Simulation of Ligand Transport in Receptors Using CaverDock
Language English Country United States Media print
Document type Journal Article, Research Support, Non-U.S. Gov't
- Keywords
- Drug design, Enzyme engineering, Ligand screening, Ligand transport, Molecular docking, Tunnel analysis,
- MeSH
- Chlorohydrins chemistry MeSH
- Ethanol analogs & derivatives chemistry MeSH
- Ethylene Dibromide chemistry MeSH
- Hydrolases chemistry MeSH
- Arachidonic Acid chemistry MeSH
- Ligands MeSH
- Drug Discovery methods MeSH
- Proteins chemistry MeSH
- Drug Design MeSH
- Molecular Dynamics Simulation MeSH
- Molecular Docking Simulation methods MeSH
- Software MeSH
- Cytochrome P-450 Enzyme System chemistry MeSH
- Thermodynamics MeSH
- Protein Binding MeSH
- Binding Sites MeSH
- Structure-Activity Relationship MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- 2,3-dichloro-1-propanol MeSH Browser
- Chlorohydrins MeSH
- Ethanol MeSH
- Ethylene Dibromide MeSH
- ethylene bromohydrin MeSH Browser
- haloalkane dehalogenase MeSH Browser
- Hydrolases MeSH
- Arachidonic Acid MeSH
- Ligands MeSH
- Proteins MeSH
- Cytochrome P-450 Enzyme System MeSH
Interactions between enzymes and small molecules lie in the center of many fundamental biochemical processes. Their analysis using molecular dynamics simulations have high computational demands, geometric approaches fail to consider chemical forces, and molecular docking offers only static information. Recently, we proposed to combine molecular docking and geometric approaches in an application called CaverDock. CaverDock is discretizing enzyme tunnel into discs, iteratively docking with restraints into one disc after another and searching for a trajectory of the ligand passing through the tunnel. Here, we focus on the practical side of its usage describing the whole method: from getting the application, and processing the data through a workflow, to interpreting the results. Moreover, we shared the best practices, recommended how to solve the most common issues, and demonstrated its application on three use cases.
See more in PubMed
Clouthier CM, Pelletier JN (2012) Expanding the organic toolbox: a guide to integrating biocatalysis in synthesis. Chem Soc Rev 41:1585–1605. https://doi.org/10.1039/c2cs15286j PubMed DOI
Koeller KM, Wong CH (2001) Enzymes for chemical synthesis. Nature 409:232–240. https://doi.org/10.1038/35051706 PubMed DOI
Soetaert W, Vandamme E (2006) The impact of industrial biotechnology. Biotechnol J 1:756–769. https://doi.org/10.1002/biot.200600066 PubMed DOI
Brezovsky J, Chovancova E, Gora A et al (2013) Software tools for identification, visualization and analysis of protein tunnels and channels. Biotechnol Adv 31:38–34 DOI
Pagadala NS, Syed K, Tuszynski J (2017) Software for molecular docking: a review. Biophys Rev 9:91–102 DOI
Hospital A, Goñi JR, Orozco M, Gelpí JL (2015) Molecular dynamics simulations: advances and applications. Adv Appl Bioinforma Chem 8:37–47
Filipovič J, Vávra O, Plhák J et al (2019) CaverDock: a novel method for the fast analysis of ligand transport. IEEE/ACM Trans Comput Biol Bioinforma:1–1. https://doi.org/10.1109/TCBB.2019.2907492
Vávra O, Filipovič J, Plhák J et al (2019) CaverDock: a molecular docking-based tool to analyse ligand transport through protein tunnels and channels. Bioinformatics 35:4986–4993. https://doi.org/10.1093/bioinformatics/btz386 PubMed DOI
Pinto GP, Vavra O, Filipovic J et al (2019) Fast screening of inhibitor binding/unbinding using novel software tool CaverDock. Front Chem 7:709. https://doi.org/10.3389/fchem.2019.00709 PubMed DOI PMC
