Systematic hierarchical coarse-graining with the inverse Monte Carlo method.

We outline our coarse-graining strategy for linking micro- and mesoscales of soft matter and biological systems. The method is based on effective pairwise interaction potentials obtained in detailed ab initio or classical atomistic Molecular Dynamics (MD) simulations, which can be used in simulations at less accurate level after scaling up the size. The effective potentials are obtained by applying the inverse Monte Carlo (IMC) method [A. P. Lyubartsev and A. Laaksonen, Phys. Rev. E 52(4), 3730-3737 (1995)] on a chosen subset of degrees of freedom described in terms of radial distribution functions. An in-house software package MagiC is developed to obtain the effective potentials for arbitrary molecular systems. In this work we compute effective potentials to model DNA-protein interactions (bacterial LiaR regulator bound to a 26 base pairs DNA fragment) at physiological salt concentration at a coarse-grained (CG) level. Normally the IMC CG pair-potentials are used directly as look-up tables but here we have fitted them to five Gaussians and a repulsive wall. Results show stable association between DNA and the model protein as well as similar position fluctuation profile.

[1]  Kurt Kremer,et al.  Kirkwood-Buff Analysis of Liquid Mixtures in an Open Boundary Simulation. , 2012, Journal of chemical theory and computation.

[2]  Alexander Mirzoev,et al.  MagiC: Software Package for Multiscale Modeling. , 2013, Journal of chemical theory and computation.

[3]  Joseph F Rudzinski,et al.  Coarse-graining entropy, forces, and structures. , 2011, The Journal of chemical physics.

[4]  W. Schommers,et al.  Pair potentials in disordered many-particle systems: A study for liquid gallium , 1983 .

[5]  P. Kollman,et al.  A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules , 1995 .

[6]  Alexey Savelyev,et al.  Recent successes in coarse‐grained modeling of DNA , 2013 .

[7]  E. Vanden-Eijnden,et al.  Mori-Zwanzig formalism as a practical computational tool. , 2010, Faraday discussions.

[8]  Alexander D. MacKerell,et al.  An all-atom empirical energy function for the simulation of nucleic acids , 1995 .

[9]  M Scott Shell,et al.  The relative entropy is fundamental to multiscale and inverse thermodynamic problems. , 2008, The Journal of chemical physics.

[10]  S. Nosé A molecular dynamics method for simulations in the canonical ensemble , 1984 .

[11]  R. Swendsen Monte Carlo Renormalization Group , 2011 .

[12]  Sarah A. Harris,et al.  The atomistic simulation of DNA , 2011 .

[13]  Salvatore Torquato,et al.  Statistical mechanical models with effective potentials: Definitions, applications, and thermodynamic consequences , 2002 .

[14]  R. C. Reeder,et al.  A Coarse Grain Model for Phospholipid Simulations , 2001 .

[15]  Adelene Y. L. Sim,et al.  Modeling nucleic acids. , 2012, Current opinion in structural biology.

[16]  Christoph Junghans,et al.  Kirkwood-Buff Coarse-Grained Force Fields for Aqueous Solutions. , 2012, Journal of chemical theory and computation.

[17]  Florian Müller-Plathe,et al.  Transferability of coarse-grained force fields: the polymer case. , 2008, The Journal of chemical physics.

[18]  Alexander Lyubartsev,et al.  Systematic coarse-graining of molecular models by the Newton inversion method. , 2010, Faraday discussions.

[19]  Kurt Kremer,et al.  Hierarchical modeling of polystyrene: From atomistic to coarse-grained simulations , 2006 .

[20]  Gregory A Voth,et al.  Effective force fields for condensed phase systems from ab initio molecular dynamics simulation: a new method for force-matching. , 2004, The Journal of chemical physics.

[21]  Y. Shamoo,et al.  A variable DNA recognition site organization establishes the LiaR-mediated cell envelope stress response of enterococci to daptomycin , 2015, Nucleic acids research.

[22]  R. Yaris,et al.  A DYNAMIC SIMULATION METHOD SUPPRESSING UNINTERESTING DEGREES OF FREEDOM , 1991 .

[23]  James B. Adams,et al.  Interatomic Potentials from First-Principles Calculations: The Force-Matching Method , 1993, cond-mat/9306054.

