Journal of Molecular Modeling

, Volume 17, Issue 1, pp 97–109 | Cite as

Computational characterization of how the VX nerve agent binds human serum paraoxonase 1

  • Steven Z. Fairchild
  • Matthew W. Peterson
  • Adel Hamza
  • Chang-Guo Zhan
  • Douglas M. Cerasoli
  • Wenling E. Chang
Original Paper

Abstract

Human serum paraoxonase 1 (HuPON1) is an enzyme that can hydrolyze various chemical warfare nerve agents including VX. A previous study has suggested that increasing HuPON1’s VX hydrolysis activity one to two orders of magnitude would make the enzyme an effective countermeasure for in vivo use against VX. This study helps facilitate further engineering of HuPON1 for enhanced VX-hydrolase activity by computationally characterizing HuPON1’s tertiary structure and how HuPON1 binds VX. HuPON1’s structure is first predicted through two homology modeling procedures. Docking is then performed using four separate methods, and the stability of each bound conformation is analyzed through molecular dynamics and solvated interaction energy calculations. The results show that VX’s lone oxygen atom has a strong preference for forming a direct electrostatic interaction with HuPON1’s active site calcium ion. Various HuPON1 residues are also detected that are in close proximity to VX and are therefore potential targets for future mutagenesis studies. These include E53, H115, N168, F222, N224, L240, D269, I291, F292, and V346. Additionally, D183 was found to have a predicted pKa near physiological pH. Given D183’s location in HuPON1’s active site, this residue could potentially act as a proton donor or accepter during hydrolysis. The results from the binding simulations also indicate that steered molecular dynamics can potentially be used to obtain accurate binding predictions even when starting with a closed conformation of a protein’s binding or active site.

Keywords

Binding Homology model HuPON1 Steered molecular dynamics Solvated interaction energy VX 

Notes

Acknowledgements

This research was supported by the Defense Threat Reduction Agency-Joint Science and Technology Office, Medical S&T Division. The opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the U.S. Army or the Department of Defense.

Supplementary material

894_2010_693_MOESM1_ESM.pdb (416 kb)
ESM 1 (PDB 416 kb)
894_2010_693_MOESM2_ESM.pdb (416 kb)
ESM 2 (PDB 416 kb)
894_2010_693_MOESM3_ESM.pdb (416 kb)
ESM 3 (PDB 416 kb)
894_2010_693_MOESM4_ESM.pdb (416 kb)
ESM 4 (PDB 416 kb)
894_2010_693_MOESM5_ESM.pdb (413 kb)
ESM 5 (PDB 412 kb)
894_2010_693_MOESM6_ESM.pdb (413 kb)
ESM 6 (PDB 412 kb)

