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Computational Methods for Modeling Metalloproteins

  • Martin T. Stiebritz
  • Yilin Hu
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1876)

Abstract

Metalloproteins are challenging objects if we want to investigate their chemical reactivity with theoretical approaches such as density functional theory (DFT). The complexity of these biomolecules often requires us to find a compromise between accuracy and feasibility, one that is tailored to the questions we set out to answer. In this chapter, we discuss computational approaches to studying chemical reactions in metalloproteins and how to utilize the information hidden in homologous proteins.

Key words

Density functional theory (DFT) Broken symmetry Homology modeling CO2 reduction Nitrogenase Fe proteins Fe4S4 clusters 

Notes

Acknowledgments

The authors are supported by the National Science Foundation CAREER award CHE-1651398 (to Y.H.).

References

  1. 1.
    van Duin ACT, Dasgupta S, Lorant F et al (2001) ReaxFF: a reactive force field for hydrocarbons. J Phys Chem A 105:9396–9409CrossRefGoogle Scholar
  2. 2.
    Hohenberg P, Kohn W (1964) Inhomogeneous electron gas. Phys Rev 136:B864–B871CrossRefGoogle Scholar
  3. 3.
    Sham LJ, Kohn W (1964) Self-consistent equations including exchange and correlation effects. Phys Rev 140:A1133–A1138Google Scholar
  4. 4.
    Harvey JN (2004) DFT computation of relative spin-state energetics of transition metal compounds. Struct Bond 112:151–183CrossRefGoogle Scholar
  5. 5.
    Siegbahn PEM, Himo F (2011) The quantum chemical cluster approach for modeling enzyme reactions. Wiley Interdiscip Rev Comput Mol Sci 1:323–336CrossRefGoogle Scholar
  6. 6.
    O’Boyle NM, Banck M, James CA et al (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33CrossRefGoogle Scholar
  7. 7.
    Sondergaard CR, Olsson MHM, Rostkowski M et al (2011) Improved treatment of ligands and coupling effects in empirical calculation and rationalization of pKa values. J Chem Theory Comput 7:2284–2295CrossRefGoogle Scholar
  8. 8.
    Olsson MHM, Sondergaard CR, Rostowski M et al (2011) PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J Chem Theory Comput 7:525–537CrossRefGoogle Scholar
  9. 9.
    Klamt A, Schüürmann G (1993) COSMO: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. J Chem Soc Perkin Trans 2:799–805CrossRefGoogle Scholar
  10. 10.
    Jacob CR, Neugebauer J (2014) Subsystem density-functional theory. WIREs Comput Mol Sci 4:325–362CrossRefGoogle Scholar
  11. 11.
    Slater JC (1951) A simplification of the Hartree-Fock method. Phys Rev 81:385–390CrossRefGoogle Scholar
  12. 12.
    Vosko SH, Wilk L, Nusair M (1980) Accurate spin-dependent electron liquid correlation energies for local spin density calculations: a critical analysis. Can J Phys 81:1200–1211CrossRefGoogle Scholar
  13. 13.
    Perdew JP, Burke K, Ernzerhof M (1996) Generalized gradient approximation made simple. Phys Rev Lett 77:3865–3868CrossRefGoogle Scholar
  14. 14.
    Becke AD (1988) Density-functional exchange-energy approximation with correct asymptotic behavior. Phys Rev A 38:3098–3100CrossRefGoogle Scholar
  15. 15.
    Tao J, Perdew JP, Staroverov VN et al (2003) Climbing the density functional ladder: nonempirical meta-generalized gradient approximation designed for molecules and solids. Phys Rev Lett 91:146401CrossRefGoogle Scholar
  16. 16.
    Zhao Y, Truhlar DG (2006) The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent nteractions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals. Theor Chem Accounts 120:215–241CrossRefGoogle Scholar
  17. 17.
    Becke AD (1993) Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys 98:5648–5652CrossRefGoogle Scholar
  18. 18.
    Perdew JP, Zupan S, Blaha P (1999) Accurate density functional with correct formal properties: a step beyond the generalized gradient approximation. Phys Rev Lett 82:2544–2547CrossRefGoogle Scholar
