CaverDock: a molecular docking-based tool to analyse ligand transport through protein tunnels and channels
Authors
Vavra, O., Filipovic, J., Plhak, J., Bednar, D., Marques, S.M., Brezovsky, J., Stourac, J., Matyska, L. and Damborsky, J.
Source
Bioinformatics, 35(23)
Abstract
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 processes 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 optimized 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 and implementation
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 upon request.
Source
Vavra, O., Filipovic, J., Plhak, J., Bednar, D., Marques, S.M., Brezovsky, J., Stourac, J., Matyska, L. and Damborsky, J.:
CaverDock: a molecular docking-based tool to analyse ligand transport through protein tunnels and channels ,
Bioinformatics, , 35(23), 4986-4993, 2019.