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5 - Free-energy calculations in structure-based drug design

from PART II - COMPUTATIONAL CHEMISTRY METHODOLOGY

Published online by Cambridge University Press:  06 July 2010

By
Michael R. Shirts ,
David L. Mobley  and
Scott P. Brown
Edited by
Kenneth M. Merz, Jr ,
Dagmar Ringe  and
Charles H. Reynolds
Kenneth M. Merz, Jr
Affiliation:
University of Florida
Dagmar Ringe
Affiliation:
Brandeis University, Massachusetts
Charles H. Reynolds
Affiliation:
Johnson & Johnson Pharmaceutical Research & Development
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Summary

INTRODUCTION

The ultimate goal of structure-based drug design is a simple, robust process that starts with a high-resolution crystal structure of a validated biological macromolecular target and reliably generates an easily synthesized, high-affinity small molecule with desirable pharmacological properties. Although pharmaceutical science has made significant gains in understanding how to generate, test, and validate small molecules for specific biochemical activity, such a complete process does not now exist. In any drug design project, enormous amounts of luck, intuition, and trial and error are still necessary.

For any small molecule to be considered a likely drug candidate, it must satisfy a number of different absorption/distribution/metabolism/excretion (ADME) properties and have a good toxicological profile. However, a small molecule must above all be active, which in most cases means that it must bind tightly and selectively to a specific location in the protein target before any of the other important characteristics are relevant. To design a drug, large regions of chemical space must be explored to find candidate molecules with the desired biological activity. High-throughput experimental screening methods have become the workhorse for finding such hits. However, their results are limited by the quality and diversity of the preexisting chemical libraries, which may contain only molecules representative of a limited portion of the relevant chemical space for a given target. Combinatorial libraries can be produced to supplement these efforts, but their use requires careful design strategies and they are subject to a number of pitfalls.

Type
Chapter
Information
Drug Design
Structure- and Ligand-Based Approaches
 , pp. 61 - 86
Publisher: Cambridge University Press
Print publication year: 2010

