Rapid estimation of hydration thermodynamics of macromolecular regions.

This work describes a novel protocol to efficiently calculate the local free energy of hydration of specific regions in macromolecules. The method employs Monte Carlo simulations in the grand canonical ensemble to generate water configurations in a selected spherical region in the macromolecule. Excess energy and entropy of hydration are calculated by analyzing the water configurational distributions following the recently published grid inhomogeneous solvation theory method [C. N. Nguyen, T. K. Young, and M. K. Gilson, J. Chem. Phys. 137, 044101 (2012)]. Our method involves the approximations of treating the macromolecule and distant solvent as rigid and performing calculations on multiple such conformations to account for conformational diversity. These approximations are tested against water configurations obtained from a molecular dynamics simulation. The method is validated by predicting the number and location of water molecules in 5 pockets in the protein Interleukin-1β for which experimental water occupancy data are available. Free energy values are validated against decoupling free energy perturbation calculations. The results indicate that the approximations used in the method enable efficient prediction of free energies of water displacement.

[1]  Donald Hamelberg,et al.  Standard free energy of releasing a localized water molecule from the binding pockets of proteins: double-decoupling method. , 2004, Journal of the American Chemical Society.

[2]  Benoît Roux,et al.  Grand canonical Monte Carlo simulations of water in protein environments. , 2004, The Journal of chemical physics.

[3]  Alexander D. MacKerell,et al.  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 , 2004, J. Comput. Chem..

[4]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[5]  W. L. Jorgensen,et al.  Energetics of displacing water molecules from protein binding sites: consequences for ligand optimization. , 2009, Journal of the American Chemical Society.

[6]  Michael K Gilson,et al.  Grid inhomogeneous solvation theory: hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril. , 2012, The Journal of chemical physics.

[7]  Robert Abel,et al.  Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding , 2007, Proceedings of the National Academy of Sciences.

[8]  Renxiao Wang,et al.  Analysis of Ligand-Bound Water Molecules in High-Resolution Crystal Structures of Protein-Ligand Complexes , 2007, J. Chem. Inf. Model..

[9]  T. Lazaridis Inhomogeneous Fluid Approach to Solvation Thermodynamics. 1. Theory , 1998 .

[10]  L. Nilsson,et al.  Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K , 2001 .

[11]  Carsten Kutzner,et al.  GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. , 2008, Journal of chemical theory and computation.

[12]  F. Guarnieri,et al.  Diverse fragment clustering and water exclusion identify protein hot spots. , 2011, Journal of the American Chemical Society.

[13]  Paolo Carnevali,et al.  Fragment-Based Computation of Binding Free Energies by Systematic Sampling , 2009, J. Chem. Inf. Model..

[14]  B. Matthews,et al.  Determination of solvent content in cavities in IL-1β using experimentally phased electron density , 2006, Proceedings of the National Academy of Sciences.

[15]  J A McCammon,et al.  Enzyme-inhibitor association thermodynamics: explicit and continuum solvent studies. , 1997, Biophysical journal.

[16]  Mihaly Mezei,et al.  Grand-canonical ensemble Monte Carlo study of dense liquid Lennard-Jones, soft spheres and water , 1987 .

[17]  Mihaly Mezei,et al.  Grand Canonical Monte Carlo Simulation of Water Positions in Crystal Hydrates , 1994 .

[18]  Mike C. Payne,et al.  Thermodynamic Properties of Water Molecules at a Protein–Protein Interaction Surface , 2011, Journal of chemical theory and computation.

[19]  Correction to “Symmetry Numbers for Rigid, Flexible, and Fluxional Molecules: Theory and Applications” , 2013 .

[20]  D. J. Adams,et al.  Grand canonical ensemble Monte Carlo for a Lennard-Jones fluid , 1975 .

[21]  Alexander D. MacKerell,et al.  All-atom empirical potential for molecular modeling and dynamics studies of proteins. , 1998, The journal of physical chemistry. B.

[22]  Gerhard Hummer,et al.  Water in the polar and nonpolar cavities of the protein interleukin-1β. , 2010, The journal of physical chemistry. B.

[23]  B. Berne,et al.  Role of the active-site solvent in the thermodynamics of factor Xa ligand binding. , 2008, Journal of the American Chemical Society.

[24]  M. Mezei,et al.  Prediction of protein loop structures using a local move Monte Carlo approach and a grid-based force field. , 2008, Protein engineering, design & selection : PEDS.

[25]  M Mezei,et al.  Grand canonical ensemble Monte Carlo simulation of the dCpG/proflavine crystal hydrate. , 1996, Biophysical journal.

[26]  M. Gilson,et al.  The statistical-thermodynamic basis for computation of binding affinities: a critical review. , 1997, Biophysical journal.