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Computational Molecular Modeling Techniques of Biomacromolecular Systems

  • Jozef Hritz
  • Arnost Mladek
Chapter

Abstract

Computational simulations are used to study the structural and dynamics properties of biomoleculer systems at atomistic resolution. Morover, the simuations also allow to access the energetics of studied systems that can be applied in free energy calculations. Free energy determines the thermodynamic stability and solubility of biomoleclacues in given solution, their affinities towards another biomolecules and their populations in available conformational states. Traditional molecular dynamics simulations of biomacromolecules in explicit water solvent technique are currently restricted to the microseconds time scale but this limitation can be overcome by variety of enhanced sampling computational simulations.

Notes

Acknowledgments

The financial contribution made by the Ministry of Education, Youths and Sports of the Czech Republic within special support paid from the National Programme for Sustainability II funds, project CEITEC 2020 (LQ1601), is gratefully acknowledged.

References

  1. Abrams C, Bussi G (2014) Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration. Entropy 16:163–199CrossRefGoogle Scholar
  2. Andersen HC (1980) Molecular dynamics simulations at constant pressure and/or temperature. J Chem Phys 72:2384CrossRefGoogle Scholar
  3. Barducci A, Bonomi M, Parrinello M (2001) Metadynamics. WILEY Int Rev Comput Sci 1:826–843Google Scholar
  4. Berendsen HJC, Postma JPM, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690CrossRefGoogle Scholar
  5. Burkert U, Allinger NL (1982) Molecular mechanics. In: ACS monograph, vol 177. American Chemical Society, Washington, DCGoogle Scholar
  6. Bussi G, Gervasio FL, Laio A, Parrinello M (2006) Free-energy landscape for beta hairpin folding from combined parallel tempering and metadynamics. J Am Chem Soc 128:13435–13441CrossRefPubMedGoogle Scholar
  7. Dauber-Osguthorpe P, Robert VA, Osguthorpe DJ, Hagler AT (1988) Structure and energetics of ligand-binding to proteins - Escherichia-coli dihydrofolate reductase trimethoprim, a drug-receptor system. Protein Struct Funct Genet 4:31–47CrossRefGoogle Scholar
  8. de Ruiter A, Oostenbrink C (2013) Protein–ligand binding from distancefield distances and hamiltonian replica exchange simulations. J Chem Theory Comput 9:883–892CrossRefPubMedGoogle Scholar
  9. Frenkel D, Smit B (2002) Understanding molecular simulation: from algorithms to applications. Academic Press, BostonGoogle Scholar
  10. Hansen JP, McDonald IR (1986) Theory of simple liquids, 2nd edn. Academic Press, LondonGoogle Scholar
  11. Hermans J, Berendsen HJC, van Gunsteren WF, Postma JPM (1984) A consistent empirical potential for water-protein interactions. Biopolymers 23:1513–1518CrossRefGoogle Scholar
  12. Hess B, Bekker H, Berendsen HJC, Fraaije JGEM (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18:1463–1472CrossRefGoogle Scholar
  13. Hoover WG (1985) Canonical dynamics: equilibrium phase-space distributions. Phys Rev A 31:1695–1697CrossRefGoogle Scholar
  14. Hritz J, Oostenbrink C (2007) Optimization of replica exchange molecular dynamics by fast mimicking. J Chem Phys 127:204104CrossRefPubMedGoogle Scholar
  15. Hritz J, Oostenbrink C (2008) Hamiltonian replica exchange molecular dynamics using soft-core interactions. J Chem Phys 128:144121CrossRefPubMedGoogle Scholar
  16. Huber T, Torda AE, van Gunsteren WF (1994) Local elevation: a method for improving the searching properties of molecular dynamics simulation. J Comput Aided Mol Des 8:695–708CrossRefPubMedGoogle Scholar
  17. Kaestner J (2011) Umbrella sampling. WILEY Int Rev Comput Sci 1:932–942Google Scholar
  18. Kaminski G, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105:6474–6487CrossRefGoogle Scholar
  19. Kirkpatrick C, Gelatt D Jr, Vecchi MP (1983) Optimalization by simulated annealing. Science 220:671–680CrossRefGoogle Scholar
