforg.highwire.dtl.DTLVardef@ec341forg.highwire.dtl.DTLVardef@15205a6_HPS_FORMAT_FIGEXP M_FIG C_FIG Tracing Evolutions Pathway to the Future

bioinformatics↗

bioRxiv2024

Prediction of Aggregation Prone Regions in Proteins Using Deep Neural Networks and Their Suppression by Computational Design

Identification of aggregation-prone regions in proteins and their suppression through mutations is a powerful strategy to enhance protein solubility and yield, significantly expanding their application potential. Here, we developed a deep neural network-based predictor AggreProt, that generates a residue-level aggregation profile for protein sequences. The model outperformed or matched current state-of-the-art algorithms, as validated on two independent datasets comprising hexapeptides and full-length proteins with annotated aggregation-prone regions. We further validated the model experimentally using a set of 34 hexapeptides identified in the model protein haloalkane dehalogenase LinB, along with seven proteins from the AmyPro database. Experimental results agreed with our predictions in 79% of cases and also revealed inaccuracies in some database annotations. Finally, the algorithms utility was demonstrated by identifying aggregation-prone regions in the LinB enzyme and designing mutations to suppress aggregation in its exposed regions. The resulting variants exhibited reduced aggregation propensity, improved solubility, and up to a 100% increase in yield compared to the wild type. AggreProt is freely available to the scientific community via a user- friendly web server: https://loschmidt.chemi.muni.cz/aggreprot.

bioinformatics↗

bioRxiv2023

Large-scale Annotation of Biochemically Relevant Pockets and Tunnels in Cognate Enzyme-Ligand Complexes

Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. A thorough validation using eight distinct molecular systems revealed that CaverDock analysis of ligand un/binding is on par with time-consuming molecular dynamics simulations, but much faster. The optimized and validated pipeline was applied to annotate more than 17,000 cognate enzyme-ligand complexes. Analysis of ligand un/binding energetics indicates that the top priority tunnel has the most favourable energies in 75 % of cases. Moreover, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks only in 50 % of cases. Our study provides essential information for the interpretation of results from tunnel calculation and energy profiling in mechanistic enzymology and protein engineering. We formulated several simple rules allowing identification of biochemically relevant tunnels based on the binding pockets, tunnel geometry, and ligand transport energy profiles.

bioinformatics↗

bioRxiv2023

Effects of Alzheimer's Disease Drug Candidates on Disordered Aβ42 Dissected by Comparative Markov State Analysis (CoVAMPnet)

Computational study of the effect of drug candidates on intrinsically disordered biomolecules is challenging due to their vast and complex conformational space. Here we developed a Comparative Markov State Analysis (CoVAMPnet) framework to quantify changes in the conformational distribution and dynamics of a disordered biomolecule in the presence and absence of small organic drug candidate molecules. First, molecular dynamics trajectories are generated using enhanced sampling, in the presence and absence of small molecule drug candidates, and ensembles of soft Markov state models (MSMs) are learned for each system using unsupervised machine learning. Second, these ensembles of learned MSMs are aligned across different systems based on a solution to an optimal transport problem. Third, the directional importance of inter-residue distances for the assignment to different conformational states is assessed by a discriminative analysis of aggregated neural network gradients. This final step provides interpretability and biophysical context to the learned MSMs. We applied this novel computational framework to assess the effects of ongoing phase 3 therapeutics tramiprosate (TMP) and its metabolite 3-sulfopropanoic acid (SPA) on the disordered A{beta}42 peptide involved in Alzheimers disease. Based on adaptive sampling molecular dynamics and CoVAMPnet analysis, we observed that both TMP and SPA preserved more structured conformations of A{beta}42 by interacting non-specifically with charged residues. SPA impacted A{beta}42 more than TMP, protecting -helices and suppressing the formation of aggregation-prone {beta}-strands. Experimental biophysical analyses showed only mild effects of TMP/SPA on A{beta}42, and activity enhancement by the endogenous metabolization of TMP into SPA. Our data suggest that TMP/SPA may also target other biomolecules than A{beta} peptides. The CoVAMPnet method is broadly applicable to study the effects of drug candidates on the conformational behavior of intrinsically disordered biomolecules. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/523007v2_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@13eea16org.highwire.dtl.DTLVardef@17a6bd1org.highwire.dtl.DTLVardef@3c6b33org.highwire.dtl.DTLVardef@a20444_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Illuminating the mechanism and allosteric behavior of NanoLuc luciferase

