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Our Mission<\/h2>\n

We educate leaders for Science, Medicine and Business!<\/em><\/p>\n

We are Loschmidt Laboratories based at the Masaryk University<\/a> and the International Clinical Research Center<\/a> of St. Anne\u2019s University Hospital Brno. Our laboratories are located at the University Campus Bohunice in Brno, Czech Republic.<\/p>\n

Proteins are natural building blocks of all living organisms and participate in virtually every process within cells. Many natural proteins are enzymes that catalyse biochemical reactions and are essential for metabolism. Proteins can be found everywhere \u2014 they play a role in the development of medicines, in food and beverages processing, animal nutrition, textiles, household cleaning, fuels, and energy generation. And therefore the study of proteins is valuable for the whole society.<\/p>\n

We conduct interdisciplinary research in the field of protein engineering. We wish to understand the structure-function relationships of proteins and improve their functionalities for biotechnologies. We study the mechanisms of Alzheimer\u2019s disease and develop novel drugs for acute stroke. Our goal is to be recognized as one of the leading protein engineering groups in Europe. We will consistently strive to publish our findings in reputable scientific journals, develop software tools and lab-on-chips, and transfer results to practice.<\/p>\n

The key components of our daily activities include collaboration between experimentalists and theoreticians, solidarity among the laboratory team, and mentoring of young colleagues \u2014 all while maintaining a friendly and creative working environment to honor the name of Jan Josef Loschmidt<\/a>.<\/p>\n

Profile Publications<\/h2>\n

Molecular Trick to Reverse SN<\/sub>2 Step in a Haloalkane Dehalogenase<\/h2>\n

Toul, M., Marques, S. M., Gao, T., Bernhardova, H., Vavra, O., Novakova, V., Damborsky, J., Bednar, D., Prokop, Z., Marek, M., 2026, Chem Catalysis<\/em> xx: 101687.<\/p>\n

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\nHaloalkane dehalogenases catalyze an SN<\/sub>2 nucleophilic substitution to erase halogen substituents in organohalogen compounds. The acid-base-nucleophile triad secures irreversible SN<\/sub>2 displacement of the halogen for the hydroxyl derived from the water. In this report, we elucidate the molecular basis for how this originally irreversible SN<\/sub>2 reaction becomes fully reversible in a histidine-to-phenylalanine mutant, favoring transhalogenation chemistry. This offers a greener alternative for industrial transhalogenation processes, applicable in the recycling of environmentally harmful halogenated compounds.<\/p>\n

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Read more<\/a><\/p>\n

FireProtDB 2.0: Large-Scale Manually Curated Database of the Protein Stability Data<\/h2>\n

Musil, M., Borko, S., Planas-Iglesias, J., Lacko, D., Rosinska, M., Kabourek, P., O Martins, L., Tataruch, M., Damborsky, J., Mazurenko, S., Bednar, D., 2026, Nucleic Acids Research<\/em> 54: D409-D418.<\/p>\n

\"\"<\/center>
<\/center>FireProtDB 2.0 significantly advances protein engineering by expanding its dataset from 16,000 to over 5.5 million experiments. This update introduces a scalable, FAIR-compliant architecture that now supports complex mutations, de novo<\/i> designs, and diverse measurement types. By providing high-quality, large-scale data, the platform empowers the global research community to develop more reliable thermostable proteins for biotechnology and medicine.<\/p>\n

Read more<\/a><\/p>\n

Machine Learning Applied to Biocatalysis Research<\/h2>\n

Buller, R., Mazurenko, S., Yang, Y., 2025, Nature Communications<\/em> 16: 1-5.<\/p>\n

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<\/center>How is machine learning reshaping biocatalysis? Experts, Prof. Rebecca Buller, Dr. Stanislav Mazurenko, and Associate Professor Yang Yang, spoke about the opportunities and hurdles of integrating machine learning into enzyme research. This interview explores strategies for interdisciplinary collaboration and the future trajectory of the field.<\/p>\n

Read more<\/a><\/p>\n

Quantum Computing for Faster Enzyme Discovery and Engineering<\/h2>\n

Damborsky, J., Kouba, P., Sivic, J., Vasina, M., Bednar, D., Mazurenko, S., 2025, Nature Catalysis<\/em> 8: 872-880.<\/p>\n

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<\/center>Quantum computing harnesses quantum mechanics to tackle problems beyond the reach of classical algorithms, with potential to revolutionize biocatalysis by enabling accurate simulation of biomolecular systems and enzyme-catalyzed reactions. Although current hardware is limited by noise and error rates, advances in quantum algorithms could drive breakthroughs in catalyst design, enzyme discovery, ancestral sequence reconstruction, and in silico protein evolution, ultimately transforming both fundamental enzymology and industrial bioprocessing.<\/p>\n

Read more<\/a><\/p>\n

Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins<\/h2>\n

Buller, R., Damborsky, J., Hilvert, D., Bornscheuer, U., 2025, Angewandte Chemie International Edition<\/em> 64: e202421686.<\/p>\n

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<\/center>The 2024 Nobel Prize in Chemistry was awarded to David Baker for computational protein design and to Demis Hassabis and John Jumper for developing AlphaFold, a machine-learning-based protein structure prediction tool. These advancements have revolutionised protein research and engineering, enabling applications in medicine, diagnostics, sustainable chemistry, and drug discovery (e.g., modelling therapeutic targets, designing peptide binders, and identifying small molecule ligands). The text further discusses the impact on organic synthesis and future directions in protein engineering, medicinal chemistry, and material design.<\/p>\n

Read more<\/a><\/p>\n

Engineering Dehalogenase Enzymes Using Variational Autoencoder-Generated Latent Spaces and Microfluidics<\/h2>\n

Kohout, P., Vasina, M., Majerova, M., Novakova, V., Damborsky, J., Bednar, D., Marek, M., Prokop, Z., Mazurenko, S., 2025, JACS Au<\/em> 5: 838-850.<\/p>\n

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<\/center>Here, we present a new machine-learning method based on variational autoencoders and an evolutionary sampling strategy to engineer model enzymes, specifically haloalkane dehalogenases. We generated variants with novel sequences, and three design-build-test cycles improved their solubility from 11% to 75%. Thorough experimental validation, including the use of the microfluidic device Micro-PEX, resulted in 20 multiple-point variants. The most stable variant demonstrated a 3.5-fold increase in activity compared to the template.<\/p>\n

Read more<\/a><\/p>\n

Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SN<\/sub>2 Step<\/h2>\n

Gelfand, N., Orel, V., Cui, W., Damborsky, J., Li, C., Prokop, Z., Xie, W. J., Warshel, A., 2025, Journal of the American Chemical Society<\/em> 147: 2747-2755.<\/p>\n

