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Computational Molecular Modelling

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Computer Simulations in Molecular Biology

Part of the book series: Scientific Computation ((SCIENTCOMP))

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Abstract

In this chapter, we discuss the computational molecular modelling strategies. In particular, the CHARMM molecular mechanics force field of biomolecules is introduced and its parametrisation. For more information about other molecular mechanics force fields and molecular modelling strategies, one can consider the following literature (Leach 2001). Besides, we will discuss some new initiatives for the force field development, such as ForceBalance, machine learning, and open force field (OpenFF) approaches.

The chapter aims to introduce the computational molecular modelling strategies. The focus is on the CHARMM molecular mechanics force field.

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References

  • M.P. Allen, D.J. Tildesley, Computer Simulation of Liquids (Oxford University Press, 1989)

    Google Scholar 

  • N.L. Allinger, Conformational Analysis 130. MM2. A Hydrocarbon Force Field Utilizing V\(_1\) and V\(_2\) Torsional Terms. J. Am. Chem. Soc. 99, 8127–8134 (1977)

    Google Scholar 

  • N.L. Allinger, F. Li, L. Yan, J.C. Tai, Molecular mechanics. The MM3 force field for alkenes. J. Comput. Chem. 11, 848–867 (1990a)

    Google Scholar 

  • N.L. Allinger, F. Li, L. Yan, J.C. Tai, Molecular mechanics (MM3) calculations on conjugated hydrocarbons. J. Comput. Chem. 11, 868–895 (1990)

    Article  Google Scholar 

  • N.L. Allinger, K. Chen, J.-H. Lii, An improved force field (MM4) for saturated hydrocarbons. J. Comput. Chem. 17, 642–668 (1996)

    Article  Google Scholar 

  • N.L. Allinger, K. Chen, J.A. Katzenelenbogen, S.R. Wilson, G.M. Anstead, Hyperconjugative effects on carbon-carbon bond lengths in molecular mechanics (MM4). J. Comput. Chem. 17, 747–755 (1996)

    Article  Google Scholar 

  • S. Boothroyd, L.P. Wang, D.L. Mobley, J.D. Chodera, M.R. Shirts, Open force field evaluator: an automated, efficient, scalable framework for the estimation of physical properties from molecular simulation. J. Chem. Theory Comput. 18(6), 3566–3576 (2022)

    Article  Google Scholar 

  • W.D. Cornell, P. Cieplak, C.I. Bayly, I.R. Gould, K.M. Merz Jr., D.M. Ferguson, D.C. Spellmeyer, T. Fox, J.W. Caldwell, P.A. Kollman, A second generation force field for the simulation of proteins, nucleic acids and organic molecules. J. Am. Chem. Soc. 117, 5179–5197 (1995)

    Article  Google Scholar 

  • N. Foloppe, A. MacKerell, All-atom empirical force field for nucleic acids: I. Parameter optimization based on small molecule and condensed phase macromolecular target data. J. Comput. Chem. 21, 86–104 (2000)

    Google Scholar 

  • S. Grimme, A general quantum mechanically derived force field (qmdff) for molecules and condensed phase simulations. J. Chem. Theory Comput. 10(10), 4497–4514 (2014)

    Article  Google Scholar 

  • P.H. Hünenberger, W.F. van Gunsteren, Computer Simulation of Biomolecular Systems Theoretical and Experimental Applications (Kluwer, Dordrecht, The Netherlands, 1997)

    Google Scholar 

  • W.L. Jorgensen, J. Tirado-Rives, The opls potential functions for proteins - energy minimizations for crystals of cyclic-peptides and crambin. J. Am. Chem. Soc. 110, 1666–1671 (1988)

