Computational prediction of ligand entry and egress paths in proteins has become an emerging topic in computational biology and has proven useful in fields such as protein engineering and drug design. Geometric tunnel prediction programs, such as Caver3.0 and MolAxis, are computationally efficient methods to identify potential ligand entry and egress routes in proteins. Although many geometric tunnel programs are designed to accommodate a single input structure, the increasingly recognized importance of protein flexibility in tunnel formation and behavior has led to the more widespread use of protein ensembles in tunnel prediction. However, there has not yet been an attempt to directly investigate the influence of ensemble size and composition on geometric tunnel prediction. In this study, we compared tunnels found in a single crystal structure to ensembles of various sizes generated using different methods on both the apo and holo forms of cytochrome P450 enzymes CYP119, CYP2C9, and CYP3A4. Several protein structure clustering methods were tested in an attempt to generate smaller ensembles that were capable of reproducing the data from larger ensembles. Ultimately, we found that by including members from both the apo and holo data sets, we could produce ensembles containing less than 15 members that were comparable to apo or holo ensembles containing over 100 members. Furthermore, we found that, in the absence of either apo or holo crystal structure data, pseudo-apo or –holo ensembles (e.g. adding ligand to apo protein throughout MD simulations) could be used to resemble the structural ensembles of the corresponding apo and holo ensembles, respectively. Our findings not only further highlight the importance of including protein flexibility in geometric tunnel prediction, but also suggest that smaller ensembles can be as capable as larger ensembles at capturing many of the protein motions important for tunnel prediction at a lower computational cost.
References
[1]
Biedermannova L, Prokop Z, Gora A, Chovancova E, Kovacs M, 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. doi: 10.1074/jbc.m112.377853
[2]
Brouk M, Derry N-L, Shainsky J, Zelas ZB-B, Boyko Y, et al. (2010) The influence of key residues in the tunnel entrance and the active site on activity and selectivity of toluene-4-monooxygenase. J Mol Catal, B Enzym 66: 72–80. doi: 10.1016/j.molcatb.2010.03.006
[3]
Buch I, Giorgino T, De Fabritiis G (2011) Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc Natl Acad Sci USA 108: 10184–10189. doi: 10.1073/pnas.1103547108
[4]
Setny P, Baron R, Michael Kekenes-Huskey P, McCammon JA, Dzubiella J (2013) Solvent fluctuations in hydrophobic cavity–ligand binding kinetics. Proc Natl Acad Sci USA 110: 1197–1202. doi: 10.1073/pnas.1221231110
[5]
Vargiu AV, Nikaido H (2012) Multidrug binding properties of the AcrB efflux pump characterized by molecular dynamics simulations. Proc Natl Acad Sci USA 109: 20637–20642. doi: 10.1073/pnas.1218348109
[6]
Pavlova M, Klvana M, Prokop Z, Chaloupkova R, Banas P, et al. (2009) Redesigning dehalogenase access tunnels as a strategy for degrading an anthropogenic substrate. Nat Chem Biol 5: 727–733. doi: 10.1038/nchembio.205
[7]
Brezovsky J, Chovancova E, Gora A, Pavelka A, Biedermannova L, et al. (2013) Software tools for identification, visualization and analysis of protein tunnels and channels. Biotechnol Adv 31: 38–49. doi: 10.1016/j.biotechadv.2012.02.002
[8]
Ulmschneider MB, Bagnéris C, McCusker EC, DeCaen PG, Delling M, et al. (2013) Molecular dynamics of ion transport through the open conformation of a bacterial voltage-gated sodium channel. Proc Natl Acad Sci USA 110: 6364–6369. doi: 10.1073/pnas.1214667110
[9]
Najmanovich R, Kuttner J, Sobolev V, Edelman M (2000) Side-chain flexibility in proteins upon ligand binding. Proteins 39: 261–268. doi: 10.1002/(sici)1097-0134(20000515)39:3<261::aid-prot90>3.0.co;2-4
[10]
Stepankova V, Khabiri M, Brezovsky J, Pavelka A, Sykora J, et al. (2013) Expansion of Access Tunnels and Active-Site Cavities Influence Activity of Haloalkane Dehalogenases in Organic Cosolvents. ChemBioChem 14: 890–897. doi: 10.1002/cbic.201200733
[11]
Cojocaru V, Winn P, Wade R (2007) The ins and outs of cytochrome P450s. Biochim Biophys Acta 1770: 390–401. doi: 10.1016/j.bbagen.2006.07.005
[12]
Copeland RA, Pompliano DL, Meek TD (2006) Drug-target residence time and its implications for lead optimization. Nat Rev Drug Discov 5: 730–739. doi: 10.1038/nrd2082
[13]
Seifert A, Tatzel S, Schmid RD, Pleiss J (2006) Multiple molecular dynamics simulations of human p450 monooxygenase CYP2C9: The molecular basis of substrate binding and regioselectivity toward warfarin. Proteins 64: 147–155. doi: 10.1002/prot.20951
[14]