  19. 19.
    Perdew JP, Tao J, Staroverov VN et al (2004) Meta-generalized gradient approximation: explanation of a realistic nonempirical density functional. J Chem Phys 120:6898–6911CrossRefGoogle Scholar
  20. 20.
    Perdew JP, Ernzerhof M, Burke K (1996) Rationale for mixing exact exchange with density functional approximations. J Chem Phys 105:9982–9985CrossRefGoogle Scholar
  21. 21.
    Grimme S (2004) Accurate description of van der Waals complexes by density functional theory including empirical corrections. J Comput Chem 25:1463–1473CrossRefGoogle Scholar
  22. 22.
    Grimme S (2006) Semiempirical GGA-type density functional constructed with a long-range dispersion contribution. J Comput Chem 27:1787–1799CrossRefGoogle Scholar
  23. 23.
    Grimme S, Antony J, Ehrlich S et al (2010) A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J Chem Phys 132:154104CrossRefGoogle Scholar
  24. 24.
    Valiev M, Bylaska EJ, Govind N et al (2010) NWChem: a comprehensive and scalable open-source solution for large scale molecular simulations. Comput Phys Commun 181:1477CrossRefGoogle Scholar
  25. 25.
    te Velde F, Bickelhaupt FM, Baerends EJ et al (2001) Chemistry with ADF. J Comput Chem 22:931–967CrossRefGoogle Scholar
  26. 26.
    Hoffmann R (1963) An extended Hückel theory. I. Hydrocarbons. J Chem Phys 39:1397–1412CrossRefGoogle Scholar
  27. 27.
    Frisch MJ, Trucks GW, Schlegel HB et al (2009) Gaussian 09, Revision E.01. Gaussian, Wallingford CTGoogle Scholar
  28. 28.
    Klamt A (1995) Conductor-like screening model for real solvents: a new approach to the quantitative calculation of solvation phenomena. J Phys Chem 99:2224–2235CrossRefGoogle Scholar
  29. 29.
    Mills F, Jónsson H, Schenter GK (1995) Reversible work transition state theory: application to dissociative adsorption of hydrogen. Surf Sci 324:305–337CrossRefGoogle Scholar
  30. 30.
    Peng C, Schlegel JB (1993) Combining synchronous transit and quasi-Newton methods for finding transition states. Israel J Chem 33:449–454CrossRefGoogle Scholar
  31. 31.
    Neese F (2012) The ORCA program system. Wiley Interdiscip Rev Comput Mol Sci 2:73–78CrossRefGoogle Scholar
  32. 32.
    Stiebritz MT, Hiller CJ, Sickerman NS et al (2018) Ambient conversion of CO2 to hydrocarbons by biogenic and synthetic [Fe4S4] clusters. Nat Catal in pressGoogle Scholar
  33. 33.
    Noodleman J (1981) Valence bond description of antiferromagnetic coupling in transition metal dimers. J Chem Phys 74:5737–5743CrossRefGoogle Scholar
  34. 34.
    Noodleman J, Post D, Baerends E (1982) Symmetry breaking and ionization from symmetry equivalent inner shells, and lone pairs in Xα theory. Chem Phys 64:159–166CrossRefGoogle Scholar
  35. 35.
    Noodleman J, Peng CY, Case DA et al (1995) Orbital interactions, electron delocalization and spin coupling in iron-sulfur clusters. Coord Chem 144:199–244CrossRefGoogle Scholar
  36. 36.
    Lovell T, Li J, Liu T et al (2001) FeMo cofactor of nitrogenase: a density functional study of states MN, MOX, MR, and MI. J Am Chem Soc 123:12392–12410CrossRefGoogle Scholar
  37. 37.
    Rebelein JG, Stiebritz MT, Lee CC et al (2017) Activation and reduction of carbon dioxide by nitrogenase iron proteins. Nat Chem Biol 13:147–149CrossRefGoogle Scholar
  38. 38.
    Strop P, Takahara PM, Chiu H et al (2001) Crystal structure of the all-ferrous [4Fe-4S]0 form of the nitrogenase iron protein from Azotobacter vinelandii. Biochemistry 40:651–656CrossRefGoogle Scholar
  39. 39.
    Schäfer A, Huber C, Ahlrichs R (1994) Fully optimized contracted Gaussian basis sets of triple zeta valence quality for atoms Li to Kr. J Chem Phys 100:5829–5836CrossRefGoogle Scholar
  40. 40.
