Computational Analysis of Protein Tunnels and Channels

Part of the Methods in Molecular Biology book series (MIMB, volume 1685)


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.

Key words

Binding Protein Tunnel Channel Gate Rational design Software CAVER Transport 



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.


  1. 1.
    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 421–464Google Scholar
  2. 2.
    Gora A, Brezovsky J, Damborsky J (2013) Gates of enzymes. Chem Rev 113:5871–5923CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Kingsley LJ, Lill MA (2015) Substrate tunnels in enzymes: structure-function relationships and computational methodology. Proteins 83:599–611CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    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 287:29062–29074CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Pavlova M, Klvana M, Prokop Z et al (2009) Redesigning dehalogenase access tunnels as a strategy for degrading an anthropogenic substrate. Nat Chem Biol 5:727–733CrossRefPubMedGoogle Scholar
  6. 6.
    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 278:52622–52628CrossRefPubMedGoogle Scholar
  7. 7.
    Prokop Z, Sato Y, Brezovsky J et al (2010) Enantioselectivity of haloalkane dehalogenases and its modulation by surface loop engineering. Angew Chem Int Ed 49:6111–6115CrossRefGoogle Scholar
  8. 8.
    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 52:1959–1963CrossRefGoogle Scholar
  9. 9.
    Liskova V, Bednar D, Prudnikova T et al (2015) Balancing the stability–activity trade-off by fine-tuning dehalogenase access tunnels. ChemCatChem 7:648–659CrossRefGoogle Scholar
  10. 10.
    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:e1002708CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Sehnal D, Svobodova Varekova R, Berka K et al (2013) MOLE 2.0: advanced approach for analysis of biomacromolecular channels. J Cheminform 5:39CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Yaffe E, Fishelovitch D, Wolfson HJ et al (2008) MolAxis: efficient and accurate identification of channels in macromolecules. Proteins 73:72–86CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Masood TB, Sandhya S, Chandra N et al (2015) CHEXVIS: a tool for molecular channel extraction and visualization. BMC Bioinformatics 16:119CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Kim J-K, Cho Y, Lee M et al (2015) BetaCavityWeb: a webserver for molecular voids and channels. Nucleic Acids Res 43:W413–W418CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Brezovsky J, Chovancova E, Gora A et al (2013) Software tools for identification, visualization and analysis of protein tunnels and channels. Biotechnol Adv 31:38–49CrossRefPubMedGoogle Scholar
  16. 16.
    Kingsley LJ, Lill MA (2014) Ensemble generation and the influence of protein flexibility on geometric tunnel prediction in cytochrome P450 enzymes. PLoS One 9:e99408CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    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 30:2684–2685CrossRefPubMedGoogle Scholar
  18. 18.
    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–W383CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Case DA, Cheatham TE, Darden T et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Berendsen HJC, van der Spoel D, van Drunen R (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91:43–56CrossRefGoogle Scholar
  21. 21.
    Brooks BR, Bruccoleri RE, Olafson BD et al (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4:187–217CrossRefGoogle Scholar
  22. 22.
    Meyer T, D’Abramo M, Hospital A et al (2010) MoDEL (molecular dynamics extended library): a database of atomistic molecular dynamics trajectories. Structure 18:1399–1409CrossRefPubMedGoogle Scholar
  23. 23.
    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 23:209–219PubMedGoogle Scholar
  24. 24.
    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 15:656–667CrossRefPubMedGoogle Scholar
  25. 25.
    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–W118CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    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 27:2083–2088CrossRefPubMedGoogle Scholar
  27. 27.
    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–W589CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    UniProt Consortium (2015) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212CrossRefGoogle Scholar
  29. 29.
    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–D489CrossRefPubMedGoogle Scholar
  30. 30.
    Pravda L, Berka K, Svobodova Varekova R et al (2014) Anatomy of enzyme channels. BMC Bioinformatics 15:379CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Laskowski RA, Swindells MB (2011) LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 51:2778–2786CrossRefPubMedGoogle Scholar
  32. 32.
    Stierand K, Rarey M (2010) Drawing the PDB: protein-ligand complexes in two dimensions. ACS Med Chem Lett 1:540–545CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Sobolev V, Sorokine A, Prilusky J et al (1999) Automated analysis of interatomic contacts in proteins. Bioinformatics 15:327–332CrossRefPubMedGoogle Scholar
  34. 34.
    Sebestova E, Bendl J, Brezovsky J et al (2014) Computational tools for designing smart libraries. Methods Mol Biol 1179:291–314CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2018

Authors and Affiliations

  1. 1.Loschmidt Laboratories, Department of Experimental BiologyResearch Centre for Toxic Compounds in the Environment RECETOX, Faculty of Science, Masaryk UniversityBrnoCzech Republic
  2. 2.Human Computer Interaction LaboratoryFaculty of Informatics, Masaryk UniversityBrnoCzech Republic

Personalised recommendations