\"\"<\/center>Here, we demonstrated that a generative maximum-entropy model can enhance the catalytic activity of designed protein variants. We gained molecular insights into these improvements through simulations and kinetic measurements, achieving a faster SN<\/sub>2 reaction rate than the wild-type enzyme, despite haloalkane dehalogenase being extensively optimized by natural evolution.<\/p>\n

Read more<\/a><\/p>\n

Azobenzene-Based Photoswitchable Substrates for Advanced Mechanistic Studies of Model Haloalkane Dehalogenase Enzyme Family<\/h2>\n

Slanska, M., Stackova, L., Marques, S. M., Stacko, P., Martinek, M., Jilek, L., Toul, M., Damborsky, J., Bednar, D., Klan, P., Prokop, Z., 2024, ACS Catalysis<\/em> 14: 11635-11645.<\/p>\n

\"\"<\/center>Here, we have provided a systematic approach to engineer photoswitchable substrates for enzymes, exemplified by the design of an azobenzene-based probe for haloalkane dehalogenases. This novel substrate enables detailed mechanistic studies combining pre-steady-state kinetics and time-resolved femtosecond crystallography, providing a foundation for rational enzyme engineering and optimization.<\/p>\n

Read more<\/a><\/p>\n

AggreProt: A Web Server for Predicting and Engineering Aggregation Prone Regions in Proteins<\/h2>\n

Planas-Iglesias, J., Borko, S., Swiatkowski, J., Elias, M., Havlasek, M., Salamon, O., Grakova, E., Kunka, A., Martinovic, T., Damborsky, J., Martinovic, J., Bednar, D., 2024, Nucleic Acids Research<\/em> gkae420: 1-11.<\/p>\n

\"\"<\/center>The AggreProt web server is a user-friendly interface to our recently developed predictor targeting protein aggregation. The predictor consists of an ensemble of deep neural networks that were trained on a collection of amyloidogenic hexapeptides WaltzDB. Amyloid aggregation is characteristic of important neurodegenerative human diseases, such as Alzheimer\u2019s or Parkinson\u2019s. We demonstrated the ability of our predictor to detect aggregation-prone regions in non-amyloid aggregation on the haloalkane dehalogenase and luciferase. AggreProt web server requires minimal input and presents comprehensive results: https:\/\/loschmidt.chemi.muni.cz\/aggreprot\/<\/a>.<\/p>\n

Read more<\/a><\/p>\n

CoVAMPnet: Comparative Markov State Analysis for Studying Effects of Drug Candidates on Disordered Biomolecules<\/h2>\n

Marques, S. M., Kouba, P., Legrand, A., Sedlar, J., Disson, L., Planas-Iglesias, J., Sanusi, Z., Kunka, A., Damborsky, J., Pajdla, T., Prokop, Z., Mazurenko, S., Sivic, J., Bednar, D., 2024, JACS Au<\/em> 4: 2228-2245.<\/p>\n

\"\"<\/center>Studying drug effects on intrinsically disordered biomolecules is complex due to their vast conformational space. Here we describe a new framework, CoVAMPnet, which quantifies changes in conformational distribution and dynamics using molecular dynamics trajectories, Markov state models, and unsupervised machine learning. We applied it to Alzheimer’s therapeutics tramiprosate (TMP) and its metabolite 3-sulfopropanoic acid (SPA) and found that both compounds preserve structured conformations of A\u03b242, with SPA being more effective. Our data suggest that TMP\/SPA may also target other biomolecules than A\u03b2 peptides.<\/p>\n

Read more<\/a><\/p>\n

Illuminating the Mechanism and Allosteric Behavior of NanoLuc Luciferase<\/h2>\n

Nemergut, M., Pluskal, D., Horackova, J., Sustrova, T., Tulis, J., Barta, T., Baatallah, R., Gagnot, G., Novakova, V., Majerova, M., Sedlackova, K., Marques, S. M., Toul, M., Damborsky, J., Prokop, Z., Bednar, D., Janin, Y. L., Marek, M., 2023, Nature Communications <\/em>14: 7864.<\/p>\n

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NanoLuc, a superior \u03b2-barrel fold luciferase, was engineered 10 years ago but the nature of its catalysis remains puzzling. In this work we reveal 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<\/i>, through concerted conformational changes. We demonstrate that restructuration of the allosteric site can boost the luminescent reaction in the remote active site. This information is critical to engineering the next-generation of ultrasensitive bioluminescent reporters.<\/p>\n

Read more<\/a><\/p>\n

Advancing Enzyme\u2019s Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning<\/h2>\n

Kunka, A., Marques, S. M., Havlasek, M., Vasina, M., Velatova, N., Cengelova, L., Kovar, D., Damborsky, J., Marek, M., Bednar, D., Prokop, Z., 2023, ACS Catalysis <\/em>13: 12506-12518.<\/p>\n

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Here, we compare different strategies for stabilizing enzymes, focusing on the hyperstable haloalkane dehalogenase DhaA115. Our results show that the automated protocols FireProt and PROSS outperformed rational design efforts in identifying multiple-point stabilizing mutations. The study also shows that manual curation of predicted mutations and machine learning algorithms can further improve stability and catalytic efficiency. The work resulted in three highly thermostable haloalkane dehalogenases with improved catalytic efficiencies.<\/p>\n

Read more<\/a><\/p>\n

In-depth Analysis of Biocatalysts by Microfluidics: An Emerging Source of Data for Machine Learning<\/h2>\n

Vasina, M., Kovar, D., Damborsky, J., Ding, Y., Yang, T., deMello, A., Mazurenko, S., Stavrakis, S., Prokop, Z., 2023, Biotechnology Advances<\/em> 66: 108171.<\/p>\n

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In this review, we present the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and their readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.<\/p>\n

Read more<\/a><\/p>\n

Identification, Characterization, and Engineering of Glycosylation in Thrombolytics<\/h2>\n

Toul, M., Slonkova, V., Mican, J., Urminsky, A., Tomkova, M., Sedlak, E., Bednar, D., Damborsky, J., Hernychova, L., Prokop, Z., 2023, Biotechnology Advances<\/em> 66: 108174.<\/p>\n

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Here we summarize current knowledge on computational and experimental identification of glycosylation sites and glycan identity, together with methods used for their reengineering. Practical examples from previous studies focus on modifying glycosylations in thrombolytics, e.g., alteplase, tenecteplase, reteplase, urokinase, saruplase, and desmoteplase. Collected clinical data on these glycoproteins demonstrate the great potential of the glycosylation engineering strategy for the developing a new generation of truly efficient and life-saving thrombolytic drugs.<\/p>\n

