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.
We have been awarded an OPUS grant from NCN to deliver new approaches for analysis of ligand transport pathways in proteins. The grant will provide funding for notable expansion of our team in the coming three years.
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.