Kaushik S, Marques SM, Khirsariya P, Paruch K, Libichova L, Brezovsky J, Prokop Z, Chaloupkova R, Damborsky J, 2018: Impact of the Access Tunnel Engineering on Catalysis is Strictly Ligand-Specific. FEBS Journal (just accepted): doi: 10.1111/febs.14418. full text
The traditional way of rationally engineering enzymes to change their biocatalytic properties utilizes the modifications of their active sites. Another emerging approach is the engineering of structural features involved in the exchange of ligands between buried active sites and the surrounding solvent. However, surprisingly little is known about the effects of mutations that alter the access tunnels on the enzymes’ catalytic properties, and how these tunnels should be redesigned to allow fast passage of cognate substrates and products. Thus, we have systematically studied the effects of single-point mutations in a tunnel-lining residue of a haloalkane dehalogenase on the binding kinetics and catalytic conversion of both linear and branched haloalkanes. The hotspot residue Y176 was identified using computer simulations and randomized through saturation mutagenesis, and the resulting variants were screened for shifts in binding rates. Strikingly, opposite effects of the substituted residues on the catalytic efficiency towards linear and branched substrates were observed, which was found to be due to substrate-specific requirements in the critical steps of the respective catalytic cycles. We conclude that not only the catalytic sites but also the access pathways must be tailored specifically for each individual ligand, which is a new paradigm in protein engineering and de novo protein design. A rational approach is proposed here to address more effectively the task of designing ligand-specific tunnels using computational tools.
Vanacek P, Sebestova E, Babkova P, Bidmanova S, Daniel L, Dvorak P, Stepankova V, Chaloupkova R, Brezovsky J, Prokop Z, Damborsky J, 2018: Exploration of Enzyme Diversity by Integrating Bioinformatics with Expression Analysis and Biochemical Characterization. ACS Catalysis 8: 2402–2412. full text
Millions of protein sequences are being discovered at an incredible pace, representing an inexhaustible source of biocatalysts. Here, we describe 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, was validated against the haloalkane dehalogenase family. The sequence-based search identified 658 potential dehalogenases. The subsequent structural bioinformatics prioritized and selected 20 candidates for exploration of protein functional diversity. Out of these twenty, the expression analysis and the robotic screening of enzymatic activity provided 8 soluble proteins with dehalogenase activity. The enzymes discovered originated from genetically unrelated Bacteria and Eukaryota, and, for the first time, from Archaea. Overall, the integrated system provided biocatalysts with broad catalytic diversity showing unique substrate specificity profiles, covering a wide range of optimal operational temperature from 20 to 70 °C and an unusually broad pH range from 5.7 to 10. We obtained the most catalytically proficient native haloalkane dehalogenase enzyme to date (kcat/K0.5 = 96.8 mM-1.s-1), the most thermostable enzyme with melting temperature 71 °C, three different cold-adapted enzymes showing dehalogenase activity at near-to-zero temperatures and a biocatalyst degrading the warfare chemical sulfur mustard. The established strategy can be adapted to other enzyme families for exploration of their biocatalytic diversity in a large sequence space continuously growing due to the use of next-generation sequencing technologies.
Dvorak P, Bednar D, Vanacek P, Balek L, Eiselleova L, Stepankova V, Sebestova E, Kunova Bosakova M, Konecna Z, Mazurenko S, Kunka A, Vanova T, Zoufalova K, Chaloupkova R, Brezovsky J, Krejci P, Prokop Z, Dvorak P, Damborsky J, 2018: Computer-Assisted Engineering of Hyperstable Fibroblast Growth Factor 2. Biotechnology and Bioengineering (just accepted): doi: 10.1002/bit.26531. full text
Fibroblast growth factors (FGFs) serve numerous regulatory functions in complex organisms, and their corresponding therapeutic potential is of growing interest to academics and industrial researchers alike. However, applications of these proteins are limited due to their low stability. Here we tackle this problem using a generalizable computer-assisted protein engineering strategy to create a unique modified FGF2 with nine mutations displaying unprecedented stability and uncompromised biological function. The data from the characterization of stabilized FGF2 showed a remarkable prediction potential of in silico methods and provided insight into the unfolding mechanism of the protein. The molecule holds a considerable promise for stem cell research and medical or pharmaceutical applications.
Brezovsky J, Kozlikova B, Damborsky J, 2018: Computational Analysis of Protein Tunnels and Channels. In: Bornscheuer U., Höhne M. (eds) Protein Engineering. Methods in Molecular Biology, vol 1685. Humana Press, New York, NY, pp. 25-42. full text
Protein tunnels connecting the functional buried cavities with bulk solvent and protein channels, enabling the transport through biological membranes, represent the structural features that govern the exchange rates of ligands, ions, and water solvent. Tunnels and channels are present in a vast number of known proteins and provide control over their function. Modification of these structural features by protein engineering frequently provides proteins with improved properties. Here we present a detailed computational protocol employing the CAVER software that is applicable for: (1) the analysis of tunnels and channels in protein structures, and (2) the selection of hot-spot residues in tunnels or channels that can be mutagenized for improved activity, specificity, enantioselectivity, or stability.
Marques SM, Dunajova Z, Prokop Z, Chaloupkova R, Brezovsky J, Damborsky J, 2017: Catalytic Cycle of Haloalkane Dehalogenases towards Unnatural Substrates Explored by Computational Modeling. Journal of Chemical Information and Modeling 57: 1970–1989. full text
The anthropogenic toxic compound 1,2,3-trichloropropane is poorly degradable by natural enzymes. We have previously constructed the haloalkane dehalogenase DhaA31 by focused directed evolution (Pavlova, M. et al. Nat. Chem. Biol. 2009, 5, 727−733), which is 32 times more active than the wild-type enzyme and is currently the most active variant known against that substrate. Recent evidence has shown that the structural basis responsible for the higher activity of DhaA31 was poorly understood. Here we have undertaken a comprehensive computational study of the main steps involved in the biocatalytic hydrolysis of 1,2,3-trichloropropane to decipher the structural basis for such enhancements. Using molecular dynamics and quantum mechanics approaches we have surveyed (i) the substrate binding, (ii) the formation of the reactive complex, (iii) the chemical step, and (iv) the release of the products. We showed that the binding of the substrate and its transport through the molecular tunnel to the active site is a relatively fast process. The cleavage of the carbon–halogen bond was previously identified as the rate-limiting step in the wild-type. Here we demonstrate that this step was enhanced in DhaA31 due to a significantly higher number of reactive configurations of the substrate and a decrease of the energy barrier to the SN2 reaction. C176Y and V245F were identified as the key mutations responsible for most of those improvements. The release of the alcohol product was found to be the rate-limiting step in DhaA31 primarily due to the C176Y mutation. Mutational dissection of DhaA31 and kinetic analysis of the intermediate mutants confirmed the theoretical observations. Overall, our comprehensive computational approach has unveiled mechanistic details of the catalytic cycle which will enable a balanced design of more efficient enzymes. This approach is applicable to deepen the biochemical knowledge of a large number of other systems and may contribute to robust strategies in the development of new biocatalysts.