1932

Abstract

Many remarkable molecular functions of proteins use their characteristic global and slow conformational dynamics through coupling of local chemical states in reaction centers with global conformational changes of proteins. To theoretically examine the functional processes of proteins in atomic detail, a methodology of quantum mechanical/molecular mechanical (QM/MM) free-energy geometry optimization is introduced. In the methodology, a geometry optimization of a local reaction center is performed with a quantum mechanical calculation on a free-energy surface constructed with conformational samples of the surrounding protein environment obtained by a molecular dynamics simulation with a molecular mechanics force field. Geometry optimizations on extensive free-energy surfaces by a QM/MM reweighting free-energy self-consistent field method designed to be variationally consistent and computationally efficient have enabled examinations of the multiscale molecular coupling of local chemical states with global protein conformational changes in functional processes and analysis and design of protein mutants with novel functional properties.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-physchem-052516-050827
2017-05-05
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/physchem/68/1/annurev-physchem-052516-050827.html?itemId=/content/journals/10.1146/annurev-physchem-052516-050827&mimeType=html&fmt=ahah

Literature Cited

  1. Wolf-Watz M, Thai V, Henzler-Wildman K, Hadjipavlou G, Eisenmesser EZ, Kern D. 1.  2004. Linkage between dynamics and catalysis in a thermophilic-mesophilic enzyme pair. Nat. Struct. Mol. Biol. 11:945–49 [Google Scholar]
  2. Henzler-Wildman KA, Lei M, Thai V, Kerns SJ, Karplus M, Kern D. 2.  2007. A hierarchy of timescales in protein dynamics is linked to enzyme catalysis. Nature 450:913–16 [Google Scholar]
  3. Boehr DD, McElheny D, Dyson HJ, Wright PE. 3.  2006. The dynamic energy landscape of dihydrofolate reductase catalysis. Science 313:1638–42 [Google Scholar]
  4. Boehr DD, McElheny D, Dyson HJ, Wright PE. 4.  2010. Millisecond timescale fluctuations in dihydrofolate reductase are exquisitely sensitive to the bound ligands. PNAS 107:1373–78 [Google Scholar]
  5. Watt ED, Shimada H, Kovrigin EL, Loria JP. 5.  2007. The mechanism of rate-limiting motions in enzyme function. PNAS 104:11981–86 [Google Scholar]
  6. Masterson LR, Cheng C, Yu T, Tonelli M, Kornev A. 6.  et al. 2010. Dynamics connect substrate recognition to catalysis in protein kinase A. Nat. Chem. Biol. 6:821–28 [Google Scholar]
  7. Masterson LR, Shi L, Metcalfe E, Gao J, Taylor SS, Veglia G. 7.  2011. Dynamically committed, uncommitted, and quenched states encoded in protein kinase A revealed by NMR spectroscopy. PNAS 108:6969–74 [Google Scholar]
  8. Shaw DE, Maragakis P, Lindorff-Larsen K, Piana S, Dror RO. 8.  et al. 2010. Atomic-level characterization of the structural dynamics of proteins. Science 330:341–46 [Google Scholar]
  9. Frauenfelder H, Parak F, Young RD. 9.  1988. Conformational substates in proteins. Annu. Rev. Biophys. Biophys. Chem. 17:451–79 [Google Scholar]
  10. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E. 10.  et al. 2005. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26:1781–802 [Google Scholar]
  11. Salomon-Ferrer R, Götz AW, Poole D, Le Grand S, Walker RC. 11.  2013. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J. Chem. Theory Comput. 9:3878–88 [Google Scholar]
  12. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC. 12.  et al. 2015. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25 [Google Scholar]
  13. Shaw DE, Deneroff MM, Dror RO, Kuskin JS, Larson RH. 13.  et al. 2007. Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51:91–97 [Google Scholar]
  14. Shaw DE, Grossman JP, Bank JA, Batson B, Butts JA. 14.  et al. 2014. Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. Proc. Int. Conf. High Perform. Comput., Netw., Storage Anal. New Orleans, LA, Nov 16–2141–53 New York: Assoc. Comput. Mach. [Google Scholar]
  15. Warshel A, Levitt M. 15.  1976. Theoretical studies of enzymic reactions: dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme. J. Mol. Biol. 103:227–49 [Google Scholar]
  16. Field MJ, Bash PA, Karplus M. 16.  1990. A combined quantum mechanical and molecular mechanical potential for molecular dynamics simulations. J. Comput. Chem. 11:700–33 [Google Scholar]