Read more<\/a><\/p>\n

Catalytic Mechanism for Renilla<\/em>-type Luciferases<\/h2>\n

Schenkmayerova, A., Toul, M., Pluskal, D., Baatallah, R., Gagnot, G., Pinto, G. P., Santana, V. T., Stuchla, M., Neugebauer, P., Chaiyen, P., Damborsky, J., Bednar, D., Janin, Y. L., Prokop, Z., Marek, M., 2023, Nature Catalysis <\/em>6: 23-38.<\/p>\n

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Here we decipher Renilla-type catalysis 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 molecular snapshots of coelenterazine-to-coelenteramide oxidation. The results obtained also reveal structural features distinguishing flash-type from glow-type bioluminescence, providing insights that will guide the engineering of luciferases for diverse bioluminescent technologies.<\/span><\/p>\n

Read more<\/a><\/p>\n

Advanced Database Mining of Efficient Biocatalysts by Sequence and Structure Bioinformatics and Microfluidics<\/h2>\n

Vasina, M., Vanacek, P., Hon, J., Kovar, D., Faldynova, H., Kunka, A., Buryska, T., Badenhorst, C.P.S., Mazurenko, S., Bednar, D., Stavrakis, S., Bornscheuer, U.T., deMello, A., Damborsky, J., Prokop, Z., 2022, Chem Catalysis<\/em> 2: 1-22.<\/p>\n

\"\"<\/center><\/p>\n

We present a pipeline integrating sequence and structural bioinformatics with microfluidic enzymology to discover efficient and robust haloalkane dehalogenases. The bioinformatic part identified 2,905 putative dehalogenases and prioritized a \u201csmall-but-smart\u201d set of 45 genes, yielding 40 active enzymes, 24 of which were biochemically characterized by microfluidic enzymology techniques. The obtained biocatalysts outperform the previously known wild-type and engineered dehalogenases. This strategy is applicable to other enzyme families, paving the way towards accelerating the identification of novel biocatalysts for industrial applications.<\/p>\n

Read more<\/a><\/p>\n

Mechanism-Guided Tunnel Engineering to Increase the Efficiency of a Flavin-Dependent Halogenase<\/h2>\n

Prakinee, K., Phintha, A., Visitsatthawong, S., Lawan, N., Sucharitakul, J., Kantiwiriyawanitch, C., Damborsky, J., Chitnumsub, P., van P\u00e9e, K.-H., Chaiyen, P., 2022, Nature Catalysis<\/em> 5: 534-544.<\/p>\n

\"\"<\/center><\/p>\n

Although flavin-dependent halogenases are attractive for C\u2013H bond activation, their applications are limited due to low turnover and stability. Here we employed a mechanism-guided semi-rational approach to engineer the intermediate transfer tunnel connecting two active sites of tryptophan 6-halogenase Thal. This Thal-V82I variant generates less hypohalous acid leakage and possesses multiple catalytic improvements such as faster halogenation, broader substrate utilization, and greater thermostability and pH tolerance compared with the wild-type Thal.<\/p>\n

Read more<\/a><\/p>\n

CalFitter 2.0: Leveraging the Power of Singular Value Decomposition to Analyse Protein Thermostability <\/h2>\n

Kunka, A., Lacko, D., Stourac, J., Damborsky, J., Prokop, Z., Mazurenko, S., 2022, Nucleic Acid Research<\/em> gkac249: 1-9. <\/p>\n

\"\"<\/center>The quantitative description of protein unfolding and aggregation is crucial for rational protein design and unravelling the molecular basis of protein misfolding diseases. To assist in the analysis of data from protein denaturation experiments, we have released CalFitter 2.0, which newly incorporates singular value decomposition. Various protein spectra can now be analyzed globally, dramatically reducing the risk of human bias and providing valuable insights into spectral fingerprints of unfolding intermediates. CalFitter 2.0 is the only freely available tool that combines singular value decomposition of unfolding kinetic and temperature scanning experiments with global data analysis. The tool is freely available at <\/span>https:\/\/loschmidt.chemi.muni.cz\/calfitter<\/a>.<\/span>  <\/p>\n

Read more<\/a><\/p>\n

Mechanism-Based Strategy for Optimizing HaloTag Protein Labeling<\/h2>\n

Marques, S. M., Slanska, M., Chmelova, K., Chaloupkova, R., Marek, M., Clark, S., Damborsky, J., Kool, E. T., Bednar, D., Prokop, Z., 2022, JACS Au <\/i>2: 1324\u22121337.<\/p>\n

\"\"<\/center><\/p>\n

HaloTag labelling technology introduced unrivalled potential in protein chemistry and biology and it quickly became the dominant tagging technology. Surprisingly, despite the huge number of diverse applications and different available ligands, a single tagging enzyme is repeatedly used without considering the selection of other enzyme variants that are more suitable for a specific ligand. Our results show that natural dehalogenases provide high efficiency of the various ligand incorporation with no need for intensive optimization using directed evolution. Based on these findings, we propose a simple strategy for designing efficient HaloTag reactions, which consist of finding the optimal enzyme tag for specific ligand from a wide range of natural dehalogenases using easy-to-use in silico tools.   <\/p>\n

Read more<\/a><\/p>\n

LoopGrafter: a Web Tool for Transplanting Dynamical Loops for Protein Engineering<\/h2>\n

Planas-Iglesias, J., Opaleny, F., Ulbrich, P., Stourac, J., Sanusi, Z., Pinto, G. P., Schenkmaye- rova, A., Byska, J., Damborsky, J., Kozlikova, B., Bednar, D., 2022, Nucleic Acid Research<\/em> gkac249: 1-9.<\/p>\n

\"\"<\/center>The transplantation or grafting of particularly dynamic loop structures from a source protein to a target one has shown to be a powerful approach to improve the properties of enzymes, and yet no tool is available to assist scientists in approaching this task. LoopGrafter is designed to fill in this gap by accepting as input 3D coordinates of two structurally-related source and target proteins and guiding the user in the process of identifying similar loops among the proteins and assessing their dynamics. Upon selection of the loops to be grafted, all possibilities for recombination points are explored, three-dimensional models built, and their goodness assessed by MODELLER and Rosetta Fast Relax. LoopGrafter is available at https:\/\/loschmidt.chemi.muni.cz\/loopgrafter\/<\/a>.<\/p>\n

Read more<\/a><\/p>\n

Extended Mechanism of the Plasminogen Activator Staphylokinase Revealed by Global Kinetic Analysis: 1000-fold Higher Catalytic Activity than That of Clinically Used Alteplase<\/h2>\n

