Effects of Biomolecular Flexibility on Alchemical Calculations of Absolute Binding Free Energies.

The independent trajectory thermodynamic integration (IT-TI) approach (Lawrenz et. al J. Chem. Theory. Comput. 2009, 5:1106-1116(1)) for free energy calculations with distributed computing is employed to study two distinct cases of protein-ligand binding: first, the influenza surface protein N1 neuraminidase bound to the inhibitor oseltamivir, and second, the M. tuberculosis enzyme RmlC complexed with the molecule CID 77074. For both systems, finite molecular dynamics (MD) sampling and varied molecular flexibility give rise to IT-TI free energy distributions that are remarkably centered on the target experimental values, with a spread directly related to protein, ligand, and solvent dynamics. Using over 2 μs of total MD simulation, alternative protocols for the practical, general implementation of IT-TI are investigated, including the optimal use of distributed computing, the total number of alchemical intermediates, and the procedure to perturb electrostatics and van der Waals interactions. A protocol that maximizes predictive power and computational efficiency is proposed. IT-TI outperforms traditional TI predictions and allows a straightforward evaluation of the reliability of free energy estimates. Our study has broad implications for the use of distributed computing in free energy calculations of macromolecular systems.

[1]  T. Darden,et al.  Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems , 1993 .

[2]  Christophe Chipot,et al.  Good practices in free-energy calculations. , 2010, The journal of physical chemistry. B.

[3]  J. Kirkwood.  Statistical Mechanics of Fluid Mixtures , 1935 .

[4]  J. Andrew McCammon,et al.  Independent-Trajectories Thermodynamic-Integration Free-Energy Changes for Biomolecular Systems: Determinants of H5N1 Avian Influenza Virus Neuraminidase Inhibition by Peramivir , 2009, Journal of chemical theory and computation.

[5]  David L Mobley,et al.  Predicting ligand binding affinity with alchemical free energy methods in a polar model binding site. , 2009, Journal of molecular biology.

[6]  K. Dill,et al.  Predicting absolute ligand binding free energies to a simple model site. , 2007, Journal of molecular biology.

[7]  S. Diamond,et al.  Identification of triazinoindol-benzimidazolones as nanomolar inhibitors of the Mycobacterium tuberculosis enzyme TDP-6-deoxy-d-xylo-4-hexopyranosid-4-ulose 3,5-epimerase (RmlC). , 2010, Bioorganic & medicinal chemistry.

[8]  W. L. Jorgensen.  The Many Roles of Computation in Drug Discovery , 2004, Science.

[9]  B. Brooks,et al.  Constant pressure molecular dynamics simulation: The Langevin piston method , 1995 .

[10]  Klaus Schulten,et al.  Molecular Dynamics Simulations Suggest that Electrostatic Funnel Directs Binding of Tamiflu to Influenza N1 Neuraminidases , 2010, PLoS Comput. Biol..

[11]  García,et al.  Large-amplitude nonlinear motions in proteins. , 1992, Physical review letters.

[12]  J. Andrew McCammon,et al.  Dynamics and design of enzymes and inhibitors , 1986 .

[13]  T. Straatsma,et al.  Separation‐shifted scaling, a new scaling method for Lennard‐Jones interactions in thermodynamic integration , 1994 .

[14]  Peter A. Kollman,et al.  FREE ENERGY CALCULATIONS : APPLICATIONS TO CHEMICAL AND BIOCHEMICAL PHENOMENA , 1993 .

[15]  J. Åqvist,et al.  Ion-water interaction potentials derived from free energy perturbation simulations , 1990 .

[16]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[17]  David J. Stevens,et al.  The structure of H5N1 avian influenza neuraminidase suggests new opportunities for drug design , 2006, Nature.

[18]  Stefan Boresch,et al.  Absolute Binding Free Energies: A Quantitative Approach for Their Calculation , 2003 .

[19]  Michael R. Shirts,et al.  Direct calculation of the binding free energies of FKBP ligands. , 2005, The Journal of chemical physics.

[20]  M. Gilson,et al.  Calculation of protein-ligand binding affinities. , 2007, Annual review of biophysics and biomolecular structure.

[21]  Wilfred F van Gunsteren,et al.  Computational Analysis of the Mechanism and Thermodynamics of Inhibition of Phosphodiesterase 5A by Synthetic Ligands. , 2007, Journal of chemical theory and computation.

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

[23]  M. Geller,et al.  Water properties inside nanoscopic hydrophobic pocket studied by computer simulations. , 2006, The Journal of chemical physics.

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

[25]  F. Hayden,et al.  In Vitro Characterization of A-315675, a Highly Potent Inhibitor of A and B Strain Influenza Virus Neuraminidases and Influenza Virus Replication , 2002, Antimicrobial Agents and Chemotherapy.

[26]  H. Berendsen,et al.  Essential dynamics of proteins , 1993, Proteins.

[27]  H. C. Andersen.  Rattle: A “velocity” version of the shake algorithm for molecular dynamics calculations , 1983 .

[28]  Gerhard Hummer,et al.  Molecular binding: Under water's influence. , 2010, Nature chemistry.

[29]  B. Berne,et al.  Dewetting transitions in protein cavities , 2010, Proteins.

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

[31]  Ignacio J General.  A Note on the Standard State's Binding Free Energy. , 2010, Journal of chemical theory and computation.

[32]  Rommie E. Amaro,et al.  Impact of calcium on N1 influenza neuraminidase dynamics and binding free energy , 2010, Proteins.

[33]  V. Hornak,et al.  Comparison of multiple Amber force fields and development of improved protein backbone parameters , 2006, Proteins.

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

[35]  Laxmikant V. Kalé,et al.  Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..

[36]  Jung-Hsin Lin,et al.  Remarkable loop flexibility in avian influenza N1 and its implications for antiviral drug design. , 2007, Journal of the American Chemical Society.

[37]  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.

[38]  W. L. Jorgensen,et al.  Monte Carlo simulation of differences in free energies of hydration , 1985 .

[39]  James Andrew McCammon,et al.  Ligand-receptor interactions , 1984, Comput. Chem..

[40]  D. Beveridge,et al.  Free energy via molecular simulation: applications to chemical and biomolecular systems. , 1989, Annual review of biophysics and biophysical chemistry.

[41]  J Andrew McCammon,et al.  Dynamics, hydration, and motional averaging of a loop-gated artificial protein cavity: the W191G mutant of cytochrome c peroxidase in water as revealed by molecular dynamics simulations. , 2007, Biochemistry.

[42]  Jonathan Greer,et al.  Influenza neuraminidase inhibitors: structure-based design of a novel inhibitor series. , 2003, Biochemistry.

[43]  R. Baron,et al.  Water in Cavity−Ligand Recognition , 2010, Journal of the American Chemical Society.

[44]  J. Andrew McCammon,et al.  How Can Hydrophobic Association Be Enthalpy Driven? , 2010, Journal of chemical theory and computation.