NanoLuc, a superior {beta}-barrel fold luciferase, was engineered 10 years ago but the nature of its catalysis remains puzzling. Here experimental and computational techniques were combined, revealing that imidazopyrazinone luciferins bind to an intra-barrel catalytic site but also to an allosteric site shaped on the enzyme surface. Structurally, binding to the allosteric site prevents simultaneous binding to the catalytic site, and vice versa, through concerted conformational changes. We demonstrate that restructuration of the allosteric site can boost the luminescent reaction in the remote active site. Mechanistically, an intra-barrel arginine coordinates the imidazopyrazinone component of luciferin, which reacts with O2 via a radical charge-transfer mechanism, and then it also protonates the resulting excited amide product to form a light-emitting neutral species. Concomitantly, an aspartate, supported by two tyrosines, fine-tunes the blue color emitter to secure a high emission intensity. This information is critical to engineering the next-generation of ultrasensitive bioluminescent reporters.

biochemistry↗

bioRxiv2022

Domino-like Effect of C112R Mutation on APOE4 Aggregation and Its Suppression by Alzheimer's Disease Drug Candidate

BackgroundApolipoprotein E (ApoE) {varepsilon}4 genotype is the most prevalent risk factor for late-onset Alzheimers Disease (AD). Although ApoE4 differs from its non-pathological ApoE3 isoform only by the C112R mutation, the molecular mechanism of its proteinopathy is unknown. MethodsHere, we reveal the molecular mechanism of ApoE4 aggregation using a combination of experimental and computational techniques, including X-ray crystallography, site-directed mutagenesis, hydrogen-deuterium mass spectrometry (HDX-MS), static light scattering and molecular dynamics simulations. Treatment of ApoE {varepsilon}3/{varepsilon}3 and {varepsilon}4/{varepsilon}4 cerebral organoids with tramiprosate was used to compare the effect of tramiprosate on ApoE4 aggregation at the cellular level. ResultsWe found that C112R substitution in ApoE4 induces long-distance (>15 [A]) conformational changes leading to the formation of a V-shaped dimeric unit that is geometrically different and more aggregation-prone than the ApoE3 structure. AD drug candidate tramiprosate and its metabolite 3-sulfopropanoic acid induce ApoE3-like conformational behavior in ApoE4 and reduce its aggregation propensity. Analysis of ApoE {varepsilon}4/{varepsilon}4 cerebral organoids treated with tramiprosate revealed its effect on cholesteryl esters, the storage products of excess cholesterol. ConclusionsOur results connect the ApoE4 structure with its aggregation propensity, providing a new druggable target for neurodegeneration and ageing.

neuroscience↗

bioRxiv2022

A non-conventional archaeal fluorinase identified by in silico mining catalyzes the fastest natural nucleophilic fluorination

Fluorinases, the only enzymes known to catalyze the transfer of fluorine to an organic molecule, are essential biocatalysts for the sustainable synthesis of valuable organofluorines. However, the few fluorinases identified so far have low turnover rates that hamper biotechnological applications. Here, we isolated and characterized putative fluorinases retrieved from systematic in silico mining and identified a non-conventional archaeal enzyme from Methanosaeta sp. that mediates the highest fluorination rate reported to date. Furthermore, we demonstrate enhanced production of fluoronucleotides in vivo in a bacterial host engineered with this archaeal fluorinase, paving the way towards synthetic metabolism for efficient biohalogenations. Fastest biological SN2 fluorination

synthetic biology↗

bioRxiv2022

A catalytic mechanism for Renilla-type bioluminescence

The widely used coelenterazine-powered Renilla luciferase was discovered over 40 years ago but the oxidative mechanism by which it generates blue photons remains unclear. Here we decipher Renilla-type bioluminescence through crystallographic, spectroscopic, and computational experiments. Structures of ancestral and extant luciferases complexed with the substrate-like analogue azacoelenterazine or a reaction product were obtained, providing unprecedented snapshots of coelenterazine-to-coelenteramide oxidation. Bound coelenterazine adopts a Y-shaped conformation, enabling the deprotonated imidazopyrazinone component to attack O2 via a radical charge-transfer mechanism. A high emission intensity is secured by an aspartate from a conserved proton-relay system, which protonates the excited coelenteramide product. Another aspartate on the rim of the catalytic pocket fine-tunes the electronic state of coelenteramide and promotes the formation of the blue light-emitting phenolate anion. The results obtained also reveal structural features distinguishing flash-type from glow-type bioluminescence, providing insights that will guide the engineering of next-generation luciferase-luciferin pairs for ultrasensitive optical bioassays.

biochemistry↗