References

  1. 1.
    Newmark J (2007) Nerve agents. Neurologist 13(1):20–32CrossRefGoogle Scholar
  2. 2.
    Yokoyama K, Yamada A, Mimura N (1996) Clinical profiles of patients with sarin poisoning after the Tokyo subway attack. Am J Med 100(5):586CrossRefGoogle Scholar
  3. 3.
    Cannard K (2006) The acute treatment of nerve agent exposure. J Neurol Sci 249(1):86–94CrossRefGoogle Scholar
  4. 4.
    Rastogi SK, Wenger GR, Mcmillan DE (1985) Effects of optical isomers of pentobarbital on behavior in rats maintained on either the D or the L optical isomer of methadone. Arch Int Pharmacodyn Ther 276(2):247–262Google Scholar
  5. 5.
    Kuo CL, La Du BN (1998) Calcium binding by human and rabbit serum paraoxonases-structural stability and enzymatic activity. Drug Metab Dispos 26(7):653–660Google Scholar
  6. 6.
    Yeung DT, Lenz DE, Cerasoli DM (2005) Analysis of active-site amino-acid residues of human serum paraoxonase using competitive substrates. FEBS J 272(9):2225–2230CrossRefGoogle Scholar
  7. 7.
    Rochu D, Chabriere E, Masson P (2007) Human paraoxonase: a promising approach for pre-treatment and therapy of organophosphorus poisoning. Toxicology 233(1–3):47–59CrossRefGoogle Scholar
  8. 8.
    Draganov DI, Teiber JF, Speelman A, Osawa Y, Sunahara R, La Du BN (2005) Human paraoxonases (PON1, PON2, and PON3) are lactonases with overlapping and distinct substrate specificities. J Lipid Res 46(6):1239–1247CrossRefGoogle Scholar
  9. 9.
    Khersonsky O, Tawfik DS (2005) Structure-reactivity studies of serum paraoxonase PON1 suggest that its native activity is lactonase. Biochemistry 44(16):6371–6382CrossRefGoogle Scholar
  10. 10.
    Josse D, Lockridge O, Xie WH, Bartels F, Schopfer LM, Masson P (2001) The active site of human paraoxonase (PON1). J Appl Toxicol 21(suppl 1):S7–S11CrossRefGoogle Scholar
  11. 11.
    Yeung DT, Josse D, Nicholson JD, Khanal A, McAndrew CW, Bahnson BJ, Lenz DE, Cerasoli DM (2004) Structure/function analyses of human serum paraoxonase (HuPON1) mutants designed from a DFPase-like homology model. BBA-Proteins Proteomics 1702(1):67–77CrossRefGoogle Scholar
  12. 12.
    Hu X, Jiang X, Lenz DE, Cerasoli DM, Wallqvist A (2009) In silico analyses of substrate interactions with human serum paraoxonase 1. Proteins 75(2):486–498CrossRefGoogle Scholar
  13. 13.
    Harel M, Aharoni A, Gaidukov L, Brumshtein B, Khersonsky O, Meged R, Dvir H, Ravelli RBG, McCarthy A, Toker L, Silman I, Sussman JL, Tawfik DS (2004) Structure and evolution of the serum paraoxonase family of detoxifying and anti-atherosclerotic enzymes. Nat Struct Mol Biol 11(5):412–419CrossRefGoogle Scholar
  14. 14.
    Lee MS, Yeh I, Zavajevski N, Wilson P, Reifman J (2006) A software pipeline for protein structure prediction. In Proceedings of the 25th Army Science Conference, Orlando, FLGoogle Scholar
  15. 15.
    Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9:40CrossRefGoogle Scholar
  16. 16.
    Zhang Y (2007) Template-based modeling and free modeling by I-TASSER in CASP7. Proteins 69(suppl 8):108–117CrossRefGoogle Scholar
  17. 17.
    Sali A, Blundell TL (1993) Comparative protein modeling by satisfaction of spatial restraints. J Mol Biol 234(3):779–815CrossRefGoogle Scholar
  18. 18.
    Fiser A, Do RKG, Sali A (2000) Modeling of loops in protein structures. Protein Sci 9(9):1753–1773CrossRefGoogle Scholar
  19. 19.
    Moustakas DT, Lang PT, Pegg S, Pettersen E, Kuntz ID, Brooijmans N, Rizzo RC (2006) Development and validation of a modular, extensible docking program: DOCK5. J Comput-Aided Mol Des 20(10-11):601–619CrossRefGoogle Scholar
  20. 20.
    Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19(14):1639–1662CrossRefGoogle Scholar
  21. 21.
    Huey R, Morris GM, Olson AJ, Goodsell DS (2007) A semiempirical free energy force field with charge-based desolvation. J Comput Chem 28(6):1145–1152CrossRefGoogle Scholar