    Weigend F, Ahlrichs R (2005) Balanced basis sets of split alence, triple zeta valence and quadruple zeta valence quality for H to Rn: design and assessment of accuracy. Phys Chem Chem Phys 7:3297–3305CrossRefGoogle Scholar
  41. 41.
    Schäfer A, Horn H, Ahrichs R (1992) Fully optimized contracted Gaussian basis sets for atoms Li to Kr. J Chem Phys 97:2571–2577CrossRefGoogle Scholar
  42. 42.
    Eichkorn K, Weigend F, Treutler O et al (1997) Auxiliary basis sets for main row atoms and transition metals and their use to approximate coulomb potentials. Theor Chem Accounts 97:119–124CrossRefGoogle Scholar
  43. 43.
    Weigend F (2006) Accurate coulomb-fitting basis sets for H to Rn. Phys Chem Chem Phys 8:1057–1065CrossRefGoogle Scholar
  44. 44.
    Martί-Renom MA, Stuart AC, Fiser A et al (2000) Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 29:291–325CrossRefGoogle Scholar
  45. 45.
    Lesk AM, Chothia C (1980) How different amino acid sequences determine similar protein structures: the structure and evolutionary dynamics of the globins. J Mol Biol 136:225–270CrossRefGoogle Scholar
  46. 46.
    Arnold K, Bordoli L, Kopp J et al (2006) The SWISS-MODEL workspace: a web based environment for protein structure homology modelling. Bioinformatics 22:195–201CrossRefGoogle Scholar
  47. 47.
    Guex N, Peitsch MC, Schwede T (2009) Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: a historical perspective. Electrophoresis 30:S162–S173CrossRefGoogle Scholar
  48. 48.
    Kiefer F, Arnold K, Künzli M et al (2009) The Swiss-model repository and associated resources. Nucleic Acids Res 37:D387–D392CrossRefGoogle Scholar
  49. 49.
    Biasini M, Bienert S, Waterhouse A et al (2014) SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 42:W252–W258CrossRefGoogle Scholar
  50. 50.
    Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234:779–815CrossRefGoogle Scholar
  51. 51.
    Webb B, Sali A (2014) Comparative protein structure modeling using modeller. Curr Protoc Bioinformatics 47:5.6.1–5.6.32CrossRefGoogle Scholar
  52. 52.
    Benkert P, Tosatto SC, Schomburg D (2008) QMEAN: a comprehensive scoring function for model quality assessment. Proteins 71:261–277CrossRefGoogle Scholar
  53. 53.
    Li L, Li C, Sarkar S et al (2012) DelPhi: a comprehensive suite for DelPhi software and associated resources. BMC Biophys 5:9CrossRefGoogle Scholar
  54. 54.
    Baker NA, Sept D, Joseph S et al (2001) Electrostatics of nanosystems: application to microtubules and the ribosome. Proc Natl Acad Sci U S A 98:10037–10041CrossRefGoogle Scholar
  55. 55.
    Finkelmann AR, Stiebritz MT, Reiher M (2013) Electric-field effects on the [FeFe]-hydrogenase active site. Chem Commun 49:8099–8101CrossRefGoogle Scholar
  56. 56.
    Dolinsky TJ, Nielsen JE, McCammon JA et al (2004) PDB2PQR: an automated pipeline for the setup, execution, and analysis of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Res 32:W665–W667CrossRefGoogle Scholar
  57. 57.
    Sitkoff D, Sharp KA, Honig B (1994) Accurate calculation of hydration free energies using macroscopic solvent models. J Phys Chem 98:1978–1988CrossRefGoogle Scholar
  58. 58.
    Warshel A, Levitt M (1976) Theoretical studies of enzymatic reactions: dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme. J Mol Biol 103:227–249CrossRefGoogle Scholar
  59. 59.
    Field MJ, Bash PA, Karplus M (1990) A combined quantum mechanical and molecular mechanical potential for molecular dynamics simulations. J Comput Chem 11:700–733CrossRefGoogle Scholar
  60. 60.
    Gao J (1996) Hybrid quantum and molecular mechanical simulations: an alternative avenue to solvent ffects in organic chemistry. Acc Chem Res 29:298–305CrossRefGoogle Scholar
  61. 61.
    Svensson M, Humbel S, Froese RDJ et al (1996) ONIOM: a multilayered integrated MO + MM method for geometry optimizations and single point energy predictions. A test for Diels-Alder reactions and Pt(P(t-Bu)3)2 + H2 oxidative addition. J Phys Chem 100:19357–19363CrossRefGoogle Scholar
