基于粗粒化模型对有机溶剂的分子动力学模拟
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摘要
这篇论文介绍了一个一般的基于电多极展开势和Gay-Berne (GB)势的粗粒化分子力学模型.在这个粗粒化模型中,把有机溶液分子处理成为单轴的椭球体,用各向异性的GB函数表示单轴椭球体之间的相互作用,从而实现了范德瓦尔斯势的粗粒化.分子体系的电荷分布用电多极展开来表示,包括位于质心处的点电荷、偶极矩和四极矩.在基于Boltzmann分布对四种基本的分子构象进行了Monte Carlo取样之后,通过与全原子模型的范德瓦尔斯势比较得到了GB参数.又在对用量子化学方法计算得到的分子体系的静电势进行了电荷、偶极矩和四极矩的拟合之后,得到了电多极展开势参数.
利用得到的粗粒化参数,基于此粗粒化模型,本文对CHCl_3、CH_2Cl_2、C_5H_5N、DMF、DMSO、THF六种有机溶剂分子进行了分子动力学模拟,并将其结果同全原子模型的模拟结果进行了比较.计算得到的结果表明了用此粗粒化模型从整体上能够重复全原子模型的模拟结果,但是在某些细节的计算上与全原子模型的计算结果有些偏差,其原因可能是在我们目前的工作中仅仅考虑了单个位点的情况.为此,今后在对具有复杂结构的分子体系进行粗粒化模拟时还应该考虑合理地放置相互作用的位点,以及考虑合理地增加相应的相互作用位点的数目.通过粗粒化模型进行分子动力学模拟的主要优点是可以在保证能获得一定精度的条件下较大幅度地提高计算的效率,这主要是由于用粗粒化的GB-EMP相互作用代替了多对原子之间的相互作用,从而使自由度的数目得到了较大地减少.此外另外一个重要的因素就是由于忽略了键长、键角和二面角的高频运动,而使模拟的时间尺度增大了.还需要指出的是,由于在处理GB效应中引入的d w参数较好地控制了GB势的柔性,所以用在本文中介绍的粗粒化模型还可以被用来模拟更大的更复杂得分子体系.此外在考虑了能够更精确地描述静电相互作用势的电多极势效应之后,使基于此粗粒化模型模拟那些带高电量的生物大分子体系成为可能.
This paper presents a general coarse-grained molecular mechanics model based on electric multipole expansion potential and Gay-Berne (GB) potential. Coarse graining of van der Waals potential is achieved by treating molecules as soft uniaxial ellipsoids interacting via a generalized anisotropic GB function. The charge distribution is represented by electric multipole expansion, including point charge, dipole moment, and quadrupole moment placed at the center of mass. In order to obtain the GB parameters, we firstly make Monte Carlo sampling for four reference configurations based on the Boltzmann distribution. After comparing with the van der Waals potential within the all-atom model, we get the GB parameters. Also after making the fitting of charge, dipole moment, and quadrupole moment with the electric potential obtained from the quantum chemical computations with Gaussian03, we get the electric multipole potential parameters.
With the GB-EMP parameters, then, we make the molecular dynamics simulations (MDS) for six kinds of organic solvents, namely, CHCl_3, CH_2Cl_2, C_5H_5N, DMF, DMSO and THF based on the coarse-grained model (CGM), and make comparison with the results within the all-atom model. The CGM can reproduce, on the whole, the results within the all-atom model, but there are some deviations in the simulations within the CGM, in some details, compare with the all-atom model. For the reason, it is because that we only take account of one interaction site in present work. So for the molecules with more complicated, It is necessary to take account how to place the interaction sites, and the situation with multi-sites when one do the MDS within the frame of coarse-grained model. The main advantages of molecular dynamics simulations based on this CGM is that accuracy can be guaranteed. It is more significantly that we can improve the computational efficiency at the same time. Primarily this is due to the use of the GB-EMP interaction instead of pairs of interactions between atoms, making the number of degree of freedom has been greatly reduced. Also another important factor is the neglect of the high-frequency motion of bond, angle and torsion. It increases the simulation time scale. We need to note that the introduction of the dw parameter can give greater control over the“softness”the GB potential. So the CGM in this paper can also be used to simulate more complex molecules. In addition, this CGM can describe the electrostatic potential accurately. We can apply it to a broad range of molecular systems including highly charged biopolymers.