  22. 22.
    McGann MR, Almond HR, Nicholls A, Grant JA, Brown FK (2003) Gaussian docking functions. Biopolymers 68(1):76–90CrossRefGoogle Scholar
  23. 23.
    OEChem, version 1.3.4 (2005) OpenEye Scientific Software, Inc., Santa Fe, NM, USA, www.eyesopen.com
  24. 24.
    Osterberg F, Morris GM, Sanner MF, Olson AJ, Goodsell DS (2002) Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins 46(1):34–40CrossRefGoogle Scholar
  25. 25.
    Pavani P, Mangala SD, Murthy JVVS, Babu AP (2008) Protein-ligand interaction studies on 2, 4, 6-trisubstituted triazine derivatives as anti-malarial DHFR agents using AutoDock. Res J Biotechnol 3(3):18–23Google Scholar
  26. 26.
    Zheng F, Yang W, Ko MC, Liu J, Cho H, Gao D, Tong M, Tai HH, Woods JH, Zhan CG (2008) Most efficient cocaine hydrolase designed by virtual screening of transition states. J Am Chem Soc 130(36):12148–12155CrossRefGoogle Scholar
  27. 27.
    Tavori H, Khatib S, Aviram M, Vaya J (2008) Characterization of the PON1 active site using modeling simulation, in relation to PON1 lactonase activity. Bioorg Med Chem 16(15):7504–7509CrossRefGoogle Scholar
  28. 28.
    Guvench G, MacKerell AD Jr (2009) Computational evaluation of protein-small molecule binding. Curr Opin Struct Biol 19(1):56–61CrossRefGoogle Scholar
  29. 29.
    Naim M, Bhat S, Rankin KN, Dennis S, Chowdhury SF, Siddiqi I, Drabik P, Sulea T, Bayly CI, Jakalian A, Purisima EO (2007) Solvated interaction energy (SIE) for scoring protein-ligand binding affinities. 1. Exploring the parameter space. J Chem Inf Model 47(1):122–133CrossRefGoogle Scholar
  30. 30.
    Cui Q, Sulea T, Schrag JD, Munger C, Hung MN, Naim M, Cygler M, Purisima EO (2008) Molecular dynamics-solvated interaction energy studies of protein-protein interactions: The MP1-p14 scaffolding complex. J Mol Biol 379(4):787–802CrossRefGoogle Scholar
  31. 31.
    Izrailev S, Stepaniants S, Isralewitz B, Kosztin D, Lu H, Molnar F, Wriggers W, Schulten K (1998) Steered molecular dynamics. In: Deuflhard P, Hermans J, Leimkuhler B, Mark AE, Reich S, Skeel RD (eds) Computational molecular dynamics: challenges, methods, ideas. Lecture notes in computational science and engineering vol 4. Springer, Berlin, pp 39–65Google Scholar
  32. 32.
    Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL (2008) GenBank. Nucleic Acids Res 36(Sp. Iss. SI):D25–D30Google Scholar
  33. 33.
    Gaidukov L, Tawfik DS (2005) High affinity, stability, and lactonase activity of serum paraoxonase PON1 anchored on HDL with ApoA-I. Biochemistry 44(35):11843–11854CrossRefGoogle Scholar
  34. 34.
    Gordon JC, Myers JB, Folta T, Shoja V, Heath LS, Onufriev A (2005) H++: a server for estimating pKas and adding missing hydrogens to macromolecules. Nucleic Acids Res 33(suppl 2):W368–W371CrossRefGoogle Scholar
  35. 35.
    Anandakrishnan R, Onufriev A (2008) Analysis of basic clustering algorithms for numerical estimation of statistical averages in biomolecules. J Comput Biol 15(2):165–184CrossRefGoogle Scholar
  36. 36.
    Case DA, Cheatham TE, Darden T, Gohlke H, Luo R, Merz KM, Onufriev A, Simmering C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26(16):1668–1688CrossRefGoogle Scholar
  37. 37.
    Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Comparison of multiple amber force fields and development of improved protein backbone parameters. Proteins: Struct Funct Bioinf 65(3):712–725CrossRefGoogle Scholar
  38. 38.
    Hawkins GD, Cramer CJ, Truhlar DG (1995) Pairwise solute descreening of solute charges from a dielectric medium. Chem Phys Lett 246(1–2):122–129CrossRefGoogle Scholar
  39. 39.
    Hawkins GD, Cramer CJ, Truhlar DG (1996) Parametrized models of aqueous free energies of solvation based on pairwise descreening of solute atomic charges from a dielectric medium. J Phys Chem 100(51):19824–19839CrossRefGoogle Scholar