  62. 62.
    Metz S, Kästner J, Sokol AA et al (2014) ChemShell – a molecular software package for QM/MM simulations. WIREs Comput Mol Sci 4:101–110CrossRefGoogle Scholar
  63. 63.
    Ryde U (1996) The coordination of the catalytic zinc ion in alcohol dehydrogenase studied by combined quantum chemical and molecular mechanical calculations. J Comput Aided Mol Des 10:153–164CrossRefGoogle Scholar
  64. 64.
    Ryde U, Olsson MH (2001) Structure, strain, and reorganization energy of blue copper models in the protein. Int J Quant Chem 81:335–347CrossRefGoogle Scholar
  65. 65.
    Lin H, Truhlar DG (2007) QM/MM: what have we learned, where are we, and where do we go from here? Theor Chem Accounts 117:185–199CrossRefGoogle Scholar
  66. 66.
    Senn HM, Thiel W (2007) QM/MM studies of enzymes. Curr Opin Chem Biol 11:182–187CrossRefGoogle Scholar
  67. 67.
    Ryde U (2003) Combined quantum and molecular mechanics calculations on metalloproteins. Curr Opin Chem Biol 7:136–142CrossRefGoogle Scholar
  68. 68.
    Senn HM, Thiel W (2009) QM/MM methods for biomolecular systems. Angew Chem Int Ed Engl 48:1198–1229CrossRefGoogle Scholar
  69. 69.
    Case DA, Cerutti DS, Cheatham TE III et al (2017) Amber. University of California, San FranciscoGoogle Scholar
  70. 70.
    Salomon-Ferrer R, Case DA, Walker RC (2013) An overview of the Amber biomolecular simulation package. WIREs Comput Mol Sci 3:198–210CrossRefGoogle Scholar
  71. 71.
    Bayly CI, Cieplak P, Cornell WD et al (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP method. J Phys Chem 97:10269–10280CrossRefGoogle Scholar
  72. 72.
    Hoops SC, Anderson KW, Merz KM (1991) Force field design for metalloproteins. J Am Chem Soc 113:8262–8270CrossRefGoogle Scholar
  73. 73.
    Ryde U (1995) Molecular dynamics simulations of alcohol dehydrogenase with a four- or five-coordinate catalytic zinc ion. Proteins 21:40–56CrossRefGoogle Scholar
  74. 74.
    Seminario JM (1996) Calculation of intramolecular force fields from second-derivative tensors. Int J Quantum Chem 60:1271–1277CrossRefGoogle Scholar
  75. 75.
    Burger SK, Lacasse M, Verstraelen T et al (2012) Automated parametrization of AMBER force field terms from vibrational analysis with a focus on functionalizing dinuclear zinc(II) scaffolds. J Chem Theory Comput 8:554–562CrossRefGoogle Scholar
  76. 76.
    Nilsson K, Lecerof D, Sigfridsson E et al (2003) An automatic method to generate force-field parameters for hetero-compounds. Acta Crystallogr Sect D 59:274–289CrossRefGoogle Scholar
  77. 77.
    Vanduyfhuys L, Vandenbrande S, Verstraelen T et al (2015) QuickFF: a program for a quick and easy derivation of force fields for metal-organic frameworks from ab initio input. J Comp Chem 36:1015–1027CrossRefGoogle Scholar
  78. 78.
    Zheng S, Tang Q, He J et al (2016) VFFDT: a new software for preparing AMBER force field parameters for metal-containing molecular systems. J Chem Inf Model 56:811–818CrossRefGoogle Scholar
  79. 79.
    Li P, Merz KM (2016) MCBP.py: a python based metal center parameter builder. J Chem Inf Model 56:599–604CrossRefGoogle Scholar
  80. 80.
    Stiebritz MT (2015) MetREx: a protein design approach for the exploration of sequence-reactivity relationships in metalloenzymes. J Comput Chem 36:553–563CrossRefGoogle Scholar
  81. 81.
    Stiebritz MT, Wengrzik S, Klein DL et al (2010) Computational design of a chain-specific tetracycline repressor heterodimer. J Mol Biol 403:371–385CrossRefGoogle Scholar
  82. 82.
    Stiebritz MT, Muller YA (2006) MUMBO: a protein-design approach to crystallographic model building and refinement. Acta Crystallogr Sect D 62:648–658CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Molecular Biology and BiochemistryUniversity of California, IrvineIrvineUSA

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