Stourac J, Vavra O, Kokkonen P et al (2019) Caver web 1.0: identification of tunnels and channels in proteins and analysis of ligand transport. Nucleic Acids Res 47:W414–W422. https://doi.org/10.1093/nar/gkz378 PubMed DOI PMC
Virtanen P, Gommers R, Oliphant TE et al (2020) SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17:1–12. https://doi.org/10.1038/s41592-019-0686-2 DOI
The CGAL Project (2020) CGAL user and reference manual, 5.0.2. CGAL Editorial Board
Morris GM, Ruth H, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. https://doi.org/10.1002/jcc.21256 PubMed DOI PMC
Berman H, Westbrook JD, Feng Z et al (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242. https://doi.org/10.1093/nar/28.1.235 PubMed DOI PMC
Schwede T, Kopp J, Guex N, Peitsch MC (2003) SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 31:3381–3385. https://doi.org/10.1093/nar/gkg520 PubMed DOI PMC
Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5:725–738. https://doi.org/10.1038/nprot.2010.5 PubMed DOI PMC
Webb B, Sali A (2014) Protein structure modeling with MODELLER. Methods Mol Biol 1137:1–15. https://doi.org/10.1007/978-1-4939-0366-5_1 PubMed DOI
Case DA, Cheatham TE, Darden T et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688. https://doi.org/10.1002/jcc.20290 PubMed DOI PMC
Pronk S, Páll S, Schulz R et al (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29:845–854. https://doi.org/10.1093/bioinformatics/btt055 PubMed DOI PMC
Seeliger D, Haas J, de Groot BL (2007) Geometry-based sampling of conformational transitions in proteins. Structure 15:1482–1492. https://doi.org/10.1016/j.str.2007.09.017 PubMed DOI
Sterling T, Irwin JJ (2015) ZINC 15 - ligand discovery for everyone. J Chem Inf Model 55:2324–2337. https://doi.org/10.1021/acs.jcim.5b00559 PubMed DOI PMC
Hanwell MD, Curtis DE, Lonie DC et al (2012) Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform 4. https://doi.org/10.1186/1758-2946-4-17
The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC
O’Boyle NM, Banck M, James CA et al (2011) Open babel: an open chemical toolbox. J Cheminform 3:1–14. https://doi.org/10.1186/1758-2946-3-33 DOI
Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization. and multithreading J Comput Chem 31:455–461. https://doi.org/10.1002/jcc.21334 PubMed DOI
Chovancova E, Pavelka A, Benes P et al (2012) CAVER 3.0: a tool for the analysis of transport pathways in dynamic protein structures. PLoS Comput Biol 8. https://doi.org/10.1371/journal.pcbi.1002708
CAVER Web Portal. https://loschmidt.chemi.muni.cz/caverweb/ . Accessed 27 Feb 2020
CAVER User Guide. http://www.caver.cz/fil/download/manual/caver_userguide.pdf . Accessed 27 Feb 2020
Gnuplot. http://www.gnuplot.info . Accessed 27 Feb 2020
Brezovsky J, Babkova P, Degtjarik O et al (2016) Engineering a de novo transport tunnel. ACS Catal 6:7597–7610. https://doi.org/10.1021/acscatal.6b02081 DOI
Cui YL, Zheng QC, Zhang JL, Zhang HX (2015) Molecular basis of the recognition of arachidonic acid by cytochrome P450 2E1 along major access tunnel. Biopolymers 103:53–66. https://doi.org/10.1002/bip.22567 PubMed DOI
Cojocaru V, Winn PJ, Wade RC (2007) The ins and outs of cytochrome P450s. Biochim Biophys Acta - Gen Subj 1770:390–401. https://doi.org/10.1016/j.bbagen.2006.07.005 DOI
Jurcik A, Bednar D, Byska J et al (2018) CAVER analyst 2.0: analysis and visualization of channels and tunnels in protein structures and molecular dynamics trajectories. Bioinformatics 34:3586–3588. https://doi.org/10.1093/bioinformatics/bty386 PubMed DOI PMC
Marques SM, Dunajova Z, Prokop Z et al (2017) Catalytic cycle of Haloalkane Dehalogenases toward unnatural substrates explored by computational modeling. JChem Inf Model 57:1970–1989. https://doi.org/10.1021/acs.jcim.7b00070 DOI