  17. Gao J. 17.  1996. Hybrid quantum and molecular mechanical simulations: an alternative avenue to solvent effects in organic chemistry. Acc. Chem. Res. 29:298–305 [Google Scholar]
  18. Svensson M, Humbel S, Froese RDJ, Matsubara T, Sieber S, Morokuma K. 18.  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. 100:19357–63 [Google Scholar]
  19. Monard G, Merz KM. 19.  1999. Combined quantum mechanical/molecular mechanical methodologies applied to biomolecular systems. Acc. Chem. Res. 32:904–11 [Google Scholar]
  20. Hayashi S, Ohmine I. 20.  2000. Proton transfer in bacteriorhodopsin: structure, excitation, IR spectra, and potential energy surface analyses by an ab initio QM/MM method. J. Phys. Chem. B 104:10678–91 [Google Scholar]
  21. Senn HM, Thiel W. 21.  2009. QM/MM methods for biomolecular systems. Angew. Chem. Int. Ed. 48:1198–229 [Google Scholar]
  22. Hammes-Schiffer S, Benkovic SJ. 22.  2006. Relating protein motion to catalysis. Annu. Rev. Biochem. 75:519–41 [Google Scholar]
  23. Yang Y, Yu H, Cui Q. 23.  2008. Extensive conformational transitions are required to turn on ATP hydrolysis in myosin. J. Mol. Biol. 381:1407–20 [Google Scholar]
  24. Kamerlin SCL, Warshel A. 24.  2010. At the dawn of the 21st century: Is dynamics the missing link for understanding enzyme catalysis?. Proteins Struct. Funct. Bioinform. 78:1339–75 [Google Scholar]
  25. McGrath MJ, Kuo I-FW, Hayashi S, Takada S. 25.  2013. Adenosine triphosphate hydrolysis mechanism in kinesin studied by combined quantum-mechanical/molecular-mechanical metadynamics simulations. J. Am. Chem. Soc. 135:8908–8919 [Google Scholar]
  26. Duarte F, Amrein BA, Blaha-Nelson D, Kamerlin SCL. 26.  2015. Recent advances in QM/MM free energy calculations using reference potentials. Biochim. Biophys. Acta. 1850:954–65 [Google Scholar]
  27. Okuyama-Yoshida N, Kataoka K, Nagaoka M, Yamabe T. 27.  2000. Structure optimization via free energy gradient method: application to glycine zwitterion in aqueous solution. J. Chem. Phys. 113:3519–24 [Google Scholar]
  28. Galván IF, Sánchez ML, Martín ME, del Valle FJ, Aguilar MA. 28.  2003. Geometry optimization of molecules in solution: joint use of the mean field approximation and the free-energy gradient method. J. Chem. Phys. 118:255–63 [Google Scholar]
  29. Higashi M, Hayashi S, Kato S. 29.  2007. Geometry optimization based on linear response free energy with quantum mechanical/molecular mechanical method: applications to Menshutkin-type and Claisen rearrangement reactions in aqueous solution. J. Chem. Phys. 126:144503 [Google Scholar]
  30. Hu H, Lu Z, Parks JM, Burger SK, Yang W. 30.  2008. Quantum mechanics/molecular mechanics minimum free-energy path for accurate reaction energetics in solution and enzymes: sequential sampling and optimization on the potential of mean force surface. J. Chem. Phys. 128:03410 [Google Scholar]
  31. Hu H, Yang W. 31.  2008. Free energies of chemical reactions in solution and in enzymes with ab initio quantum mechanics/molecular mechanics methods. Annu. Rev. Phys. Chem. 59:573–601 [Google Scholar]
  32. Yamamoto T. 32.  2008. Variational and perturbative formulations of quantum mechanical/molecular mechanical free energy with mean-field embedding and its analytical gradients. J. Chem. Phys. 129:244104 [Google Scholar]
  33. Kosugi T, Hayashi S. 33.  2012. QM/MM reweighting free energy SCF for geometry optimization on extensive free energy surface of enzymatic reaction. J. Chem. Theory Comput. 8:322–34 [Google Scholar]
  34. Kosugi T, Hayashi S. 34.  2012. Crucial role of protein flexibility in formation of a stable reaction transition state in an α-amylase catalysis. J. Am. Chem. Soc. 134:7045–55 [Google Scholar]
  35. Ufimtsev IS, Martinez TJ. 35.  2009. Quantum chemistry on graphical processing units. 3. Analytical energy gradients, geometry optimization, and first principles molecular dynamics. J. Chem. Theory Comput. 5:2619–28 [Google Scholar]
  36. Ufimtsev IS, Luehr N, Martinez TJ. 36.  2011. Charge transfer and polarization in solvated proteins from ab initio molecular dynamics. J. Phys. Chem. Lett. 2:1789–93 [Google Scholar]
  37. Kulik HJ, Luehr N, Ufimtsev IS, Martinez TJ. 37.  2012. Ab initio quantum chemistry for protein structures. J. Phys. Chem. B 116:12501–9 [Google Scholar]