    Article  Google Scholar 

  • A.D. Jr MacKerell, D. Bashford, M. Bellot, R.L. Dunbrack Jr., J.D. Evanseck, M.J. Field, S. Fisher, J. Gao, H. Guo, S. Ha, D. Joseph-McCarthy, L. Kuchnir, K. Kuczera, F.T.K. Lau, C. Mattos, S. Michnick, T. Ngo, D.T. Nguyen, B. Prodhom, W.E. Reiher, B. Roux, M. Schlenkrich, J.C. Smith, R. Stote, J. Straub, M. Watanabe, J. Wiorkiewicz-Kuczera, D. Yin, M. Karplus. All-atom empirical potential for molecular modelling and dynamics studies of proteins. J. Phys. Chem. B 102(18), 3586–3616 (1998)

    Google Scholar 

  • H. Kamberaj, Molecular Dynamics Simulations in Statistical Physics: Theory And Applications Computational Series. (Springer Nature, Switzerland, 2020)

    Book  Google Scholar 

  • A.R. Leach, Molecular Modelling, Principles And Applications, 2nd edn. (Prentice Hall, Pearson Education Limited, Edingburgh Gate, 2001)

    Google Scholar 

  • A. MacKerell, N. Banavali All-atom empirical force field for nucleic acids: II. Parameter optimization based on small molecule and condensed phase macromolecular target data. J. Comput. Chem. 21, 105–120 (2000)

    Google Scholar 

  • A. MacKerell, M. Feig, C. Brooks, Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J. Comput. Chem. 25, 1400–1415 (2004)

    Article  Google Scholar 

  • M.P. Metz, K. Piszczatowski, K. Szalewicz, Automatic generation of intermolecular potential energy surfaces. J. Chem. Theory Comput. 12(12), 5895–5919 (2016)

    Article  Google Scholar 

  • A.J. Misquitta, R. Podeszwa, B. Jeziorski, K. Szalewicz, Intermolecular potentials based on symmetry-adapted perturbation theory with dispersion energies from time-dependent density-functional calculations. J. Chem. Phys. 123(21), 214103 (2005)

    Article  ADS  Google Scholar 

  • Y. Qiu, P.S. Nerenberg, T. Head-Gordon, L.P. Wang, Systematic optimization of water models using liquid/vapor surface tension data. J. Phys. Chem. B 123, 7061–7073 (2019)

    Article  Google Scholar 

  • W. van Gunsteren, D. Bakowies, R. Baron, I. Chandrasekhar, M. Christen, X. Daura, P. Gee, D.P. Geerke, A. Glättli, P.H. Hünenberger, M.A. Kastenholz, C. Oostenbrink, M. Schenk, D. Trzesniak, N.F.A. can der Vegt, H.B. Yu, Biomolecular modeling: goals, problems, perspectives. Angew. Chem. Int. Ed. 45(25), 4064–4092 (2006)

    Google Scholar 

  • M.J. Van Vleet, A.J. Misquitta, A.J. Stone, J. Schmidt, Beyond Borm-Mayer: improved models for short-range repulsion in ab initio force fields. J. Chem. Theory Comput. 12, 3851–3870 (2016)

    Article  Google Scholar 

  • S. Vandenbrande, M. Waroquier, V.V. Speybroeck, T. Verstraelen, The monomer electron density force field (MEDFF): a physically inspired model for noncovalent interactions. J. Chem. Theory Comput. 13(1), 161–179 (2017)

    Article  Google Scholar 

  • L.P. Wang, J. Chen, T. Van Voorhis, Systematic parametrization of polarizable force fields from quantum chemistry data. J. Chem. Theory Comput. 9, 452–460 (2013)

    Article  Google Scholar 

  • L.P. Wang, T.J. Martinez, V.S. Pande, Building force fields: an automatic, systematic, and reproducible approach. J. Phys. Chem. Lett. 5, 1885–1891 (2014)

    Article  Google Scholar 

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Correspondence to Hiqmet Kamberaj .

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Kamberaj, H. (2023). Computational Molecular Modelling. In: Computer Simulations in Molecular Biology. Scientific Computation. Springer, Cham. https://doi.org/10.1007/978-3-031-34839-6_6

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