Jurcik A, Bednar D, Byska J, Marques SM, Furmanova K, Daniel L, Kokkonen P, Brezovsky J, Strnad O, Stourac J, Pavelka A, Manak M, Damborsky J, Kozlikova B, 2018: CAVER Analyst 2.0: Analysis and Visualization of Channels and Tunnels in Protein Structures and Molecular Dynamics Trajectories. Bioinformatics (just accepted): doi:10.1093/bioinformatics/bty386. full text

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 (k_cat/K_0.5 = 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.