利用得到的粗粒化参数,基于此粗粒化模型,本文对CHCl_3、CH_2Cl_2、C_5H_5N、DMF、DMSO、THF六种有机溶剂分子进行了分子动力学模拟,并将其结果同全原子模型的模拟结果进行了比较.计算得到的结果表明了用此粗粒化模型从整体上能够重复全原子模型的模拟结果,但是在某些细节的计算上与全原子模型的计算结果有些偏差,其原因可能是在我们目前的工作中仅仅考虑了单个位点的情况.为此,今后在对具有复杂结构的分子体系进行粗粒化模拟时还应该考虑合理地放置相互作用的位点,以及考虑合理地增加相应的相互作用位点的数目.通过粗粒化模型进行分子动力学模拟的主要优点是可以在保证能获得一定精度的条件下较大幅度地提高计算的效率,这主要是由于用粗粒化的GB-EMP相互作用代替了多对原子之间的相互作用,从而使自由度的数目得到了较大地减少.此外另外一个重要的因素就是由于忽略了键长、键角和二面角的高频运动,而使模拟的时间尺度增大了.还需要指出的是,由于在处理GB效应中引入的d w参数较好地控制了GB势的柔性,所以用在本文中介绍的粗粒化模型还可以被用来模拟更大的更复杂得分子体系.此外在考虑了能够更精确地描述静电相互作用势的电多极势效应之后,使基于此粗粒化模型模拟那些带高电量的生物大分子体系成为可能.
This paper presents a general coarse-grained molecular mechanics model based on electric multipole expansion potential and Gay-Berne (GB) potential. Coarse graining of van der Waals potential is achieved by treating molecules as soft uniaxial ellipsoids interacting via a generalized anisotropic GB function. The charge distribution is represented by electric multipole expansion, including point charge, dipole moment, and quadrupole moment placed at the center of mass. In order to obtain the GB parameters, we firstly make Monte Carlo sampling for four reference configurations based on the Boltzmann distribution. After comparing with the van der Waals potential within the all-atom model, we get the GB parameters. Also after making the fitting of charge, dipole moment, and quadrupole moment with the electric potential obtained from the quantum chemical computations with Gaussian03, we get the electric multipole potential parameters.
With the GB-EMP parameters, then, we make the molecular dynamics simulations (MDS) for six kinds of organic solvents, namely, CHCl_3, CH_2Cl_2, C_5H_5N, DMF, DMSO and THF based on the coarse-grained model (CGM), and make comparison with the results within the all-atom model. The CGM can reproduce, on the whole, the results within the all-atom model, but there are some deviations in the simulations within the CGM, in some details, compare with the all-atom model. For the reason, it is because that we only take account of one interaction site in present work. So for the molecules with more complicated, It is necessary to take account how to place the interaction sites, and the situation with multi-sites when one do the MDS within the frame of coarse-grained model. The main advantages of molecular dynamics simulations based on this CGM is that accuracy can be guaranteed. It is more significantly that we can improve the computational efficiency at the same time. Primarily this is due to the use of the GB-EMP interaction instead of pairs of interactions between atoms, making the number of degree of freedom has been greatly reduced. Also another important factor is the neglect of the high-frequency motion of bond, angle and torsion. It increases the simulation time scale. We need to note that the introduction of the dw parameter can give greater control over the“softness”the GB potential. So the CGM in this paper can also be used to simulate more complex molecules. In addition, this CGM can describe the electrostatic potential accurately. We can apply it to a broad range of molecular systems including highly charged biopolymers.
引文
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[6] M. P. Allen, D. J. Tildesley. Computer simulation of liquids. Oxford University Press. 1989. ISBN 0-19-855645-4
[7] D. C. Rapaport. The Art of Molecular Dynamics Simulation. 2004. ISBN 0-521-82586-7
[8] Tozzini V et al. Coarse-grained models for protein. Curr Opin Struc Biol. 2005. 15: 144-150
[9] Ueda Y et al. Studies on protein folding, unfolding and fluctuations by computer simulation.Ⅱ. A. Three-dimensional lattice model of lysozyme. Biopolymer. 1978. 17: 1531-1548
[10] Klein M et al. Large-scale molecular dynamics simulations of self-assembling systems. Science. 2008. 321: 798-800
[11] Marrink J et al. Coarse grained model for semiquantitative lipid simulations. J Phys Chem B. 2004. 108: 750-760