  40. 40.
    Jorgensen WL (1981) Quantum and statistical mechanical studies of liquids. 10. transferable intermolecular potential functions for water, alcohols, and ethers. Application to liquid water. J Am Chem Soc 103(2):335–340CrossRefGoogle Scholar
  41. 41.
    Laskowski RA, Macarthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26(Part 2):283–291CrossRefGoogle Scholar
  42. 42.
    Morris AL, Macarthur MW, Hutchinson EG, Thornton JM (1992) Stereochemical quality of protein-structure coordinates. Proteins 12(4):345–364CrossRefGoogle Scholar
  43. 43.
    Marti-Renom MA, Stuart AC, Fiser A, Sanchez R, Melo F, Sali A (2000) Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biom 29:291–325CrossRefGoogle Scholar
  44. 44.
    Wang JM, Cieplak P, Kollman PA (2000) How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J Comput Chem 21(12):1049–1074CrossRefGoogle Scholar
  45. 45.
    Ordentlich A, Barak D, Sod-Moriah G, Kaplan D, Mizrahi D, Segall Y, Kronman C, Karton Y, Lazar A, Marcus D, Velan B, Shafferman A (2005) The role of AChE active site gorge in determining stereoselectivity of charged and noncharged VX enantiomers. Chem Biol Interact 157(Sp. Iss. SI):191–198CrossRefGoogle Scholar
  46. 46.
    Anderson E, Veith GD, Weininger D (1987) Smiles: a line notation and computerized interpreter for chemical structures. Technical report U.S. EPA, Environmental Research Laboratory, Duluth, MNGoogle Scholar
  47. 47.
  48. 48.
    Jakalian A, Bush BL, Jack DB, Bayly CI (2000) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method. J Comput Chem 21(2):132–146CrossRefGoogle Scholar
  49. 49.
    Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J Comput Chem 23(16):1623–1641CrossRefGoogle Scholar
  50. 50.
    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Mont-gomery JA Jr, Vreven T, Kudin KN, Burant JC, Millam JM, Iyengar SS, Tomasi J, Barone V, Mennucci B, Cossi M, Scalmani G, Rega N, Petersson GA, Nakatsuji H, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Klene M, Li X, Knox JE, Hratchian HP, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Ayala PY, Morokuma K, Voth GA, Salvador P, Dannenberg JJ, Zakrzewski VG, Dapprich S, Daniels AD, Strain MC, Farkas O, Malick DK, Rabuck AD, Raghavachari K, Foresman JB, Ortiz JV, Cui Q, Baboul AG, Clifford S, Cioslowski J, Ste-fanov BB, Liu G, Liashenko A, Piskorz P, Komaromi I, Martin RL, Fox DJ, Keith T, Al-Laham MA, Peng CY, Nanayakkara A, Challacombe M, Gill PMW, Johnson B, Chen W, Wong MW, Gonzalez C, Pople JA (2004) Gaussian 03, Revision C.02. Gaussian, Inc, Wallingford, CTGoogle Scholar
  51. 51.
    Cieplak P, Cornell WD, Bayly C, Kollman PA (1995) Application of the multimolecule and multiconformational RESP methodology to biopolymers-charge derivation for DNA, RNA, and proteins. J Comp Chem 16(11):1357–1377CrossRefGoogle Scholar
  52. 52.
    Bayly CI, Cieplak P, Cornell WD, Kollman PA (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges-the RESP model. J Phys Chem 97(40):10269–10280CrossRefGoogle Scholar
  53. 53.
    Case DA, Darden TA, Cheatham TE, Simmerling CL, Wang J, Duke RE, Luo R, Merz KM, Pearlman DA, Crowley M, Walker RC, Zhang W, Wang B, Hayik S, Roitberg A, Seabra G, Wong KF, Paesani F, Wu X, Brozell S, Tsui V, Gohlke H, Yang L, Tan C, Mongan J, Hornak V, Cui G, Beroza P, Mathews DH, Schafmeister C, Ross WS, Kollman PA (2006) AMBER 10. University of California, San FranciscoGoogle Scholar
  54. 54.
    Marsili M, Gasteiger J (1980) Pi-charge distributions from molecular topology and pi-orbital electronegativity. Croat Chem Acta 53:601–614Google Scholar
  55. 55.
    Marsili M, Gasteiger J (1980) Iterative partial equalization of orbital electronegativity-a rapid access to atomic charges. Tetrahedron 36:3219–3228CrossRefGoogle Scholar
  56. 56.
