Abstract
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.
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References
Prokop Z, Gora A, Brezovsky J et al (2012) Engineering of protein tunnels: keyhole-lock-key model for catalysis by the enzymes with buried active sites. In: Lutz S, Bornscheuer UT (eds) Protein engineering handbook. Wiley-VCH, Weinheim, pp 421–464
Gora A, Brezovsky J, Damborsky J (2013) Gates of enzymes. Chem Rev 113:5871–5923
Kingsley LJ, Lill MA (2015) Substrate tunnels in enzymes: structure-function relationships and computational methodology. Proteins 83:599–611
Biedermannova L, Prokop Z, Gora A et al (2012) A single mutation in a tunnel to the active site changes the mechanism and kinetics of product release in haloalkane dehalogenase LinB. J Biol Chem 287:29062–29074
Pavlova M, Klvana M, Prokop Z et al (2009) Redesigning dehalogenase access tunnels as a strategy for degrading an anthropogenic substrate. Nat Chem Biol 5:727–733
Chaloupkova R, Sykorova J, Prokop Z et al (2003) Modification of activity and specificity of haloalkane dehalogenase from Sphingomonas paucimobilis UT26 by engineering of its entrance tunnel. J Biol Chem 278:52622–52628
Prokop Z, Sato Y, Brezovsky J et al (2010) Enantioselectivity of haloalkane dehalogenases and its modulation by surface loop engineering. Angew Chem Int Ed 49:6111–6115
Koudelakova T, Chaloupkova R, Brezovsky J et al (2013) Engineering enzyme stability and resistance to an organic cosolvent by modification of residues in the access tunnel. Angew Chem Int Ed 52:1959–1963
Liskova V, Bednar D, Prudnikova T et al (2015) Balancing the stability–activity trade-off by fine-tuning dehalogenase access tunnels. ChemCatChem 7:648–659
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:e1002708
Sehnal D, Svobodova Varekova R, Berka K et al (2013) MOLE 2.0: advanced approach for analysis of biomacromolecular channels. J Cheminform 5:39
Yaffe E, Fishelovitch D, Wolfson HJ et al (2008) MolAxis: efficient and accurate identification of channels in macromolecules. Proteins 73:72–86
Masood TB, Sandhya S, Chandra N et al (2015) CHEXVIS: a tool for molecular channel extraction and visualization. BMC Bioinformatics 16:119
Kim J-K, Cho Y, Lee M et al (2015) BetaCavityWeb: a webserver for molecular voids and channels. Nucleic Acids Res 43:W413–W418
Brezovsky J, Chovancova E, Gora A et al (2013) Software tools for identification, visualization and analysis of protein tunnels and channels. Biotechnol Adv 31:38–49
Kingsley LJ, Lill MA (2014) Ensemble generation and the influence of protein flexibility on geometric tunnel prediction in cytochrome P450 enzymes. PLoS One 9:e99408
Kozlikova B, Sebestova E, Sustr V et al (2014) CAVER analyst 1.0: graphic tool for interactive visualization and analysis of tunnels and channels in protein structures. Bioinformatics 30:2684–2685
Pavelka A, Chovancova E, Damborsky J (2009) HotSpot wizard: a web server for identification of hot spots in protein engineering. Nucleic Acids Res 37:W376–W383
Case DA, Cheatham TE, Darden T et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688
Berendsen HJC, van der Spoel D, van Drunen R (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91:43–56
Brooks BR, Bruccoleri RE, Olafson BD et al (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4:187–217
Meyer T, D’Abramo M, Hospital A et al (2010) MoDEL (molecular dynamics extended library): a database of atomistic molecular dynamics trajectories. Structure 18:1399–1409
Henrich S, Salo-Ahen OMH, Huang B et al (2010) Computational approaches to identifying and characterizing protein binding sites for ligand design. J Mol Recognit 23:209–219
Perot S, Sperandio O, Miteva MA et al (2010) Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery. Drug Discov Today 15:656–667
Dundas J, Ouyang Z, Tseng J et al (2006) CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 34:W116–W118
Zhang Z, Li Y, Lin B et al (2011) Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction. Bioinformatics 27:2083–2088
Schmidtke P, Le Guilloux V, Maupetit J et al (2010) Fpocket: online tools for protein ensemble pocket detection and tracking. Nucleic Acids Res 38:W582–W589
UniProt Consortium (2015) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212
Furnham N, Holliday GL, de Beer TAP et al (2014) The catalytic site atlas 2.0: cataloging catalytic sites and residues identified in enzymes. Nucleic Acids Res 42:D485–D489
Pravda L, Berka K, Svobodova Varekova R et al (2014) Anatomy of enzyme channels. BMC Bioinformatics 15:379
Laskowski RA, Swindells MB (2011) LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 51:2778–2786
Stierand K, Rarey M (2010) Drawing the PDB: protein-ligand complexes in two dimensions. ACS Med Chem Lett 1:540–545
Sobolev V, Sorokine A, Prilusky J et al (1999) Automated analysis of interatomic contacts in proteins. Bioinformatics 15:327–332
Sebestova E, Bendl J, Brezovsky J et al (2014) Computational tools for designing smart libraries. Methods Mol Biol 1179:291–314
Acknowledgments
The authors would like to express their thanks to Sergio Marques and David Bednar (Masaryk University, Brno) and to the editors Uwe Bornscheuer and Matthias Höhne (University Greifswald, Greifswald) for critical reading of the manuscript. MetaCentrum and CERIT-SC are acknowledged for providing access to supercomputing facilities (LM2015042 and LM2015085). The Czech Ministry of Education is acknowledged for funding (LQ1605, LO1214, LM2015051, LM2015047 and LM2015055). Funding has been also received from the European Union Horizon 2020 research and innovation program under the grant agreement No. 676559.
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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. https://doi.org/10.1007/978-1-4939-7366-8_3
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DOI: https://doi.org/10.1007/978-1-4939-7366-8_3
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