Toul, M., Nikitin, D., Marek, M., Damborsky, J., Prokop, Z., 2022, ACS Catalysis<\/em> 12: 3807-3814.<\/p>\n

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\n\"\"<\/a><\/center><\/p>\n

We probed the complex kinetic mechanism of staphylokinase, an attractive thrombolytic drug candidate that is active in a multiprotein form with complex regulation. Using a modern global numerical approach, we found that the net value of its catalytic efficiency is 10,000-fold higher than previously determined by conventional methods limited by simplifying assumptions and approximations. We uncover the main catalytic bottleneck whose elimination might provide orders of magnitude more active alternative to the clinically used thrombolytic drug alteplase. Our findings pave the way for the development of a new generation of drugs effectively treating cardiovascular diseases. <\/p>\n

Read more<\/a><\/p>\n

Computational Enzyme Stabilization Can Affect Folding Energy Landscapes and Lead to Catalytically Enhanced Domain-Swapped Dimers<\/h2>\n

Markova, K., Kunka, A., Chmelova, K., Havlasek, M., Babkova, P., Marques, S. M., Vasina, M., Planas-Iglesias, J., Chaloupkova, R., Bednar, D., Prokop, Z., Damborsky, J., Marek, M., 2021, ACS Catalysis <\/em>11: 12864-12885.<\/p>\n

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Computer-encoded algorithms are increasingly employed to stabilize native proteins for research, biotechnologies, and biomedical applications. Methods such as FireProt<\/a> are available to the research community as web services, making computer-aided design of multiple mutants extremely easy and widely accessible. This study reports important findings related to potential changes in the folding pathway and catalytic properties of designed hyperstable mutants. Structural analysis revealed that stabilizing mutations may activate cryptic hinge-loop regions and establish new secondary interfaces, leading to the formation of so-called domain-swapped dimer.<\/p>\n

Read more<\/a><\/p>\n

Engineering the Protein Dynamics of an Ancestral Luciferase<\/h2>\n

Schenkmayerova, A., Pinto, G. P., Toul, M., Marek, M., Hernychova, L., Planas-Iglesias, J., Daniel Liskova, V., Pluskal, D., Vasina, M., Emond, S., D\u00f6rr, M., Chaloupkova, R., Bednar, D., Prokop, Z., Hollfelder, F., Bornscheuer, U. T., Damborsky, J., 2021, Nature Communications<\/em> 12: 3616.<\/p>\n

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We track the role of dynamics in evolution, starting from the evolvable and thermostable ancestral protein AncHLD-RLuc<\/sup> which catalyses both dehalogenase and luciferase reactions. Insertion-deletion backbone mutagenesis of AncHLD-RLuc<\/sup> challenged the scaffold dynamics. Screening for both activities reveals mutations localized in three distinct regions that lead to altered protein dynamics. Transplantation of a highly dynamic fragment leads to highly stable glow-type bioluminescence. The success of our approach suggests that a strategy comprising (i) constructing a stable and evolvable template, (ii) mapping functional regions by backbone mutagenesis, and (iii) transplantation of dynamic features, can lead to functionally innovative proteins.<\/p>\n

Read more<\/a><\/p>\n

Exploring Mechanism of Enzyme Catalysis by On-Chip Transient Kinetics Coupled with Global Data Analysis and Molecular Modeling<\/h2>\n

Hess, D., Dockalova, V., Kokkonen, P., Bednar, D., Damborsky, J., deMello, A., Prokop, Z., Stavrakis, S., 2021, Chem<\/em> 7: 1-14.<\/p>\n

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We develop a droplet-based microfluidic platform for high-throughput acquisition of transient kinetic data over a range of substrate concentrations and temperatures. To demonstrate their utility, we measure the transient kinetics of three model enzymes, namely, b-galactosidase, horseradish peroxidase, and microperoxidase. Additionally, we conduct a complex kinetic and thermodynamic study of engineered variants of haloalkane dehalogenases. Datasets are globally analyzed and complemented by molecular dynamics simulations, providing new insights into the molecular basis of substrate specificity and the role of hydration-related entropy.<\/p>\n

Read more<\/a><\/p>\n

Decoding the Intricate Network of Molecular Interactions of a Hyperstable Engineered Biocatalyst<\/h2>\n

Markova, K., Chmelova, K., Marques, S. M., Carpentier, P., Bednar, D., Damborsky, J., Marek, M., 2020, Chemical Science<\/em> 11: 11162\u201311178.<\/p>\n

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We report X-ray structures of a hyperstable engineered haloalkane dehalogenase (T<\/em>m<\/sub> = 73.5 \u00b0C), which highlight key thermostabilization effects. Unexpectedly, mutations toward bulky aromatic amino acids at the protein surface triggered long-distance (\u223c27 \u00c5) backbone changes due to cooperative effects. These cooperative interactions produced an unprecedented double-lock system that dramatically reduced the volumes of enzyme access tunnels.<\/p>\n

Read more<\/a>  \u2022  Cover graphics<\/a><\/p>\n

FireProtASR<\/sup>: A Web Server for Fully Automated Ancestral Sequence Reconstruction<\/h2>\n

Musil, M., Khan, R. T., Beier, A., Stourac, J., Konegger, H., Damborsky, J., Bednar, D., 2020, Briefings in Bioinformatics <\/em>bbaa337:<\/em> 1-11.<\/p>\n

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Ancestral sequence reconstruction is an engineering strategy applicable to the improvement of protein thermal stability and other characteristics. The FireProt-ASR provides users with one-stop-shop solution for ancestral sequence reconstruction. It fully automatizes all the steps: (i) search for the biologically relevant homolog sequences, (ii) reduction of the initial dataset, (iii) construction of the multiple-sequence alignment and the rooted phylogenetic tree, and (iv) reconstruction of the ancestral sequences. We employ a novel algorithm for the ancestral gaps reconstruction. FireProt-ASR provides easy-to-use user interface: http:\/\/loschmidt.chemi.muni.cz\/fireprotasr\/<\/a>.<\/p>\n

Read more<\/a><\/p>\n

FireProtDB<\/sup>: Database of Manually Curated Protein Stability Data<\/h2>\n

Stourac, J., Dubrava, J., Musil, M., Horackova, J., Damborsky, J., Mazurenko, S., Bednar, D., 2020, Nucleic Acids Research<\/em> 49: D319-D324.<\/p>\n

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The recent advances in machine learning facilitate the development of software tools for computational design of stable proteins. The accuracy of these tools strongly depends on the quality and amount of data used for training and testing. FireProtDB is a database of experimental thermostability data for single-point mutants. The database combines the published datasets, data extracted from the recent literature, and the data collected in our laboratory. Its user interface is designed to facilitate both types of the expected use: (i) the interactive explorations of individual entries and (ii) the construction of machine learning-friendly data sets: https:\/\/loschmidt.chemi.muni.cz\/fireprotdb<\/a>.<\/p>\n

