Biology subjects

Prokop, Z.

Publications and source records attributed to Prokop, Z..

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

CascadeMAP: Autonomous Closed-loop Optimization of Enzyme Cascades via Microfluidics, Machine Learning and Agentic AI

Enzyme cascades enable complex biochemical transformations, but their optimization is resource-intensive, requiring navigation through high-dimensional parameter spaces encompassing reaction conditions, enzyme ratios, and buffer composition. Here we introduce CascadeMAP, an autonomous microfluidic platform for closed-loop optimization of enzyme cascades, integrating high-throughput microfluidics with Bayesian optimization and multi-agent AI system. We demonstrate the platform across two cascades: (i) a glycerol detection pathway monitored by fluorescence and (ii) a 1,2,3-trichloropropane degradation pathway monitored by label-free Raman spectroscopy providing orthogonal detection modalities. Bayesian optimization identified optimal conditions three times faster than Design of Experiments. Multi-agent AI system automated hypothesis generation, processing 11 GB of experimental data, pattern recognition, and insight synthesis. Operating without human intervention for 7 days, CascadeMAP processed [~]220,000 reactions across [~]7,400 different conditions. This capability establishes a generalizable framework for the autonomous optimization of enzyme cascades and metabolic pathways and accelerates the development of biocatalytic and synthetic biological systems.

biochemistry↗

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

Fibrin Selective Alteplase with Improved Thrombolysis and Inhibition Resistance Engineered by Rational Design

Thrombolytic enzymes represent an important class of proteolytic biocatalysts for medical applications, yet currently used FDA-approved variants, including alteplase and tenecteplase, remain limited by suboptimal catalytic efficiency, off-target activity, and susceptibility to inhibition. These limitations reflect the complexity of enzyme function in physiological environments, where therapeutic performance depends on the simultaneous optimization of multiple catalytic and biophysical properties. Here, we introduce a multi-objective enzyme engineering strategy for the design of next-generation thrombolytic proteases, explicitly targeting multiple properties required for therapeutic performance. Our approach combines computer-aided design, evolutionary reconstruction, and literature-guided mutation selection to improve catalytic activity, fibrin selectivity, inhibition resistance, and functional lifetime within a single workflow. This framework is coupled with systematic biochemical characterization, in vitro evaluation of clot penetration and fibrinolytic activity, and in vivo validation of efficacy and safety. By addressing multiple performance parameters simultaneously, this strategy enables efficient navigation of trade-offs that typically limit enzyme optimization. Using this approach, we identify Brnoteplase as a lead variant with enhanced fibrin selectivity, improved resistance to inhibition, and superior clot penetration, resulting in increased effective catalytic lifetime and enabling bolus administration. In vivo studies demonstrate enhanced thrombolysis and recanalization with reduced hemorrhagic complications. These findings provide a broadly applicable framework for designing proteolytic biocatalysts suitable for complex biological environments.

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

SoluProtMut: Siamese Deep Learning for Predicting Solubility Effects of Protein Mutations with Experimental Validation

Protein solubility is an attractive engineering target because it is a critical property influencing the scalability of protein production and the success of therapeutic proteins in biomedical applications. However, predicting solubility changes upon mutation in silico is challenging due to data heterogeneity and protein bias. Here, we explore how different sources of solubility data can be used for machine learning and present SoluProtMut, a Siamese deep geometric neural network trained to predict the impact of mutations on protein solubility. Our final model was trained exclusively on deep mutational scanning data. We compare our model with five established solubility prediction methods. The model achieves state-of-the-art performance on an independent dataset of various proteins, especially in predicting the effects of multipoint mutations (informedness of 26.5 %). Our findings also reaffirm that the scarcity of solubility data continues to hamper progress in this field. To address this limitation, we experimentally quantified solubility changes for hundreds of single-point and multipoint mutants of haloalkane dehalogenase. Complemented with recent deep-mutational-scanning data on myoglobin, we employed both these data for external validation. Although the generalization to unseen proteins remains limited, our findings demonstrate the potential of integrating high-throughput assays with deep learning to improve the accuracy and scope of solubility prediction. Highlights- We present a novel anti-symmetric Siamese architecture for mutational prediction on structures built on graph-based convolutional neural networks ensuring SE(3) invariance. - We demonstrate that a model trained only on single-point mutants of a single protein derived from high-throughput experiments generalizes to multipoint mutants of unseen proteins, achieving the state-of-the-art binary prediction informedness of 26.5 %. - We address the key limitation in the domain by extending the available data with 277 single-point and multipoint mutants of haloalkane dehalogenase labelled in-house and a selection of recently published 1037 single-point mutants of myoglobin. - We systematically study how different data subsets affect the performance of the trained models, revealing that including yeast-derived high-throughput data in training hampers generalization to low-throughput assays but recovers the performance on the yeast-derived myoglobin dataset.

bioinformatics↗

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

Kinetic mechanism of Renilla luciferase guides induced-fit engineering for improved bioluminescence

