Reference state for the generalized Yvon-Born-Green theory: application for coarse-grained model of hydrophobic hydration.

Coarse-grained (CG) models provide a computationally efficient means for investigating phenomena that remain beyond the scope of atomically detailed models. Although CG models are often parametrized to reproduce the results of atomistic simulations, it is highly desirable to determine accurate CG models from experimental data. Recently, we have introduced a generalized Yvon-Born-Green (g-YBG) theory for directly (i.e., noniteratively) determining variationally optimized CG potentials from structural correlation functions. In principle, these correlation functions can be determined from experiment. In the present work, we introduce a reference state potential into the g-YBG framework. The reference state defines a fixed contribution to the CG potential. The remaining terms in the potential are then determined, such that the combined potential provides an optimal approximation to the many-body potential of mean force. By specifying a fixed contribution to the potential, the reference state significantly reduces the computational complexity and structural information necessary for determining the remaining potentials. We also validate the quantitative accuracy of the proposed method and numerically demonstrate that the reference state provides a convenient framework for transferring CG potentials from neat liquids to more complex systems. The resulting CG model provides a surprisingly accurate description of the two- and three-particle solvation structures of a hydrophobic solute in methanol. This work represents a significant step in developing the g-YBG theory as a useful computational framework for determining accurate CG models from limited experimental data.

[1]  R. Larson,et al.  The MARTINI Coarse-Grained Force Field: Extension to Proteins. , 2008, Journal of chemical theory and computation.

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

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

[4]  M J Sippl,et al.  Knowledge-based potentials for proteins. , 1995, Current opinion in structural biology.

[5]  J. Skolnick In quest of an empirical potential for protein structure prediction. , 2006, Current opinion in structural biology.

[6]  H. Scheraga,et al.  Medium- and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins. , 1976, Macromolecules.

[7]  A. Liwo,et al.  A united‐residue force field for off‐lattice protein‐structure simulations. I. Functional forms and parameters of long‐range side‐chain interaction potentials from protein crystal data , 1997 .

[8]  Avisek Das,et al.  The multiscale coarse-graining method. V. Isothermal-isobaric ensemble. , 2010, The Journal of chemical physics.

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

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

[11]  Gerrit Groenhof,et al.  GROMACS: Fast, flexible, and free , 2005, J. Comput. Chem..

[12]  A. Ben-Naim STATISTICAL POTENTIALS EXTRACTED FROM PROTEIN STRUCTURES : ARE THESE MEANINGFUL POTENTIALS? , 1997 .

[13]  Ed Anderson,et al.  LAPACK Users' Guide , 1995 .

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

[15]  T. Darden,et al.  The effect of long‐range electrostatic interactions in simulations of macromolecular crystals: A comparison of the Ewald and truncated list methods , 1993 .

[16]  Matej Praprotnik,et al.  A macromolecule in a solvent: adaptive resolution molecular dynamics simulation. , 2007, The Journal of chemical physics.

[17]  James Demmel,et al.  Applied Numerical Linear Algebra , 1997 .

[18]  J Moult,et al.  Comparison of database potentials and molecular mechanics force fields. , 1997, Current opinion in structural biology.

[19]  A J Chorin,et al.  Optimal prediction and the Mori-Zwanzig representation of irreversible processes. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Jennifer Chayes,et al.  On the validity of the inverse conjecture in classical density functional theory , 1984 .

[21]  R. Mcgreevy,et al.  Structural modelling of glasses using reverse Monte Carlo simulation , 1990, Nature.

[22]  K Schulten,et al.  VMD: visual molecular dynamics. , 1996, Journal of molecular graphics.

[23]  Alexander P. Lyubartsev,et al.  OSMOTIC AND ACTIVITY COEFFICIENTS FROM EFFECTIVE POTENTIALS FOR HYDRATED IONS , 1997 .

[24]  K. Dill,et al.  Statistical potentials extracted from protein structures: how accurate are they? , 1996, Journal of molecular biology.

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

[26]  Adrien Leygue,et al.  Systematic Coarse Graining of 4-Cyano-4'-pentylbiphenyl , 2011 .