Read more<\/a><\/p>\n

EnzymeMiner: Automated Mining of Soluble Enzymes with Diverse Structures, Catalytic Properties and Stabilities<\/h2>\n

Hon, J., Borko, S., Stourac J., Prokop, Z., Zendulka, J., Bednar D., Martinek, T., Damborsky, J., 2020, Nucleic Acids Research<\/em> 48: W104\u2013W109.<\/p>\n

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EnzymeMiner identifies putative members of enzyme families and facilitates the selection of promising targets for experiments. The server mines sequences that are likely to show the desired catalytic activity and prioritizes sequences heterologously expressible in a soluble form in Escherichia coli. EnzymeMiner reduces the time devoted to data gathering, multi-step analysis, sequence prioritization and selection from days to hours. EnzymeMiner is universally applicable to any enzyme family and provides an interactive and easy-to-use web interface freely available at https:\/\/loschmidt.chemi.muni.cz\/enzymeminer\/<\/a><\/p>\n

Read more<\/a><\/p>\n

Machine Learning in Enzyme Engineering<\/h2>\n

Mazurenko, S., Prokop., Z., Damborsky, J., 2019, ACS Catalysis<\/em> 10: 1210-1223.<\/p>\n

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We analyse the state of the art in databases and machine learning methods used for training and validating predictors in enzyme engineering in this Perspective. We discuss current limitations and challenges which the community is facing and recent advancements in experimental and theoretical methods that have the potential to address those challenges. We also present our view on possible future directions for developing the applications to the design of efficient biocatalysts.<\/p>\n

Read more<\/a><\/p>\n

Caver Web 1.0: Identification of Tunnels and Channels in Proteins and Analysis of Ligand Transport<\/h2>\n

Stourac, J., Vavra, O., Kokkonen, P., Filipovic, J., Pinto, G., Brezovsky, J., Damborsky, J., Bednar, D., 2019, Nucleic Acid Research<\/em> W1: W414\u2013W422.<\/p>\n

\"\"<\/a><\/center><\/p>\n

Caver Web 1.0 is a web server for comprehensive analysis of protein tunnels and channels, and study of the ligands\u2019 transport through these transport pathways. Caver Web is the first interactive tool allowing both the analyses within a single graphical user interface. The tool is very fast (2-20 min per job) and is applicable even for virtual screening purposes. Its simple setup and comprehensive graphical user interface make the tool accessible for a broad scientific community. The server is freely available at https:\/\/loschmidt.chemi.muni.cz\/caverweb<\/a>.<\/p>\n

Read more<\/a><\/p>\n

Light-Emitting Dehalogenases: Reconstruction of Multifunctional Biocatalysts<\/h2>\n

Chaloupkova, R., Liskova, V., Toul, M., Markova, K., Sebestova, E., Hernychova, L., Marek, M., Pinto. G. P., Pluskal, D., Waterman, J., Prokop, Z., Damborsky, J., 2019, ACS Catalysis<\/em> 9: 4810\u20134823.<\/p>\n

\"\"<\/a><\/p>\n

To obtain structural insights into the emergence of new biological functions from catalytically promiscuous enzymes, we reconstructed an ancestor of catalytically distinct, but evolutionarily related, haloalkane dehalogenases (EC 3.8.1.5) and Renilla<\/i> luciferase (EC 1.13.12.5). This ancestor has both hydrolase and monooxygenase activities. We demonstrate, that a<\/span> single substitution next to the catalytic pentad enables the emergence of new activity at enzyme class-level. Ancestral reconstruction has a clear potential for obtaining multi-functional catalysts.<\/span><\/p>\n

Read more<\/a><\/p>\n

Exploring the Challenges of Computational Enzyme Design by Rebuilding the Active Site of a Dehalogenase<\/h2>\n

Jindal, G., Slanska, K., Kolev, V., Damborsky, J., Prokop, Z., Warshel, A., 2019, Proceedings of the National Academy of Sciences of the United States of America<\/em> 116: 389\u2013394.<\/p>\n

\"\"<\/a><\/center><\/p>\n

The goal of rational computer-aided enzyme design is hampered by the lack of knowledge of the maximum possible rate enhancement. We address this problem by considering the enzyme DhlA, which is naturally adapted for the degradation of dihalogenated ethanes. Using empirical valence bond calculations, we determine the effect of finding mutations that reduce the catalysis and then introducing mutations that restore catalysis. One of our predicted cycles is confirmed experimentally, while the other attempt remains inconclusive. We believe that the proposed strategy provides a very powerful way of validating and refining approaches for computational enzyme design.<\/p>\n

Read more<\/a><\/p>\n

Molecular Gating of an Engineered Enzyme Captured in Real Time<\/h2>\n

Kokkonen, P., Sykora, J., Prokop, Z., Ghose, A., Bednar, D., Amaro, M., Beerens, K., Bidmanova, S., Slanska, M., Brezovsky, J., Damborsky, J., Hof, M., 2018, Journal of the American Chemical Society<\/em> 140: 17999\u201318008.<\/p>\n

\"\"<\/a><\/p>\n

Engineering dynamical molecular gates represents a widely applicable strategy for designing efficient biocatalysts. Here we analyzed the dynamics of a molecular gate artificially introduced into an access tunnel of the most efficient haloalkane dehalogenase using pre-steady-state kinetics, a single-molecule fluorescence spectroscopy and molecular dynamics. Photoinduced electron-transfer \u2013 fluorescence correlation spectroscopy (PET-FCS) has enabled real-time observation of molecular gating at single-molecule level with the rate constants (kon = 1822 s-1, koff = 60 s-1) corresponding well with those from the pre-steady-state kinetics (k-1 = 1100 s-1, k1 = 20 s-1).<\/p>\n

Read more<\/a><\/p>\n

Evolutionary Analysis is a Powerful Complement to Energy Calculations for Protein Stabilization<\/h2>\n

Beerens, K., Mazurenko, S., Kunka, A., Marques, S. M., Hansen, N., Musil, M., Chaloupkova, R., Waterman, J., Brezovsky, J., Bednar, D., Prokop, Z., Damborsky, J., 2018, ACS Catalysis<\/em> 8: 9420\u22129428.<\/p>\n