Luciferases are widely used bioluminescent reporters, yet the molecular determinants of their catalytic efficiency and light-emission stability remain incompletely understood. Here, we reconstruct the complete catalytic pathway of Renilla luciferase by combining steady-state, transient, and temperature-dependent kinetics with crystallography and molecular simulations. We show that the enzyme is substantially undersaturated with oxygen (Km,O2 = 719 M), causing its true turnover number (kcat = 21.9 s-1) to be systematically underestimated. Concurrently, elevated oxygen drives irreversible enzyme inactivation after ~1,500 turnovers, revealing a fundamental trade-off that limits oxygen-affinity engineering. Instead, the genuine bottleneck of the catalytic cycle is the induced-fit conformational opening of the product-bound enzyme. Selective engineering of this transition step through rational loop grafting yielded a variant AncFT-L14 with enhanced catalytic efficiency and glow-type bioluminescence with substantially slower signal decay in cell lysates. Collectively, our results identify conformational dynamics as a primary tunable determinant of luciferase function. More broadly, this work establishes a mechanistically grounded framework for the development of next-generation bioluminescent tools and for engineering enzymes controlled by dynamically gated ligand exchange.

biochemistry↗

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↗

bioRxiv2025

Mechanism-Guided Engineering of Fluorinase Unlocks EfficientNucleophilic Biofluorination

The fluorinase enzyme, the only known biocatalyst forming stable carbon-fluorine bonds, operates with extremely low efficiency, catalyzing one reaction every 2-12 minutes. This severely limits its utility for sustainable biofluorination, and its sluggish activity remains poorly understood. We suppressed its aggregation through directed mutagenesis and elucidated the kinetic mechanism using a novel mathematical framework that fits complex kinetic and oligomerization data. This analysis revealed that >80% of enzyme molecules are inactive under standard conditions due to two dead-end pathways. The designed W50F+A279R mutant preferentially formed hexamers and displayed enhanced catalytic efficiency in this oligomeric state. When coupled with mechanism-based optimization of the reaction medium, including enzymatic removal of the inhibitory product, the catalytic turnover rate reached 12.5 {+/-} 2.1 min-{superscript 1}, representing [~]60-fold increase compared with previously reported turnover rates of the wild-type enzyme. Our work provides a mechanistic blueprint for fluorinase enhancement and a generalizable mathematical framework for analyzing kinetics of multimeric enzymes.

biochemistry↗

bioRxiv2024

Functional characterization of luciferase in a brittle star indicates parallel evolution influenced by genomic availability of haloalkane dehalogenase

Determining why convergent traits use distinct versus shared genetic components is crucial for understanding how evolutionary processes generate and sustain biodiversity. However, the factors dictating the genetic underpinnings of convergent traits remain incompletely understood. Here, we use heterologous protein expression, biochemical assays, and phylogenetic analyses to confirm the origin of a luciferase gene from haloalkane dehalogenases in the brittle star Amphiura filiformis. Through database searches and gene tree analyses, we also show a complex pattern of presence and absence of haloalkane dehalogenases across organismal genomes. These results first confirm parallel evolution across a vast phylogenetic distance, because octocorals like Renilla also use luciferase derived from haloalkane dehalogenases. This parallel evolution is surprising, even though previously hypothesized, because many organisms that also use coelenterazine as the bioluminescence substrate evolved completely distinct luciferases. The inability to detect haloalkane dehalogenases in the genomes of several bioluminescent groups suggests that the distribution of this gene family influences its recruitment as a luciferase. Together, our findings highlight how biochemical function and genomic availability help determine whether distinct or shared genetic components are used during the convergent evolution of traits like bioluminescence.

evolutionary biology↗

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

SAP domain facilitates efficient loading of Ku onto DNA ends

Recognition and processing of DNA ends play a central role in maintaining genome integrity. The evolutionarily conserved DNA repair complex Ku serves as the primary sensor of free DNA ends in eukaryotic cells. Its rapid association with DNA ends is crucial for several cellular processes, including non-homologous end joining (NHEJ) DNA repair and telomere protection. In this study, we conducted a transient kinetic analysis to investigate the impact of the SAP domain on individual phases of the Ku-DNA interaction. Specifically, we examined the initial binding, the subsequent docking of Ku onto DNA, and the sliding of Ku along DNA. Our findings revealed that the C-terminal domain of Ku70, known as SAP ((SAF-A/B, Acinus and PIAS), facilitates the initial phases of Ku-DNA interaction, but does not affect the sliding process. This suggests that SAP may either establish the first interactions with DNA, or stabilize these initial interactions during loading. To assess the biological role of SAP, we generated Arabidopsis plants expressing Ku lacking the SAP domain ({Delta}SAP). Intriguingly, despite the decreased efficiency of the {Delta}SAP Ku complex in loading onto DNA, the mutant plants exhibited full proficiency in classical NHEJ and telomere maintenance. This indicates that the speed of Ku loading onto telomeres or DNA double-strand breaks (DSBs) is not the decisive factor in stabilizing these DNA structures.

molecular biology↗

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 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↗