We have an open positions for the first-year Master students and the third-year Bachelor students from Poznan interested in structural bioinformatics of proteins. The call is open until all available positions are filled.
Beerens K, Mazurenko S, Kunka A, Marques SM, Hansen N, Musil M, Chaloupkova R, Waterman J, Brezovsky J, Bednar D, Prokop Z, Damborsky J, 2018: Evolutionary Analysis is a Powerful Complement to Energy Calculations for Protein Stabilization. ACS Catalysis 8: 9420-9428. full text
Stability is one of the most important characteristics of proteins employed as biocatalysts, biotherapeutics, and biomaterials, and the role of computational approaches in modifying protein stability is rapidly expanding. We have recently identified stabilizing mutations in haloalkane dehalogenase DhaA using phylogenetic analysis but were not able to reproduce the effects of these mutations using force-field calculations. Here we tested four different hypotheses to explain the molecular basis of stabilization using structural, biochemical, biophysical, and computational analyses. We demonstrate that stabilization of DhaA by the mutations identified using the phylogenetic analysis is driven by both entropy and enthalpy contributions, in contrast to primarily enthalpy-driven stabilization by mutations designed by the force-field calculations. Comprehensive bioinformatics analysis revealed that more than half (53%) of 1 099 evolution-based stabilizing mutations would be evaluated as destabilizing by force-field calculations. Thermodynamic integration considers both folded and unfolded states and can describe the entropic component of stabilization, yet it is not suitable for predictive purposes due to its high computational demands. Altogether, our results strongly suggest that energetic calculations should be complemented by a phylogenetic analysis in protein-stabilization endeavors.
We have been invited to guest-edit the special issue on Novel Computational and Data-Driven Approaches for Protein Design and Their Application in Biomolecules journal. With CiteScore 2017 (Scopus) of 5.72, the journal ranks in Q1 in Biochemistry and Molecular Biology categories. Looking forward to the experience.
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
Studying the transport paths of ligands, solvents, or ions in transmembrane proteins and proteins with buried binding sites is fundamental to the understanding of their biological function. A detailed analysis of the structural features influencing the transport paths is also important for engineering proteins for biomedical and biotechnological applications.
CAVER Analyst 2.0 is a software tool for quantitative analysis and real-time visualization of tunnels and channels in static and dynamic structures. This version provides the users with many new functions, including advanced techniques for intuitive visual inspection of the spatiotemporal behavior of tunnels and channels. Novel integrated algorithms allow an efficient analysis and data reduction in large protein structures and molecular dynamic simulations.
CAVER Analyst 2.0 is a multi-platform standalone Java-based application. Binaries and documentation are freely available at www.caver.cz.
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.