 <\/p>\n

\"\"<\/a><\/center><\/p>\n

Stability is one of the most important characteristics of proteins and the role of computational approaches in modifying protein stability is rapidly expanding. Here we present a detailed mechanistic study of stabilizing mutations derived from the phylogenetic analysis. We explain why these highly beneficial mutations can be easily missed by widely used force-field calculations. A hybrid approach to protein stabilization – combining both energy calculation and evolutionary analysis – is freely available to the broad scientific community via the web server application FireProt: https:\/\/loschmidt.chemi.muni.cz\/fireprot\/<\/a>.<\/p>\n

Read more<\/a><\/p>\n

CalFitter: A Web Server for Analysis of Protein Thermal Denaturation Data<\/h2>\n

Mazurenko, S., Stourac, J., Kunka, A., Nedejlkovic, S., Bednar, D., Prokop, Z., Damborsky, J., 2018, Nucleic Acids Research<\/em> 46: W344-W349.<\/p>\n

\"\"<\/center><\/p>\n

CalFitter web server is a unified platform for a comprehensive data fitting and an analysis of protein thermal denaturation data. The server allows simultaneous global data fitting using any combination of input data types and offers twelve protein unfolding pathway models to select from. The data fitting produces optimal parameters, their confidence intervals, and statistical information to define unfolding pathways. The server provides an interactive and easy-to-use interface that allows users to directly analyse input datasets: https:\/\/loschmidt.chemi.muni.cz\/calfitter\/<\/a>.<\/p>\n

Read more<\/a><\/p>\n

HotSpot Wizard 3.0: Web Server for Automated Design of Mutations and Smart Libraries Based on Sequence Input Information<\/h2>\n

Sumbalova, L., Stourac, J., Martinek, T., Bednar, D., Damborsky, J., 2018, Nucleic Acids Research<\/em> 46: W356-W362.<\/p>\n

\"\"<\/center><\/p>\n

HotSpot Wizard is a web server for an automatic identification of \u2018hot spots\u2019 for the engineering of substrate specificity, activity or enantioselectivity of enzymes. The version 3.0 accepts the protein sequence as input data. The protein structure for the query sequence is obtained either from eight repositories of homology models or is modelled using Modeller and I-Tasser. A new module for the estimation of thermodynamic stabilities using the Rosetta suite has also been introduced which prevents destabilising mutations: http:\/\/loschmidt.chemi.muni.cz\/hotspotwizard<\/a>.<\/p>\n

Read more<\/a><\/p>\n

Exploration of Enzyme Diversity by Integrating Bioinformatics with Expression Analysis and Biochemical Characterization<\/h2>\n

Vanacek, P., Sebestova, E., Babkova, P., Bidmanova, S., Daniel, L., Dvorak, P., Stepankova, V., Chaloupkova, R., Brezovsky, J., Prokop, Z., Damborsky, J., 2018, ACS Catalysis<\/em> 8: 2402\u20132412.<\/p>\n

\"\"<\/center><\/p>\n

Millions of protein sequences are being discovered at an incredible pace, representing an inexhaustible source of biocatalysts. We present an integrated system for automated in silico screening and systematic characterization of diverse family members. The workflow consists of: (i) identification and computational characterization of relevant genes by sequence\/structural bioinformatics, (ii) expression analysis and activity screening of selected proteins, and (iii) complete biochemical\/biophysical characterization.<\/p>\n

Read more<\/a><\/p>\n

FireProt: Web Server for Automated Design of Thermostable Proteins<\/h2>\n

Musil, M., Stourac, J., Bendl, J., Brezovsky, J., Prokop, Z., Zendulka, J., Martinek, T., Bednar, D., Damborsky, J., 2017, Nucleic Acids Research<\/em> 45: W393-W399.<\/p>\n

\"\"<\/center><\/p>\n

FireProt is a web server for the automated design of multiple-point thermostable mutant proteins that combines structural and evolutionary information in its calculation core. FireProt utilizes sixteen tools and three protein engineering strategies for making reliable protein designs. The server is complemented with interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostable mutants. FireProt is freely available at https:\/\/loschmidt.chemi.muni.cz\/fireprot<\/a>.<\/p>\n

Read more<\/a><\/p>\n

Different Structural Origins of the Enantioselectivity of Haloalkane Dehalogenases toward Linear \u03b2-Haloalkanes: Open\u2013Solvated versus Occluded\u2013Desolvated Active Sites<\/h2>\n

Liskova, V., Stepankova, V., Bednar, D., Brezovsky, J., Prokop, Z., Chaloupkova, R., Damborsky, J., 2017, Angewandte Chemie International Edition<\/em> 56: 4719-4723.<\/p>\n

\"\"<\/center><\/p>\n

The enzymatic enantiodiscrimination of linear \u03b2-haloalkanes is difficult because the simple structures of the substrates prevent directional interactions. Herein we describe two distinct molecular mechanisms for the enantiodiscrimination of the \u03b2-haloalkane 2-bromopentane by haloalkane dehalogenases. Highly enantioselective DbjA has an open, solvent-accessible active site, whereas the engineered enzyme DhaA31 has an occluded and less solvated cavity but shows similar enantioselectivity. The enantioselectivity of DhaA31 arises from steric hindrance imposed by two specific substitutions rather than hydration as in DbjA.<\/p>\n

Read more<\/a><\/p>\n

Enzyme Tunnels and Gates as Relevant Targets in Drug Design<\/h2>\n

Marques, S. M., Daniel, L., Buryska, T., Prokop, Z., Brezovsky, J., Damborsky, J., 2016, Medicinal Research Reviews<\/em> 37: 1095-1139.<\/p>\n

\"drug-design_tunnels-gates_small\"<\/a><\/center><\/p>\n

We described a set of general concepts relating to the structural properties, function, and classification of enzyme tunnels and gates. We highlighted the potential of enzyme tunnels and gates as targets for the binding of small molecules, the different types of their binding, and the potential pharmacological benefits. Twelve examples of ligands bound to the tunnels and\/or gates of clinically relevant enzymes were used to illustrate the different binding modes and to explain some new strategies for drug design.<\/p>\n

Read more<\/a><\/p>\n

Engineering a de Novo Transport Tunnel<\/h2>\n

Brezovsky, J., Babkova, P., Degtjarik, O., Fortova, A., Gora, A., Iermak, I., Rezacova, P., Dvorak, P., Kuta Smatanova, I., Prokop, Z., Chaloupkova, R., Damborsky, J., 2016, ACS Catalysis <\/em> 6: 7597-7610.<\/p>\n

