Stourac J, Vavra O, Kokkonen P, Filipovic J, Pinto G, Brezovsky J, Damborsky J, Bednar D, 2019: Caver Web 1.0: Identification of Tunnels and Channels in Proteins and Analysis of Ligand Transport. Nucleid Acids Research (advance article DOI: 10.1093/nar/gkz378). full text

Caver Web 1.0 is a web server for comprehensive analysis of protein tunnels and channels, and study of the ligands’ transport through these transport pathways. Caver Web is the first interactive tool allowing both the analyses within a single graphical user interface. The server is built on top of the abundantly used tunnel detection tool Caver 3.02 and CaverDock 1.0 enabling the study of the ligand transport. The program is easy-to-use as the only required inputs are a protein structure for a tunnel identification and a list of ligands for the transport analysis. The automated guidance procedures assist the users to set up the calculation in a way to obtain biologically relevant results. The identified tunnels, their properties, energy profiles and trajectories for ligands’ passages can be calculated and visualized. The tool is very fast (2-20 min per job) and is applicable even for virtual screening purposes. Its simple setup and comprehensive graphical user interface make the tool accessible for a broad scientific community. The server is freely available at https://loschmidt.chemi.muni.cz/caverweb.

Filipovic J, Vavra O, Plhak J, Bednar D, Marques  SM, Brezovsky J, Matyska L, Damborsky J, 2019: CaverDock: A Novel Method for the Fast Analysis of Ligand Transport. IEEE Transactions on Computational Biology and Bioinformatics (early access DOI:10.1109/TCBB.2019.2907492). full text

Here we present a novel method for the analysis of transport processes in proteins and its implementation called CaverDock. Our method is based on a modified molecular docking algorithm. It iteratively places the ligand along the access tunnel in such a way that the ligand movement is contiguous and the energy is minimized. The result of CaverDock calculation is a ligand trajectory and an energy profile of the transport process. CaverDock uses the modified docking program Autodock Vina for molecular docking and implements a parallel heuristic algorithm for searching the space of possible trajectories. Our method lies in between the geometrical approaches and molecular dynamics simulations. Contrary to the geometrical methods, it provides an evaluation of chemical forces. However, it is far less computationally demanding and easier to set up compared to molecular dynamics simulations. CaverDock will find broad use in the fields of computational enzymology, drug design and protein engineering. The software is available free of charge to the academic users at https://loschmidt.chemi.muni.cz/caverdock/.

Vavra O, Filipovic J, Plhak J, Bednar D, Marques SM, Brezovsky J, Stourac J, Matyska L, Damborsky J, 2019: CaverDock: A Molecular Docking-Based Tool to Analyse Ligand Transport through Protein Tunnels and Channels. Bioinformatics (accepted manuscript DOI: 10.1093/bioinformatics/btz386). full text

Motivation: Protein tunnels and channels are key transport pathways that allow ligands to pass between proteins’ external and internal environments. These functionally important structural features warrant detailed attention. It is difficult to study the ligand binding and unbinding process experimentally, while molecular dynamics simulations can be time-consuming and computationally demanding.
Results: CaverDock is a new software tool for analysing the ligand passage through the biomolecules. The method uses the optimised docking algorithm of AutoDock Vina for ligand placement docking and implements a parallel heuristic algorithm to search the space of possible trajectories. The duration of the simulations takes from minutes to a few hours. Here we describe the implementation of the method and demonstrate CaverDock’s usability by i) comparison of the results with other available tools, ii) determination of the robustness with large ensembles of ligands and iii) the analysis and comparison of the ligand trajectories in engineered tunnels. Thorough testing confirms that CaverDock is applicable for the fast analysis of ligand binding and unbinding in fundamental enzymology and protein engineering.
Availability: User guide and binaries for Ubuntu are freely available for non-commercial use at https://loschmidt.chemi.muni.cz/caverdock/. The web implementation is available at https://loschmidt.chemi.muni.cz/caverweb/. The source code is available on request.

Kokkonen P, Sykora J, Prokop Z, Ghose A, Bednar D, Amaro M, Beerens K, Bidmanova S, Slanska M, Brezovsky J, Damborsky J, Hof M, 2018: Molecular Gating of an Engineered Enzyme Captured in Real Time. Journal of the American Chemical Society (just accepted), doi: 10.1021/jacs.8b09848. full text

Engineering dynamical molecular gates represents a widely applicable strategy for designing efficient biocatalysts. Here we analyzed the dynamics of a molecular gate artificially introduced into an access tunnel of the most efficient haloalkane dehalogenase using pre-steady-state kinetics, a single-molecule fluorescence spectroscopy and molecular dynamics. Photoinduced electron-transfer – fluorescence correlation spectroscopy (PET-FCS) has enabled real-time observation of molecular gating at single molecule level with the rate constants (k_on = 1822 s^-1, k_off = 60 s^-1) corresponding well with those from the pre-steady-state kinetics (k_-1 = 1100 s^-1, k₁ = 20 s^-1).

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.