[24]  Lanyuan Lu,et al.  Fitting coarse-grained distribution functions through an iterative force-matching method. , 2013, The Journal of chemical physics.

[25]  A. Laaksonen,et al.  A Solvent-Mediated Coarse-Grained Model of DNA Derived with the Systematic Newton Inversion Method. , 2014, Journal of chemical theory and computation.

[26]  A. Lyubartsev,et al.  Calculation of effective interaction potentials from radial distribution functions: A reverse Monte Carlo approach. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[27]  Zun-Jing Wang,et al.  A Systematically Coarse-Grained Solvent-Free Model for Quantitative Phospholipid Bilayer Simulations , 2010, The Journal of Physical Chemistry. B.

[28]  A. Lyubartsev,et al.  Multiscale coarse-grained simulations of ionic liquids: comparison of three approaches to derive effective potentials. , 2013, Physical chemistry chemical physics : PCCP.

[29]  Clarisse G. Ricci,et al.  Molecular dynamics of DNA: comparison of force fields and terminal nucleotide definitions. , 2010, The journal of physical chemistry. B.

[30]  W G Noid,et al.  Perspective: Coarse-grained models for biomolecular systems. , 2013, The Journal of chemical physics.

[31]  Gregory A Voth,et al.  A multiscale coarse-graining method for biomolecular systems. , 2005, The journal of physical chemistry. B.

[32]  Alexander Lukyanov,et al.  Versatile Object-Oriented Toolkit for Coarse-Graining Applications. , 2009, Journal of chemical theory and computation.

[33]  Steve Plimpton,et al.  Fast parallel algorithms for short-range molecular dynamics , 1993 .

[34]  K. Tunstrøm,et al.  Using force covariance to derive effective stochastic interactions in dissipative particle dynamics. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  M Scott Shell,et al.  Coarse-graining errors and numerical optimization using a relative entropy framework. , 2011, The Journal of chemical physics.

[36]  Alexey Savelyev,et al.  Molecular renormalization group coarse-graining of polymer chains: application to double-stranded DNA. , 2009, Biophysical journal.

[37]  Kurt Kremer,et al.  Computer Simulations of Soft Matter: Linking the Scales , 2014, Entropy.

[38]  Alexey Savelyev,et al.  Molecular renormalization group coarse-graining of electrolyte solutions: application to aqueous NaCl and KCl. , 2009, The journal of physical chemistry. B.

[39]  Alexander Lyubartsev,et al.  Systematic implicit solvent coarse graining of dimyristoylphosphatidylcholine lipids , 2014, J. Comput. Chem..

[40]  Dirk Reith,et al.  Deriving effective mesoscale potentials from atomistic simulations , 2002, J. Comput. Chem..

[41]  A. Louis Beware of density dependent pair potentials , 2002, cond-mat/0205110.

[42]  Pengyu Y. Ren,et al.  Systematic improvement of a classical molecular model of water. , 2013, The journal of physical chemistry. B.

[43]  Alan K. Soper,et al.  Empirical potential Monte Carlo simulation of fluid structure , 1996 .

[44]  Gregory A. Voth,et al.  The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models. , 2008, The Journal of chemical physics.

[45]  R. L. Henderson A uniqueness theorem for fluid pair correlation functions , 1974 .

[46]  A. Laaksonen,et al.  A coarse-grained simulation study of the structures, energetics, and dynamics of linear and circular DNA with its ions. , 2015, Journal of chemical theory and computation.

[47]  L. Monticelli,et al.  Coarse-grained force fields for molecular simulations. , 2015, Methods in molecular biology.

[48]  Alexander P. Lyubartsev,et al.  On the Reduction of Molecular Degrees of Freedom in Computer Simulations , 2004 .

[49]  Berk Hess,et al.  Modeling multibody effects in ionic solutions with a concentration dependent dielectric permittivity. , 2006, Physical review letters.

[50]  A. Lyubartsev,et al.  A Coarse-Grained DNA Model Parameterized from Atomistic Simulations by Inverse Monte Carlo , 2014 .

[51]  A. Laaksonen,et al.  On the Modularity of the Intrinsic Flexibility of the µ Opioid Receptor: A Computational Study , 2014, PloS one.