Determination of the rate limiting step during zearalenone hydrolysis by ZenA

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Engineering of haloalkane dehalogenase enantioselectivity towards βbromoalkanes: Open-solvated versus occluded-desolvated active sites

Enzymatic catalysis is widely used for preparing optically pure chemicals. Natural catalysts have to be often optimized to exhibit sufficient enantioselectivity towards industrially attractive non-natural substrates. Understanding the molecular basis of enzyme–substrate interactions involved in enantiodiscrimination is essential for rational design of selective catalysts. Haloalkane dehalogenases (EC 3.8.1.5) can convert a broad range of halogenated aliphatic compounds to their corresponding alcohols via SN2 mechanism [1]. The very first haloalkane dehalogenase exhibiting high enantioselectivity towards β-brominated alkanes (E-values of up to 174) was DbjA from Bradyrhizobium japonicum USDA110 [2]. This enzyme has a wide open solvent-accessible active site and its enantioselectivity towards β-brominated alkanes is modulated by a surface loop unique to DbjA [2]. Assuming that the active site geometry is crucial for substrate recognition, it was proposed that DbjA’s enantioselectivity could be transferred to closely related, but non-selective DhaA from Rhodococcus rhodochrous NCIMB13064 [1] by active site transplantation [3]. The unique loop fragment from DbjA together with additional 8-point substitutions was inserted to DhaA. Although the crystal structure of resulting variant DhaA12 exhibited identical geometry of the active site and the access tunnel as DbjA, it did not reach identical level of hydration and flexibility and lacked enantioselectivity towards β-bromoalkanes (E-value = 18) [3]. Interestingly, the variant DhaA31 constructed independently with a goal to enhance enzyme activity towards anthropogenic compound 1,2,3-trichlopropane [4], exhibited high enantioselectivity towards 2-bromopentane (E-value = 179) [5] as DbjA (E-value = 174) [2, 3]. DhaA31 contains five mutations, I135F, C176Y, V245F, L246I and Y273F, located in a main and a slot tunnel. Four of five mutations are large and aromatic residues narrowing two access tunnels and occluding the enzyme active site [4]. The level of DhaA31 active site hydration, so important for DbjA’s enantioselectivity [2, 3] is low, suggesting a different structural basis of enantioselectivity towards 2-bromopentane. A systematic study on the molecular basis of enantioselectivity in DbjA, DhaA, and DhaA31 using thermodynamic and kinetic analyses, site-directed mutagenesis, and molecular modeling was carried out. DhaA31 enantioselectivity arises from the hydrophobic substrate’s interactions with the occluded and desolvated active site [5], while DbjA enantioselectivity results from water-mediated interactions of 2-bromopentane with the active site’s hydrophobic wall [2]. Our data imply that enantioselectivity of haloalkane dehalogenases can be achieved by both occluded-desolvated active site and open-solvated active site. The engineering of “DbjA-like” enantioselectivity by modification of the active site hydration remains challenging. References: 1. Koudelakova, T., et al. 2013. Biotechnol. J. 8: 32–45. Prokop, Z., et al. 2010. Angew. Chem. Int. Ed., 49: 6111-6115. Sykora, J., et al. 2014. Nat. Chem. Biol., 10: 428-430. Pavlova, M., et al. 2009. Nat. Chem. Biol., 5: 727-733. Liskova, V., et al. 2017. Angew. Chem. Int. Ed., DOI: 10.1002/anie.201611193

FireProt ASR: Automated design of ancestral proteins

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A single mutation in a tunnel to the active site changes the mechanism and kinetics of product release in haloalkane dehalogenase LinB