[12] Marrink J et al. The MARTINI force field: Coarse grained model for biomolecular simulations. J Phys Chem B. 2007. 111: 7812-7824
[13] Jensen F. Introduction to Computational Chemistry. New York: Wiley. 1999
[14] Burkert U, Allinger N L. Molecular Mechanics. Washington D. C.: American Chemistry Society. 198
[15] Allen M P, Tildesley D J. Computer Simulation of Liquids. Oxford: Oxford University Press. 1987
[16] Haile J M. Molecular Dynamics Simulation, Elementary Methods. New York: John Wiley. 1992
[17] Andrews D H. The Relation Between the Raman Spectra and the Structure of Organic Molecules. Phys Rev. 1930. 36: 544-554
[18]帅志刚,邵久书等.理论化学原理与应用.北京:科学出版社.2008
[19] Brooks B R, Bruccoleri R E, Olafson B D et al. J. Comp. Chem. 1983. 4: 187-217
[20] Jorgensen W L, Tirado-Rives J. J. Am. Chem. Soc. 1988. 110: 1666-1671
[21] Allinger N L, Chen Katzenellenbogen J A et al. J. Comp. Chem. 1996. 17: 747-755
[22] Cornell W D, Cieplak P, Bayly C I et al. J. Am. Chem. Soc. 1995. 117: 5179-5197
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[25] Dror R. O., Jensen M. ?., Borhani D. W., Shaw D. E. J. Gen. Physiol. 2010. 135: 555
[26] Karplus M., McCammon J. A. Nature Struct. Biol. 2002. 9: 646
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[29] Klepeis J. L., Lindorff-Larsen K., Dror R. O., Shaw D. E. Curr. Opin. Struct. Biol. 2009. 19: 120
[30] Freddolino P. L., Harrison C. B., Liu Y. X., Schulten. K. Nature Phys. 2010. 6: 751
[31] Xu X. J., Hou T. J., Qiao X. B., Zhang W. Computer aided drug design. Beijing: Chemical Industry Press. 2004: 169-172 [徐筱杰,侯廷军,乔学斌,章威.计算机辅助药物分子设计.北京:化学工业出版社, 2004: 169-172]
[32] Leach A. P. Molecular modeling: principles and application. England: Person Education Limited. 2001: 165-245
[33] Schlick T. Molecular modeling and simulation: an interdisciplinary guide (2nd). New York: Springer. 2010: 265-343
[34] Voth G. A. Coarse-graining of condensed phase and biomolecular systems. England: CRC Press. 2009
[35] Chu J. W., Izvekov S., Voth G. A. Mol. Sim. 2006. 32: 211
[36] Marrink S. J., de Vries A. H., Mark A. E. J. Phys. Chem. B. 2004. 108: 750
[37] Tozzini V. Curr. Opin. Struc. Biol. 2005. 15: 114
[38] Golubkov P. A., Ren P. Y. J. Chem. Phys. 2006. 125: 064103
[39] J. Hirschfelder, C. Curtiss, and R. Bird. Molecular Theory of Cases and Liquids. Wiley, New York. 1954
[40] Berne B. J., Pechukas P. J. Chem. Phys. 1972. 56: 4213
[41] Streett W. B. and Gubbins K. E. Annu. Rev. Phys. Chem. 1977. 28: 373, and references therein.
[42] Singer K., Taylor A., and Singer J. V. L. Mol. Phys. 1977. 33: 1757
[43] Streett W. B. and Tildseley D. J. Proc. Poy. Soc.(London) A. 1977. 335: 229
[44] Streett W. B. and Tildseley D. J. Faraday Disc. Chem. Soc. 1978. 66:161
[45] Murad S., Evans D. J., and Gubbins K. E. Mol. Phys. 1979. 37:725
[46] Steele W. A. and Streett W. B. Mol. Phys. 1980 39:279
[47] Steele W. A. and Streett W. B. Mol. Phys. 1980. 39:299
[48] Tildseley D. J., Streett W. B., and Steele W. A. Mol. Phys. 1980. 39:1169
[49] Gay J. G., Berne B. J. J. Chem. Phys. 1981. 74: 3316
[50] Corner J. Proc. Roy. Soc. (London) A. 1948. 192:275
[51] Kihara T. Rev. Mod. Phys. 1953. 25:831
[52] Berner B. J. and Pechukas P. J. Chem. Phys. 1972. 56:4213
[53] Walmsley S. H. Chem. Phys. Lett. 1977. 49:320
[54] Kushick J. and Berner B. J. J. Chem. Phys. 1977. 64: 320
[55] Cleaver D. J., Care C. M., Allen M. P., Neal M. P. Phys. Rev. E. 1996. 54: 559
[56] Wilson M. R. J. Chem. Phys. 1997. 107: 8654
[57] Care C. M., Cleaver D. J. Rep. Prog. Phys. 2005. 68: 2665
[58] Paramonov L., Yaliraki S. N. J. Chem. Phys. 2005. 123: 194111
[59] Kabadi V. N., Steele W. A. Ber. Bunsenges. Phys. Chem. 1985. 89: 2
[60] Kabadi V. N. Ber. Bunsenges. Phys. Chem. 1986. 90: 327
[61] Darrin M Y, Yang W. J. Chem. Phys. 1996. 104: 159-172
[62] Yang Z Z, Shen E Z. J. Mol. Struct. (Theochem). 1994. 312: 167
[63] Tannor D J, Marten B, Murphy R et al. J. Am. Chem. Soc. 1994. 116: 11875-11882
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