\"de_novo_tunnel\"<\/a><\/p>\n

We described the computational design and directed evolution of a de novo transport tunnel in a haloalkane dehalogenase. Mutants with a blocked native tunnel and a newly opened auxiliary tunnel in a distinct part of the structure showed dramatically modified properties. The mutants with blocked tunnels acquired specificity never observed with native family members: up to 32 times increased substrate inhibition and 17 times reduced catalytic rates. Opening of the auxiliary tunnel resulted in specificity and substrate inhibition similar to those of the native enzyme and the most proficient haloalkane dehalogenase reported to date.<\/p>\n

Read more<\/a><\/p>\n

PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions<\/h2>\n

Bendl, J., Musil, M., Stourac, J., Zendulka, J., Damborsky, J., Brezovsky, J., 2016, PLOS Computational Biology <\/em> 12: e1004962.<\/p>\n

\"PredictSNP2:<\/a><\/p>\n

We have developed a web server PredictSNP2 providing easy access to binary predictions and uniform confidence values for the five best-performing prediction tools and their consensus. These predictions are supplemented with information gathered from eight publicly available databases. PredictSNP2 extends the scope of genome analysis to the level of nucleotide substitutions that enables to identify disease\u00ad-related variants within the whole genome.<\/p>\n

Read more<\/a><\/p>\n

HotSpot Wizard 2: Automated Design of Site-Specific Mutations and Smart Libraries in Protein Engineering<\/h2>\n

Bendl, J., Stourac, J., Sebestova, E., Vavra, O., Musil, M., Brezovsky, J., Damborsky, J., 2016, Nucleic Acids Research<\/em> 44: W479-W487.<\/p>\n

\"HotSpot<\/a><\/p>\n

We developed HotSpot Wizard 2.0, a web server for automated identification of hot spots and design of smart libraries for engineering proteins\u2019 stability, catalytic activity, substrate specificity and enantioselectivity. Compared to its predecessor, HotSpot Wizard 2.0 introduces several major improvements, extending the scope and quality of its analyses. It implements four different established protein engineering strategies, enabling the user to selectively target sites affecting the protein’s stability and catalytic properties. A new graphical interface provides an intuitive and comprehensive overview of the results of the analysis. The resulting pipeline of twenty integrated tools, including our in-house software Caver 3.0 for analysis of protein tunnels and channels, and three databases represents a unique one-stop solution that makes library design accessible even to users with no prior knowledge of bioinformatics.<\/p>\n

Read more<\/a><\/p>\n

FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants<\/h2>\n

Bednar, D., Beerens, K., Sebestova, E., Bendl, J., Khare, S., Chaloupkova, R., Prokop, Z., Brezovsk\u00fd, J., Baker, D., Damborsky, J., 2015, PLOS Computational Biology<\/em> 11: e1004556.<\/p>\n

\"FireProt:<\/p>\n

FireProt is a robust computational strategy for predicting highly stable multiple-point mutants that combines energy- and evolution-based approaches with smart filtering to identify additive stabilizing mutations. We demonstrate that thermostability of the model enzymes can be substantially increased (\u0394Tm<\/sub> = 24\u00b0C and 21\u00b0C) by constructing and characterizing only a handful of multiple-point mutants. FireProt\u2019s reliability and applicability was demonstrated by validating its predictions against 656 mutations from the ProTherm database. FireProt can be applied to any protein for which a tertiary structure and homologous sequences are available.<\/p>\n

Read more<\/a><\/p>\n

Site-specific Analysis of Protein Hydration Based on Unnatural Amino Acid Fluorescence<\/h2>\n

Amaro, M., Brezovsky, J., Kovacova, S., Sykora, J., Bednar, D., Nemec, V., Liskova, V., Kurumbang, N., Beerens, K., Chaloupkova, R., Paruch, K., Hof, M., Damborsky, J., 2015, Journal of the American Chemical Society<\/em> 137: 4988-4992.<\/p>\n

\"Site-specific<\/p>\n

Hydration of proteins profoundly affects their functions. We describe a simple and general method for a site-specific analysis of protein hydration based on the in vivo incorporation of fluorescent unnatural amino acids and their analysis by steady-state fluorescence spectroscopy. Using this method, we investigate the hydration of functionally important regions of dehalogenases DhaA and DbjA. The experimental results are compared to findings from molecular dynamics simulations. Given the ongoing development of unnatural amino acids technology, this method could potentially be used to analyze hydration at specific sites in a wide range of proteins.<\/p>\n

Read more<\/a><\/p>\n

Dynamics and Hydration Explain Failed Functional Transformation in Dehalogenase Design<\/h2>\n

Sykora, J., Brezovsky, J., Koudelakova, T., Lahoda, M., Fortova, A., Chernovets, T., Chaloupkova, R., Stepankova, Prokop, Z., Kuta Smatanova, I., Hof, M., Damborsky, J., 2014, Nature Chemical Biology<\/em> 10: 428-430.<\/p>\n

\"Dynamics<\/p>\n

We emphasize the importance of dynamics and hydration for enzymatic catalysis and protein design by transplanting the active site from a haloalkane dehalogenase with high enantioselectivity to nonselective dehalogenase. Protein crystallography confirms that the active site geometry of the redesigned dehalogenase matches that of the target, but its enantioselectivity remains low. Time-dependent fluorescence shifts and computer simulations revealed that dynamics and hydration at the tunnel mouth differ substantially between the redesigned and target dehalogenase.<\/p>\n

Read more<\/a><\/p>\n

PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations<\/h2>\n

Bendl J., Stourac J., Salanda O., Pavelka A., Wieben E.D., Zendulka J., Brezovsky J., Damborsky J., 2014, PLOS Computational Biology<\/em> 10: e1003440.<\/p>\n

\"PredictSNP:<\/p>\n

We have constructed three independent datasets by removing duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance and robustness. The web server and the datasets are freely available to the academic community at https:\/\/loschmidt.chemi.muni.cz\/predictsnp<\/a>.<\/p>\n

Read more<\/a><\/p>\n

Gates of Enzymes<\/h2>\n

Gora, A., Brezovsky, J., Damborsky, J., 2013, Chemical Reviews<\/em> 113: 5871\u20135923.<\/p>\n

\"The<\/a><\/p>\n

This review highlights the importance of gates in enzymes. The gates control substrate access to the active site and product release, restrict solvent access to specific protein regions, and synchronize processes occurring in distinct parts of the enzyme. Survey of 129 gates in 71 enzymes enabled a rigorous definition of gates and establishment of a new scheme for their classification. Gates were assigned to six distinct classes \u2013 wings, swinging doors, apertures, drawbridges, double drawbridges and shells. Presented are summary statistics describing the propensity of specific amino acid residues in particular gate classes. The proposed classification scheme provides guidance for the analysis and engineering of gates in biomolecular systems.<\/p>\n