Many enzymes have buried active sites. The properties of the tunnels connecting the active site with bulk solvent affect ligand binding and unbinding and, therefore, also the catalytic properties. Here, we investigate ligand passage in the haloalkane dehalogenase enzyme LinB, and the effect of replacing leucine by a bulky tryptophan at a tunnel-lining position. Transient kinetic experiments show that the mutation significantly slows down the rate of product release. Moreover, the mechanism of bromide ion release is changed from a one-step process in the wild type enzyme to a two-step process in the mutant. The rate constant of bromide ion release corresponds to the overall steady-state turnover rate constant, suggesting that product release became the rate-limiting step of catalysis in the mutant. We explain the experimental findings by investigating the molecular details of the process computationally. Analysis of trajectories from molecular dynamics simulations with the CAVER 3.0 program reveals differences in the tunnels available for ligand egress. Corresponding differences are seen in simulations of product egress using the Random Acceleration Molecular Dynamics technique. The differences in the free energy barriers for egress of a bromide ion calculated using the Adaptive Biasing Force method are in good agreement with the differences in rates obtained from the transient kinetic experiments. Interactions of the bromide ion with the introduced tryptophan are shown to affect the free energy barrier for its passage. The study demonstrates how the mechanism of an enzymatic catalytic cycle and reaction kinetics can be engineered by modification of protein tunnels

EnzymeMiner: Exploration of sequence space of enzymes

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FireProt: Web server for automated design of thermostable proteins

Stable proteins are used in numerous biomedical and biotechnological applications. Unfortunately, naturally occurring proteins cannot usually withstand the harsh industrial environment, since they are mostly evolved to function at mild conditions. Therefore, there is a continuous interest in increasing protein stability to enhance their industrial potential. A number of in silico tools for the prediction of the effect of mutations on protein stability have been developed recently. However, only single-point mutations with a small effect on protein stability are typically predicted with the existing tools and have to be followed by laborious protein expression, purification, and characterization. A much higher degree of stabilization can be achieved by the construction of the multiple-point mutants. Here, we present the FireProt method [1] and the web server [2] for the automated design of multiple-point mutant proteins that combines structural and evolutionary information in its calculation core. FireProt utilizes sixteen bioinformatics tools, including several force field calculations. Highly reliable designs of the thermostable proteins are constructed by two distinct protein engineering strategies, based on the energy and evolution approaches and the multiple-point mutants are checked for the potentially antagonistic effects in the designed protein structure. Furthermore, time demands of the FireProt method are radically decreased by the utilization of the smart knowledge-based filters, protocol optimization, and effective parallelization. The server is complemented with an interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostable proteins. The server is freely available at http://loschmidt.chemi.muni.cz/fireprot. 1. Bednar, D., Beerens, K., Sebestova, E., Bendl, J., Khare, S., Chaloupkova, R., Prokop, Z., Brezovsky, J., Baker, D., Damborsky, J., 2015: FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants. PLOS Computational Biology 11: e1004556. 2. Musil, M., Stourac, J., Bendl, J., Brezovsky, J., Prokop, Z., Zendulka, J., Martinek, T., Bednar, D., Damborsky, J., 2017, FireProt: Web Server for Automated Design of Thermostable Proteins, Nucleic Acids Research, in press, doi: 10.1093/nar/gkx285

Controlled Oil/Water Partitioning of Hydrophobic Substrates Extending the Bioanalytical Applications of Droplet-Based Microfluidics.

Functional annotation of novel proteins lags behind the number of sequences discovered by the next-generation sequencing. The throughput of conventional testing methods is far too low compared to sequencing; thus, experimental alternatives are needed. Microfluidics offer high throughput and reduced sample consumption as a tool to keep up with a sequence-based exploration of protein diversity. The most promising droplet-based systems have a significant limitation: leakage of hydrophobic compounds from water compartments to the carrier prevents their use with hydrophilic reagents. Here, we present a novel approach of substrate delivery into microfluidic droplets and apply it to high-throughput functional characterization of enzymes that convert hydrophobic substrates. Substrate delivery is based on the partitioning of hydrophobic chemicals between the oil and water phases. We applied a controlled distribution of 27 hydrophobic haloalkanes from oil to reaction water droplets to perform substrate specificity screening of eight model enzymes from the haloalkane dehalogenase family. This droplet-on-demand microfluidic system reduces the reaction volume 65 000-times and increases the analysis speed almost 100-fold compared to the classical test tube assay. Additionally, the microfluidic setup enables a convenient analysis of dependences of activity on the temperature in a range of 5 to 90 °C for a set of mesophilic and hyperstable enzyme variants. A high correlation between the microfluidic and test tube data supports the approach robustness. The precision is coupled to a considerable throughput of >20 000 reactions per day and will be especially useful for extending the scope of microfluidic applications for high-throughput analysis of reactions including compounds with limited water solubility.ERC Advanced Investigator grant no. 69566