  20. Kumar S, Rosenberg JM, Bouzida D, Swendsen RH, Kollman PA (1992) The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J Comput Chem 13:1011–1021CrossRefGoogle Scholar
  21. Laio A, Parrinello M (2002) Escaping free energy minima. Proc Natl Acad Sci U S A 99:12562–12566CrossRefPubMedPubMedCentralGoogle Scholar
  22. Lautz J, Kessler H, Kaptein R, van Gunsteren WF (1987) Molecular dynamics simulations of cyclosporin A: the crystal structure and dynamic modelling of a structure in Apolar solution based on NMR data. J Comput Aided Mol Des 1:219–241CrossRefPubMedGoogle Scholar
  23. Lavery R, Rivail J-L (1991) In: Smith J (ed) American Institute of Physics (A.I.P.) conference proceedings 1991, vol 239. New York, pp 131–146 Google Scholar
  24. MacKerell AD Jr, Bashford D, Bellott M, Dunbrack RL Jr, Evanseck JD, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph-McCarthy D, Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T, Nguyen DT, Prodhom B, Reiher WE III, Roux B, Schlenkrich M, Smith JC, Stote R, Straub J, Watanabe M, Wiorkiewicz-Kuczera J, Yin D, Karplus M (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616CrossRefPubMedGoogle Scholar
  25. McDonald IR, Singer K (1967) Machine calculation of thermodynamic properties of a simple fluid at supercritical temperatures. J Chem Phys 47:4766–4772CrossRefGoogle Scholar
  26. McDonald IR, Singer K (1969) Examination of the adequacy of the 12–6 potential for liquid argon by means of Monte Carlo calculations. J Chem Phys 50:2308–2315CrossRefGoogle Scholar
  27. Meli M, Colombo G (2013) A Hamiltonian replica exchange molecular dynamics (MD) method for the study of folding, based on the analysis of the stabilization determinants of proteins. Int J Mol Sci 14:12157–12169CrossRefPubMedPubMedCentralGoogle Scholar
  28. Nosé S (1984) A unified formulation of the constant temperature molecular-dynamics methods. J Chem Phys 81:511–519CrossRefGoogle Scholar
  29. Oostenbrink C, de Ruiter A, Hritz J, Vermeulen NPE (2012) Malleability and versatility of Cytochrome P450 active sites studied by molecular simulations. Curr Drug Metab 13:190–196CrossRefPubMedGoogle Scholar
  30. Piana S, Laio A (2007) A bias-exchange approach to protein folding. J Phys Chem B 111:4553–4559CrossRefPubMedGoogle Scholar
  31. Ryckaert J-P, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341CrossRefGoogle Scholar
  32. Spiwok V, Sucur Z, Hosek P (2015) Enhanced sampling techniques in biomolecular simulations. Biotechnol Adv 33:1130–1140CrossRefPubMedGoogle Scholar
  33. Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314:141–151CrossRefGoogle Scholar
  34. Swendsen RH, Wang JS (1986) Replica Monte Carlo simulation of spin glasses. Phys Rev Lett 57:2607–2609CrossRefPubMedGoogle Scholar
  35. Torrie GM, Valleau JP (1974) Monte Carlo free energy estimates using non-Boltzmann sampling: application to the sub-critical Lennard-Jones fluid. Chem Phys Lett 28:578–581CrossRefGoogle Scholar
  36. Torrie GM, Valleau JP (1977) Non-physical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J Comput Phys 23:187–199CrossRefGoogle Scholar
  37. van Gunsteren WF, Berendsen HJC (1984) Computer simulation as a tool for tracing the comformational differences between proteins in solution and in the crystalline state. J Mol Biol 176:559–564CrossRefPubMedGoogle Scholar
  38. van Gunsteren WF, Berendsen HJC (1987) Gromos-87 manual. Biomos BV, GroningenGoogle Scholar
  39. van Gunsteren WF (1991) Computer simulation of biomolecular systems: overview of timesaving techniques. In: Advances in biomolecular simulations. American Institute of Physics (A.I.P.), New YorkGoogle Scholar
  40. Weiner SJ, Kollman PA, Case DA, Singh UC, Ghio C, Alagona G, Profeta S Jr, Weiner P (1984) A new force field for molecular mechanical simulation of nucleic acids and proteins. J Am Chem Soc 106:765–784CrossRefGoogle Scholar
  41. Weiner SJ, Kollman PA, Nguyen DT et al (1986) An all atom force-field for simulations of proteins and nucleic-acids. J Comput Chem 7:230–252CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.CEITEC MU, Masaryk UniversityBrnoCzech Republic

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