    Sanner MF (1999) Python: a programming language for software integration and development. J Mol Graphics Mod 17:57–61Google Scholar
  57. 57.
    Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF chimera-a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612CrossRefGoogle Scholar
  58. 58.
    Richards FM (1977) Areas, volumes, packing and protein structure. Annu Rev Biophys Bioeng 6:151–176CrossRefGoogle Scholar
  59. 59.
    Harvey SC (1989) Treatment of electrostatic effects in macromolecular modeling. Proteins 5(1):78–92CrossRefGoogle Scholar
  60. 60.
    Guenot J, Kollman PA (1992) Molecular-dynamics studies of a DNA-binding protein: 2. An evaluation of implicit and explicit solvent models for the molecular-dynamics simulation of the Escherichia-coli trp repressor. Protein Sci 1(9):1185–1205CrossRefGoogle Scholar
  61. 61.
    Pan YM, Gao DQ, Yang WC, Cho H, Yang GF, Tai HH, Zhan CG (2005) Computational redesign of human butyrylcholinesterase for anticocaine medication. Proc Natl Acad Sci USA 102(46):16656–16661CrossRefGoogle Scholar
  62. 62.
    Zhang T, Hamza A, Cao X, Wang B, Yu S, Zhan CG, Sun D (2008) A novel Hsp90 inhibitor to disrupt Hsp90/Cdc37 complex against pancreatic cancer cells. Mol Cancer Ther 7(1):162–170CrossRefGoogle Scholar
  63. 63.
    Bargagna-Mohan P, Hamza A, Kim Y, Ho YK, Mor-Valknin N, Wendschlag N, Li J, Evans RM, Markovitz DM, Zhan CG, Kim KB, Mohan R (2007) The tumor inhibitor and antiangiogenic agent withaferin A targets the intermediate filament protein vimentin. Chem Biol 14(6):623–634CrossRefGoogle Scholar
  64. 64.
    Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis 18(15):2714–2723CrossRefGoogle Scholar
  65. 65.
    Kollman PA, Massova I, Reyes C, Kuhn B, Huo SH, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Accounts Chem Res 33(12):889–897CrossRefGoogle Scholar
  66. 66.
    Brenke R, Kozakov D, Chuang GY, Beglov D, Hall D, Landon MR, Mattos C, Vajda S (2009) Fragment-based identification of druggable ‘hot spots’ of proteins using Fourier domain correlation techniques. Bioinformatics 25(5):621–627CrossRefGoogle Scholar
  67. 67.
    Kortvelyesi T, Dennis S, Silberstein M, Brown L, Vajda S (2003) Algorithms for computational solvent mapping of proteins. Proteins 51(3):340–351CrossRefGoogle Scholar
  68. 68.
    Silberstein M, Dennis S, Brown L, Kortvelyesi T, Clodfelter K, Vajda S (2003) Identification of substrate binding sites in enzymes by computational solvent mapping. J Mol Biol 332(5):1095–1113CrossRefGoogle Scholar
  69. 69.
    Blum MM, Timperley CM, Williams GR, Thiermann H, Worek F (2008) Inhibitory potency against human acetylcholinesterase and enzymatic hydrolysis of fluorogenic nerve agent mimics by human paraoxonase 1 and squid diisopropyl fluorophosphatase. Biochemistry 47(18):5216–5224Google Scholar
  70. 70.
    Khersonsky O, Tawfik DS (2006) The histidine 115-histidine 134 dyad mediates the lactonase activity of mammalian serum paraoxonases. J Biol Chem 281(11):7649–7656CrossRefGoogle Scholar
  71. 71.
    Blum MM, Löhr F, Richardt A, Rüterjans H, Chen JCH (2006) Binding of a designed substrate analogue to diisopropyl fluorophosphatase: Implications for the phosphotriesterase mechanism. J Am Chem Soc 128(39):12750–12757Google Scholar
  72. 72.
    DeLano WL (2002) The PyMOL molecular graphics system. http://www.pymol.org

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Steven Z. Fairchild
    • 1
  • Matthew W. Peterson
    • 2
  • Adel Hamza
    • 3
  • Chang-Guo Zhan
    • 3
  • Douglas M. Cerasoli
    • 4
  • Wenling E. Chang
    • 5
  1. 1.The MITRE CorporationMcLeanUSA
  2. 2.The MITRE CorporationBedfordUSA
  3. 3.University of Kentucky College of PharmacyLexingtonUSA
  4. 4.US Army Medical Research Institute of Chemical DefenseAberdeen Proving GroundsUSA
  5. 5.The MITRE CorporationSan DiegoUSA

Personalised recommendations