[27]  K. Sharp,et al.  Potential energy functions for protein design. , 2007, Current opinion in structural biology.

[28]  G. Voth Coarse-Graining of Condensed Phase and Biomolecular Systems , 2008 .

[29]  Valentina Tozzini,et al.  Coarse-grained models for proteins. , 2005, Current opinion in structural biology.

[30]  Matej Praprotnik,et al.  Multiscale simulation of soft matter: from scale bridging to adaptive resolution. , 2008, Annual review of physical chemistry.

[31]  Berk Hess,et al.  GROMACS 3.0: a package for molecular simulation and trajectory analysis , 2001 .

[32]  Wataru Shinoda,et al.  Coarse-grained potential models for phenyl-based molecules: II. Application to fullerenes. , 2010, The journal of physical chemistry. B.

[33]  Alexandre J. Chorin Conditional Expectations and Renormalization , 2003, Multiscale Model. Simul..

[34]  Frank L. H. Brown,et al.  Implicit solvent simulation models for biomembranes , 2005, European Biophysics Journal.

[35]  Wataru Shinoda,et al.  Coarse-grained potential models for phenyl-based molecules: I. Parametrization using experimental data. , 2010, The journal of physical chemistry. B.

[36]  E. Lieb,et al.  The inverse problem in classical statistical mechanics , 1984 .

[37]  Florian Müller-Plathe,et al.  Coarse-graining in polymer simulation: from the atomistic to the mesoscopic scale and back. , 2002, Chemphyschem : a European journal of chemical physics and physical chemistry.

[38]  B. Honig,et al.  On the formation of protein tertiary structure on a computer. , 1978, Proceedings of the National Academy of Sciences of the United States of America.

[39]  W G Noid,et al.  Generalized Yvon-Born-Green theory for molecular systems. , 2009, Physical review letters.

[40]  Kurt Kremer,et al.  Bridging the Gap Between Atomistic and Coarse-Grained Models of Polymers: Status and Perspectives , 2000 .

[41]  Gregory A Voth,et al.  The multiscale coarse-graining method. VI. Implementation of three-body coarse-grained potentials. , 2010, The Journal of chemical physics.

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

[43]  Ilpo Vattulainen,et al.  Multiscale modeling of emergent materials: biological and soft matter. , 2009, Physical chemistry chemical physics : PCCP.

[44]  M. Parrinello,et al.  Strain fluctuations and elastic constants , 1982 .

[45]  William George Noid,et al.  Extended ensemble approach for deriving transferable coarse-grained potentials , 2009 .

[46]  John Aurie Dean,et al.  Lange's Handbook of Chemistry , 1978 .

[47]  R. Jernigan,et al.  Structure-derived potentials and protein simulations. , 1996, Current opinion in structural biology.

[48]  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.

[49]  Hongyi Zhou,et al.  A physical reference state unifies the structure‐derived potential of mean force for protein folding and binding , 2004, Proteins.

[50]  G. Rutledge,et al.  Coarse-grained, density dependent implicit solvent model reliably reproduces behavior of a model surfactant system. , 2009, The Journal of chemical physics.

[51]  Gregory A Voth,et al.  Multiscale modeling of biomolecular systems: in serial and in parallel. , 2007, Current opinion in structural biology.

[52]  A. Chorin,et al.  Stochastic Tools in Mathematics and Science , 2005 .

[53]  Gregory A Voth,et al.  The multiscale coarse-graining method. IV. Transferring coarse-grained potentials between temperatures. , 2009, The Journal of chemical physics.

[54]  I. R. Mcdonald,et al.  Theory of simple liquids , 1998 .

[55]  William George Noid,et al.  A Generalized-Yvon−Born−Green Theory for Determining Coarse-Grained Interaction Potentials† , 2010 .

[56]  William H. Press,et al.  Book-Review - Numerical Recipes in Pascal - the Art of Scientific Computing , 1989 .

[57]  Wataru Shinoda,et al.  Multi-property fitting and parameterization of a coarse grained model for aqueous surfactants , 2007 .

[58]  M. Sippl Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. , 1990, Journal of molecular biology.