Otyepka M, Skopalik J, Anzenbacherova E, Anzenbacher P (2007) What common structural features and variations of mammalian P450s are known to date? Biochim Biophys Acta 1770: 376–389. doi: 10.1016/j.bbagen.2006.09.013
[15]
Hendrychova T, Berka K, Navratilova V, Anzenbacher P, Otyepka M (2012) Dynamics and hydration of the active sites of mammalian cytochromes P450 probed by molecular dynamics simulations. Curr Drug Metab 13: 177–189. doi: 10.2174/138920012798918408
[16]
Berka K, Paloncyová M, Anzenbacher P, Otyepka M (2013) Behavior of Human Cytochromes P450 on Lipid Membranes. J Phys Chem B 117: 11556–11564. doi: 10.1021/jp4059559
[17]
Schleinkofer K, Sudarko, Winn PJ, Lüdemann SK, Wade RC (2005) Do mammalian cytochrome P450s show multiple ligand access pathways and ligand channelling? EMBO reports 6: 584–589. doi: 10.1038/sj.embor.7400420
[18]
Yaffe E, Fishelovitch D, Wolfson H, Halperin D, Nussinov R (2008) MolAxis: efficient and accurate identification of channels in macromolecules. Proteins 73: 72–86. doi: 10.1002/prot.22052
[19]
Berka K, Hanak O, Sehnal D, Banas P, Navratilova V, et al. (2012) MOLEonline 2.0: interactive web-based analysis of biomacromolecular channels. Nucleic Acids Res 40: W222–W227. doi: 10.1093/nar/gks363
[20]
Totrov M, Abagyan R (2008) Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr Opin Struct Biol 18: 178–184. doi: 10.1016/j.sbi.2008.01.004
[21]
Korb O, Olsson TSG, Bowden SJ, Hall RJ, Verdonk ML, et al. (2012) Potential and Limitations of Ensemble Docking. J Chem Inf Model 52: 1262–1274. doi: 10.1021/ci2005934
[22]
Chovancova E, Pavelka A, Benes P, Strnad O, Brezovsky J, et al. (2012) CAVER 3.0: A Tool for the Analysis of Transport Pathways in Dynamic Protein Structures. PLoS Comput Biol 8: e1002708. doi: 10.1371/journal.pcbi.1002708
[23]
Oda A, Yamaotsu N, Hirono S (2005) New AMBER force field parameters of heme iron for cytochrome P450s determined by quantum chemical calculations of simplified models. J Comp Chem 26: 818–826. doi: 10.1002/jcc.20221
[24]
Word JM, Lovell SC, Richardson JS, Richardson DC (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol 285: 1735–1747. doi: 10.1006/jmbi.1998.2401
[25]
DA Case TAD TE, Cheatham III CL, Simmerling J, Wang RE, Duke R, et al. (2012) AMBER 12. University of California, San Francisco.
[26]
Schr？dinger L (Version 1.2r3pre) The PyMOL Molecular Graphics System.
[27]
Berendsen HJC, van der Spoel D, van Drunen R (1995) GROMACS: A message-passing parallel molecular dynamics implementation. Comput Phys 91: 43–56. doi: 10.1016/0010-4655(95)00042-e
[28]
Wang J, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25: 247–260. doi: 10.1016/j.jmgm.2005.12.005
[29]
Hess B, Bekker H, Berendsen HJC, Fraaije JGEM (1997) LINCS: A linear constraint solver for molecular simulations. J Comput Chem 18: 1463–1472. doi: 10.1002/(sici)1096-987x(199709)18:12<1463::aid-jcc4>3.3.co;2-l
[30]
Cheng LS, Amaro RE, Xu D, Li WW, Arzberger PW, et al. (2008) Ensemble-Based Virtual Screening Reveals Potential Novel Antiviral Compounds for Avian Influenza Neuraminidase. J Med Chem 51: 3878–3894. doi: 10.1021/jm8001197
[31]
Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-Pdb Viewer: An environment for comparative protein modeling. Electrophoresis 18: 2714–2723. doi: 10.1002/elps.1150181505
[32]
Haines DC, Tomchick DR, Machius M, Peterson JA (2001) Pivotal Role of Water in the Mechanism of P450BM-3？. Biochemistry-US 40: 13456–13465. doi: 10.1021/bi011197q
[33]
Cui Y-L, Zheng Q-C, Zhang J-L, Xue Q, Wang Y, et al. (2013) Molecular Dynamic Investigations of the Mutational Effects on Structural Characteristics and Tunnel Geometry in CYP17A1. Journal of Chem Inf Model doi: 10.1021/ci400553w
[34]
Cojocaru V, Balali-Mood K, Sansom MSP, Wade RC (2011) Structure and Dynamics of the Membrane-Bound Cytochrome P450 2C9. PLoS Comput Biol 7: e1002152. doi: 10.1371/journal.pcbi.1002152
[35]
Skopalik J, Anzenbacher P, Otyepka M (2008) Flexibility of human cytochromes P450: molecular dynamics reveals differences between CYPs 3A4, 2C9, and 2A6, which correlate with their substrate preferences. J Phys Chem B 112: 8165–8173. doi: 10.1021/jp800311c
[36]
Winn PJ, Lüdemann SK, Gauges R, Lounnas V, Wade RC (2002) Comparison of the dynamics of substrate access channels in three cytochrome P450s reveals different opening mechanisms and a novel functional role for a buried arginine. Proc Natl Acad Sci USA 99: 5361–5366. doi: 10.1073/pnas.082522999
Guskov A, Kern J, Gabdulkhakov A, Broser M, Zouni A, et al. (2009) Cyanobacterial photosystem II at 2.9-A resolution and the role of quinones, lipids, channels and chloride. Nat Struct Mol Biol 16: 334–342. doi: 10.1038/nsmb.1559
[39]
Caillat C, Topalis D, Agrofoglio LA, Pochet S, Balzarini J, et al. (2008) Crystal structure of poxvirus thymidylate kinase: An unexpected dimerization has implications for antiviral therapy. Proc Natl Acad Sci USA 105: 16900–16905. doi: 10.1073/pnas.0804525105