Read more<\/a><\/p>\n

Engineering Enzyme Stability and Resistance to an Organic Cosolvent by Modification of Residues in the Access Tunnel<\/h2>\n

Koudelakova, T., Chaloupkova, R., Brezovsky, J., Prokop, Z., Sebestova, E., Hesseler, M., Khabiri, M., Plevaka, M., Kulik, D., Kuta Smatanova, I., Rezacova, P., Ettrich, R., Bornscheuer, U. T., Damborsky, J., 2013, Angewandte Chemie International Edition<\/em> 52: 1959-1963.<\/p>\n

\"Engineering<\/a><\/center><\/p>\n

Mutations targeting as few as four residues lining the access tunnel extended enzyme\u2019s half-life in 40% dimethyl sulfoxide from minutes to weeks (4,000-fold) and increased its melting temperature by 19 \u00b0C. Protein crystallography and molecular dynamics revealed that the tunnel residue packing is a key determinant of protein stability and the active-site accessibility for co-solvent molecules (red dots). The broad applicability of this concept was verified by analyzing twenty six proteins with buried active sites from all six enzyme classes.<\/p>\n

Read more<\/a><\/p>\n

CAVER 3.0: A Tool for Analysis of Transport Pathways in Dynamic Protein Structures<\/h2>\n

Chovancova, E., Pavelka, A., Benes, P., Strnad, O., Brezovsky, J., Kozlikova, B., Gora, A., Sustr, V., Klvana, M., Medek, P., Biedermannova, L., Sochor, J., Damborsky, J., 2012, PLOS Computational Biology<\/em> 8: e1002708.<\/p>\n

\"CAVER<\/p>\n

Tunnels and channels facilitate the transport of small molecules, ions and water solvent in a large variety of proteins. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromolecular structures. A new version of CAVER was developed enabling automatic analysis of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calculation and clustering of pathways. The software is freely available as a multiplatform command-line application at https:\/\/www.caver.cz<\/a>.<\/p>\n

Read more<\/a><\/p>\n

Enantioselectivity of Haloalkane Dehalogenases and its Modulation by Surface Loop Engineering<\/h2>\n

Prokop, Z., Sato, Y., Brezovsky, J., Mozga, T., Chaloupkova, R., Koudelakova, T., Jerabek, P., Stepankova, V., Natsume, R., Leeuwen, J. G. E., Janssen, D. B., Florian, J., Nagata, Y., Senda, T., Damborsky, J., 2010, Angewandte Chemie International Edition<\/em> 49: 6111-6115.<\/p>\n

\"Enantioselectivity<\/center><\/p>\n

Engineering of the surface loop in haloalkane dehalogenases affects their enantiodiscrimination behavior. The temperature dependence of the enantioselectivity (lnE versus 1\/T) of \u03b2-bromoalkanes by haloalkane dehalogenases is reversed (red data points) by deletion of the surface loop; the selectivity switches back when an additional single-point mutation is made. This behavior is not observed for \u03b1-bromoesters.<\/p>\n

Read more<\/a><\/p>\n

Redesigning Dehalogenase Access Tunnels as a Strategy for Degrading an Anthropogenic Substrate<\/h2>\n

Pavlova, M., Klvana, M., Chaloupkova, R., Banas, P., Otyepka, M., Wade, R., Nagata, Y., Damborsky, J., 2009, Nature Chemical Biology<\/em> 5: 727-733.<\/p>\n

\"Redesigning<\/p>\n

Engineering enzymes to degrade anthropogenic compounds efficiently is challenging. We obtained Rhodococcus rhodochrous haloalkane dehalogenase mutants with up to 32-fold higher activity than wild type toward the toxic, recalcitrant anthropogenic compound 1,2,3-trichloropropane (TCP) using a new strategy. Key residues in access tunnels connecting the buried active site with bulk solvent by rational design were identified and randomized by directed evolution. The most active mutant has large aromatic residues at two out of three randomized positions and two positions modified by site-directed mutagenesis. These changes apparently enhance activity with TCP by decreasing accessibility of the active site for water molecules, thereby promoting activated complex formation.<\/p>\n

Read more<\/a><\/p>\n

HotSpot Wizard: a Web Server for Identification of Hot Spots in Protein Engineering<\/h2>\n

Pavelka, A., Chovancova, E., Damborsky, J., 2009, Nucleic Acids Research<\/em> 37: W376-W383.<\/p>\n

\"HotSpot<\/center><\/p>\n

HotSpot Wizard is a web server for automatic identification of \u2018hot spots\u2019 for engineering of substrate specificity, activity or enantioselectivity of enzymes and for annotation of protein structures. The web server implements the protein engineering protocol, which targets evolutionarily variable amino acid positions located in the active site or lining the access tunnels. The \u2018hot spots\u2019 for mutagenesis are selected through the integration of structural, functional and evolutionary information obtained from: (i) the databases RCSB PDB, UniProt, PDBSWS, Catalytic Site Atlas and nr NCBI and (ii) the tools CASTp, CAVER, BLAST, CD-HIT, MUSCLE and Rate4Site. The HotSpot Wizard is freely available at https:\/\/loschmidt.chemi.muni.cz\/hotspotwizard\/<\/a>.<\/p>\n

Read more<\/a><\/p>\n

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 <\/p>\n","protected":false},"excerpt":{"rendered":"

Our Mission We educate leaders for Science, Medicine and Business! We are Loschmidt Laboratories based at the Masaryk University and the International Clinical Research Center of St. Anne\u2019s University Hospital Brno. Our laboratories are located at the University Campus Bohunice in Brno, Czech Republic. Proteins are natural building blocks of all living organisms and participate […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":53,"comment_status":"closed","ping_status":"closed","template":"","meta":{"ngg_post_thumbnail":0,"footnotes":""},"class_list":["post-896","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/loschmidt.chemi.muni.cz\/peg\/wp-json\/wp\/v2\/pages\/896","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/loschmidt.chemi.muni.cz\/peg\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/loschmidt.chemi.muni.cz\/peg\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/loschmidt.chemi.muni.cz\/peg\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/loschmidt.chemi.muni.cz\/peg\/wp-json\/wp\/v2\/comments?post=896"}],"version-history":[{"count":345,"href":"https:\/\/loschmidt.chemi.muni.cz\/peg\/wp-json\/wp\/v2\/pages\/896\/revisions"}],"predecessor-version":[{"id":11372,"href":"https:\/\/loschmidt.chemi.muni.cz\/peg\/wp-json\/wp\/v2\/pages\/896\/revisions\/11372"}],"wp:attachment":[{"href":"https:\/\/loschmidt.chemi.muni.cz\/peg\/wp-json\/wp\/v2\/media?parent=896"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}