The SAP domain of Ku facilitates its efficient loading onto DNA ends

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 sliding of Ku along DNA. Our findings revealed that the C-terminal SAP domain of Ku70 facilitates the initial phases of the Ku–DNA interaction but does not affect the sliding process. This suggests that the SAP domain may either establish the first interactions with DNA, or stabilize these initial interactions during loading. To assess the biological role of the SAP domain, we generated Arabidopsis plants expressing Ku lacking the SAP domain. Intriguingly, despite the decreased efficiency of the ΔSAP Ku complex in loading onto DNA, the mutant plants exhibited full proficiency in classical NHEJ and telomere maintenance. This indicates that the speed with which Ku loads onto telomeres or DNA double-strand breaks is not the decisive factor in stabilizing these DNA structures.peer-reviewe

Strategies and software tools for engineering protein tunnels and dynamical gates

Improvements in the catalytic activity, substrate specificity or enantioselectivity of enzymes are traditionally achieved by modification of enzymes’ active sites. We have recently proposed that the enzyme engineering endeavors should target both the active sites and the access tunnels/channels [1,2]. Using the model enzymes haloalkane dehalogenases, we have demonstrated that engineering of access tunnels provides enzymes with significantly improved catalytic properties [3] and stability [4]. User-friendly software tools Caver [5], Caver Analyst [6], CaverDock [7] and Caver Web [8], have been developed for the computational design of protein tunnels/channels; FireProt [9] and HotSpot Wizard [10] for automated design of stabilizing mutations and smart libraries. Using these tools we were able to introduce a new tunnel to a protein structure and tweak its conformational dynamics. This engineering strategy has led to improved catalytic efficiency [2], enhanced promiscuity or even a functional switch (unpublished). Our concepts and software tools are widely applicable to various enzymes with known structures and buried active sites. 1. Damborsky, J., et al., 2009: Computational Tools for Designing and Engineering Biocatalysts. Current Opinion in Chemical Biology 13: 26-34. 2. Prokop, Z., et al., 2012: Engineering of Protein Tunnels: Keyhole-lock-key Model for Catalysis by the Enzymes with Buried Active Sites. Protein Engineering Handbook, Wiley-VCH, Weinheim, pp. 421-464. 3. Brezovsky, J., et al., 2016: Engineering a de Novo Transport Tunnel. ACS Catalysis 6: 7597-7610. 4. Koudelakova, T., et al., 2013: Engineering Enzyme Stability and Resistance to an Organic Cosolvent by Modification of Residues in the Access Tunnel. Angewandte Chemie 52: 1959-1963. 5. Chovancova, E., et al., 2012: CAVER 3.0: A Tool for Analysis of Transport Pathways in Dynamic Protein Structures. PLOS Computational Biology 8: e1002708. 6. Jurcik, A., et al., 2018: CAVER Analyst 2.0: Analysis and Visualization of Channels and Tunnels in Protein Structures and Molecular Dynamics Trajectories. Bioinformatics 34: 3586-3588. 7. Vavra, O., et al., 2019: CaverDock 1.0: A New Tool for Analysis of Ligand Binding and Unbinding Based on Molecular Docking. Bioinformatics (under review). 8. Stourac, J., et al. 2019: Caver Web 1.0: Identification of Tunnels and Channels in Proteins and Analysis of Ligand Transport. Nucleic Acids Research (under review). 9. Musil, M., et al., 2017: FireProt: Web Server for Automated Design of Thermostable Proteins. Nucleic Acids Research 45: W393-W399. 10. Sumbalova, L. et al., 2018: HotSpot Wizard 3.0: Automated Design of Site-Specific Mutations and Smart Libraries in Protein Engineering. Nucleic Acids Research 46: W356-W362

Advanced database mining integrating sequence and structure bioinformatics with microfluidics challenges enzyme engineering

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