[59]  Gerhard Stock,et al.  Conformational dynamics of trialanine in water. 2. Comparison of AMBER, CHARMM, GROMOS, and OPLS force fields to NMR and infrared experiments , 2003 .

[60]  Cecilia Clementi,et al.  Coarse-grained models of protein folding: toy models or predictive tools? , 2008, Current opinion in structural biology.

[61]  J. Banavar,et al.  Computer Simulation of Liquids , 1988 .

[62]  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.

[63]  Hoover,et al.  Canonical dynamics: Equilibrium phase-space distributions. , 1985, Physical review. A, General physics.

[64]  Margaret E. Johnson,et al.  Representability problems for coarse-grained water potentials. , 2007, The Journal of chemical physics.

[65]  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.

[66]  Berend Smit,et al.  Understanding molecular simulation: from algorithms to applications , 1996 .

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

[68]  Chris Oostenbrink,et al.  A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force‐field parameter sets 53A5 and 53A6 , 2004, J. Comput. Chem..

[69]  Michael L. Klein,et al.  A coarse grain model for n-alkanes parameterized from surface tension data , 2003 .

[70]  Gregory A Voth,et al.  Multiscale coarse-graining and structural correlations: connections to liquid-state theory. , 2007, The journal of physical chemistry. B.

[71]  W. L. Jorgensen,et al.  Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids , 1996 .

[72]  D. Thirumalai,et al.  Pair potentials for protein folding: Choice of reference states and sensitivity of predicted native states to variations in the interaction schemes , 2008, Protein science : a publication of the Protein Society.

[73]  Wataru Shinoda,et al.  A Transferable Coarse Grain Non-bonded Interaction Model For Amino Acids. , 2009, Journal of chemical theory and computation.

[74]  Gregory A Voth,et al.  Multiscale coarse graining of liquid-state systems. , 2005, The Journal of chemical physics.

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

[76]  Gregory C Rutledge,et al.  Evaluating the transferability of coarse-grained, density-dependent implicit solvent models to mixtures and chains. , 2009, The Journal of chemical physics.

[77]  R. L. McGreevy,et al.  Reverse Monte Carlo Simulation: A New Technique for the Determination of Disordered Structures , 1988 .

[78]  M. Levitt A simplified representation of protein conformations for rapid simulation of protein folding. , 1976, Journal of molecular biology.

[79]  Yuko Okamoto,et al.  Secondary-structure preferences of force fields for proteins evaluated by generalized-ensemble simulations , 2004 .

[80]  A. Godzik,et al.  Derivation and testing of pair potentials for protein folding. When is the quasichemical approximation correct? , 1997, Protein science : a publication of the Protein Society.

[81]  Berend Smit,et al.  Molecular Dynamics Simulations , 2002 .

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

[83]  Gregory A Voth,et al.  The multiscale coarse-graining method. II. Numerical implementation for coarse-grained molecular models. , 2008, The Journal of chemical physics.

[84]  S. Doniach,et al.  A computer model to dynamically simulate protein folding: Studies with crambin , 1989, Proteins.

[85]  R. Jernigan,et al.  Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation , 1985 .

[86]  Kurt Kremer,et al.  Simulation of polymer melts. I. Coarse‐graining procedure for polycarbonates , 1998 .

[87]  E. Shakhnovich,et al.  Analysis of knowledge‐based protein‐ligand potentials using a self‐consistent method , 2008, Protein science : a publication of the Protein Society.

[88]  D. Tieleman,et al.  The MARTINI force field: coarse grained model for biomolecular simulations. , 2007, The journal of physical chemistry. B.

[89]  A. Mark,et al.  Coarse grained model for semiquantitative lipid simulations , 2004 .

[90]  David Chandler,et al.  Roles of Repulsive and Attractive Forces in Liquids : The Equilibrium Theory of Classical Fluids , 2007 .

[91]  Ilpo Vattulainen,et al.  Systematic coarse graining from structure using internal states: application to phospholipid/cholesterol bilayer. , 2009, The Journal of chemical physics.

[92]  W G Noid,et al.  Recovering physical potentials from a model protein databank , 2010, Proceedings of the National Academy of Sciences.