  38. Kulik HJ, Zhang J, Klinman JP, Martínez TJ. 38.  2016. How large should the QM region be in QM/MM calculations? The case of catechol O-methyltransferase. J. Phys. Chem. B 120:11381–94 [Google Scholar]
  39. Cui Q, Elstner M, Kaxiras E, Frauenheim T, Karplus M. 39.  2001. A QM/MM implementation of the self-consistent charge density functional tight binding (SCC-DFTB) method. J. Phys. Chem. B 105:569–85 [Google Scholar]
  40. Seabra GdM, Walker RC, Elstner M, Case DA, Roitberg AE. 40.  2007. Implementation of the SCC-DFTB method for hybrid QM/MM simulations within the Amber molecular dynamics package. J. Phys. Chem. A 111:5655–64 [Google Scholar]
  41. Higashi M, Truhlar DG. 41.  2008. Electrostatically embedded multiconfiguration molecular mechanics based on the combined density functional and molecular mechanical method. J. Chem. Theory Comput. 4:790–803 [Google Scholar]
  42. Tomasi J, Mennucci B, Cammi R. 42.  2005. Quantum mechanical continuum solvation models. Chem. Rev. 105:2999–3094 [Google Scholar]
  43. Ten-no S, Hirata F, Kato S. 43.  1994. Reference interaction site model self-consistent field study for solvation effect on carbonyl compounds in aqueous solution. J. Chem. Phys. 100:7443–53 [Google Scholar]
  44. Nakano H, Yamamoto T. 44.  2012. Variational calculation of quantum mechanical/molecular mechanical free energy with electronic polarization of solvent. J. Chem. Phys. 136:134107 [Google Scholar]
  45. Nakano H, Yamamoto T. 45.  2013. Accurate and efficient treatment of continuous solute charge density in the mean-field QM/MM free energy calculation. J. Chem. Theory Comput. 9:188–203 [Google Scholar]
  46. Bayly CI, Cieplak P, Cornell W, Kollman PA. 46.  1993. A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model. J. Phys. Chem. 97:10269–80 [Google Scholar]
  47. Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG. 47.  1995. A smooth particle mesh Ewald method. J. Chem. Phys. 103:8577–93 [Google Scholar]
  48. Morita A, Kato S. 48.  1997. Ab initio molecular orbital theory on intramolecular charge polarization: effect of hydrogen abstraction on the charge sensitivity of aromatic and nonaromatic species. J. Am. Chem. Soc. 119:4021–32 [Google Scholar]
  49. Schmidt MW, Baldridge KK, Boatz JA, Elbert ST, Gordon MS. 49.  et al. 1993. General atomic and molecular electronic structure system. J. Comput. Chem. 14:1347–63 [Google Scholar]
  50. Case DA, Darden TA, Cheatham TEISL, Wang J, Duke RE. 50.  et al. 2006. Amber 9 Program Package, Univ. Calif., San Francisco
  51. Jung J, Mori T, Kobayashi C, Matsunaga Y, Yoda T. 51.  et al. 2015. GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations. Wiley Interdiscip. Rev. Comput. Mol. Sci. 5:310–23 [Google Scholar]
  52. Kohen A. 52.  2015. Role of dynamics in enzyme catalysis: substantial versus semantic controversies. Acc. Chem. Res. 48:466–73 [Google Scholar]
  53. Nagel ZD, Klinman JP. 53.  2009. A 21st century revisionist's view at a turning point in enzymology. Nat. Chem. Biol. 5:543–50 [Google Scholar]
  54. Bhabha G, Lee J, Ekiert DC, Gam J, Wilson IA. 54.  et al. 2011. A dynamic knockout reveals that conformational fluctuations influence the chemical step of enzyme catalysis. Science 332:234–38 [Google Scholar]
  55. Tamura K, Hayashi S. 55.  2015. Role of bulk water environment in regulation of functional hydrogen-bond network in photoactive yellow protein. J. Phys. Chem. B 119:15537–49 [Google Scholar]
  56. Higashi M, Kosugi T, Hayashi S, Saito S. 56.  2014. Theoretical study on excited states of bacteriochlorophyll a in solutions with density functional assessment. J. Phys. Chem. B 118:10906–18 [Google Scholar]
  57. Cheng C, Kamiya M, Uchida Y, Hayashi S. 57.  2015. Molecular mechanism of wide photoabsorption spectral shifts of color variants of human cellular retinol binding protein II. J. Am. Chem. Soc. 137:13362–70 [Google Scholar]
  58. Wang W, Nossoni Z, Berbasova T, Watson CT, Yapici I. 58.  et al. 2012. Tuning the electronic absorption of protein-embedded all-trans-retinal. Science 338:1340–43 [Google Scholar]
  59. Kato HE, Kamiya M, Sugo S, Ito J, Taniguchi R. 59.  et al. 2015. Atomistic design of microbial opsin-based blue-shifted optogenetics tools. Nat. Commun. 6:7177 [Google Scholar]
/content/journals/10.1146/annurev-physchem-052516-050827
Loading
/content/journals/10.1146/annurev-physchem-052516-050827
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error