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References

Houston, J. G.; Banks, M. N.; Binnie, A.; Brenner, S.; O'Connell, J.; Petrillo, E. W.Case study: impact of technology investment on lead discovery at Bristol Myers Squibb, 1998–2006. Drug Discov. Today 2008, 13, 44–51.Google Scholar
Keseru, G. M.; Makara, G. M.Hit discovery and hit-to-lead approaches. Drug Discov. Today 2006, 11, 741–748.Google Scholar
Koppitz, M.; Eis, K.Automated medicinal chemistry. Drug Discov. Today 2006, 11, 561–568.Google Scholar
Perola, E.; Walters, W.; Charifson, P. S.A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins 2004, 56, 235–249.Google Scholar
Warren, G. L.; Andrews, C. W.; Capelli, A.-M.; Clarke, B.; LaLonde, J.; Lambert, M. H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Sender, S.; Tedesco, G.; Wall, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. S.A critial assessment of docking programs and scoring functions. J. Med. Chem. 2006, 49(20), 5912–5931.Google Scholar
Enyedy, I.Egan, W.Can we use docking and scoring for hit-to-lead optimization?J. Comput. Aided Mol. Des. 2008.Google Scholar
Hajduk, P. J.; Sauer, D. R.Statistical analysis of the effects of common chemical substituents on ligand potency. J. Med. Chem. 2008, 51, 553–564.Google Scholar
Shirts, M. R.; Mobley, D. L.; Chodera, J. D.Alchemical free energy calculations: ready for prime time?Annu. Rep. Comput. Chem. 2007, 3, 41–59.Google Scholar
Huang, N.; Jacobson, M. P.Physics-based methods for studying protein-ligand interactions. Curr. Opin. Drug Di. De. 2007, 10, 325–331.Google Scholar
Gilson, M. K.; Zhou, H.-X.Calculation of protein-ligand binding affinities. Annu. Rev. Biophys. Biomed. 2007, 36, 21–42.Google Scholar
Meirovitch, H.Recent developments in methodologies for calculating the entropy and free energy of biological systems by computer simulation. Curr. Opin. Struc. Biol. 2007, 17, 181–186.Google Scholar
Ytreberg, F. M.; Swendsen, R. H.; Zuckerman, D. M.Comparison of free energy methods for molecular systems. J. Chem. Phys. 2006, 125, 184114.Google Scholar
Rodinger, T.; Pomès, R.Enhancing the accuracy, the efficiency and the scope of free energy simulations. Curr. Opin. Struc. Biol. 2005, 15, 164–170.Google Scholar
Brandsdal, B. O.; Österberg, F.; Almlöf, M.; Feierberg, I.; Luzhkov, V. B.; Åqvist, J.Free energy calculations and ligand binding. Adv. Prot. Chem. 2003, 66, 123–158.Google Scholar
Chipot, C.; Pohorille, A.; Eds. Free Energy Calculations: Theory and Applications in Chemistry and Biology, Vol. 86. New York: Springer, 2007.
Frenkel, D.; Smit, B.Understanding Molecular Simulation: from Algorithms to Applications. San Diego, CA: Academic Press; 2002.
Leach, A. R.Molecular Modelling: Principles and Applications. Harlow, Essex, England: Addison Wesley Longman Limited; 1996.
Allen, M. P.; Tildesley, D. J.Computer Simulation of Liquids. New York: Oxford University Press; 1987.
Woods, C. J.; Manby, F. R.; Mulholland, A. J.An efficient method for the calculation of quantum mechanics/molecular mechanics free energies. J. Chem. Phys. 2008, 128(1), 014109.Google Scholar
Kollman, P. A.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W.; Donini, O.; Cieplak, P.; Srinivasan, J.; Case, D. A.; Cheatham, T. E.Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc. Chem. Res. 2000, 33, 889–897.Google Scholar
Lee, M. S.; Olson, M. A.Calculation of absolute protein-ligand binding affinity using path and endpoint approaches. Biophys J. 2006, 90, 864–877.Google Scholar
Gohlke, H.; Case, D. A.Converging free energy estimates: Mm-pb(gb)sa studies on the protein-protein complex ras-raf. J. Comput. Chem. 2004, 25, 238–250.Google Scholar
Swanson, J. M. J.; Henchman, R. H.; McCammon, J. A.Revisiting free energy calculations: a theoretical connection to MM/PBSA and direct calculation of the association free energy. Biophys J. 2004, 86, 67–74.Google Scholar
Daggett, V.Long timescale simulations. Curr. Opin. Struc. Biol. 2000, 10, 160–164.Google Scholar
Sitkoff, D.; Sharp, K.; Honig, B. H.Accurate calculation of hydration free energies using macroscopic solvent models. J. Phys. Chem. 1994, 98, 1978–1988.Google Scholar
Honig, B. H.; Nicholls, A.Classical electrostatics in biology and chemistry. Science 1995, 268, 1144–1149.Google Scholar
Gilson, M. K.; Sharp, K.; Honig, B. H.Calculating the electrostatic potential of molecules in solution: method and error assessment. J. Comput. Chem. 1987, 9, 327–335.Google Scholar
Gilson, M. K.; Honig, B. H.Calculation of the total electrostatic energy of a macromolecular system: solvation energies, binding energies, and conformational analysis. Proteins 1988, 4, 7–18.Google Scholar
Rubinstein, A.; Sherman, S.Influence of the solvent structure on the electrostatic interactions in proteins. Biophys J. 2004, 87, 1544–1557.Google Scholar
Swanson, J. M. J.; Mongan, J.; McCammon, J. A.Limitations of atom-centered dielectric functions in implicit solvent models. J. Phys. Chem. B 2005, 109, 14769–14772.Google Scholar
Schutz, C. N.; Warshel, A.What are the dielectric “constants” of proteins and how to validate electrostatic models?Proteins 2001, 44, 400–417.Google Scholar
Archontis, G.; Simonson, T.; Karplus, M.Binding free energies and free energy components from molecular dynamics and Poisson-Boltzmann calculations: Application to amino acid recognition by aspartyl-tRNA. J. Mol. Biol. 2001, 306, 307–327.Google Scholar
Sharp, K.; Honig, B. H.Electrostatic interactions in macromolecules: theory and applications. Annu. Rev. Biophys. Biol. 1990, 19, 301–332.Google Scholar
Richards, F. M.Areas, volumes, packing and protein structure. Annu. Rev. Biophys. Biol. 1977, 6, 151–176.Google Scholar
Sharp, K.; Nicholls, A.; Fine, R. F.; Honig, B. H.Reconciling the magnitude of the microscopic and macroscopic hydrophobic effects. Science 1991, 252, 106–109.Google Scholar
Hermann, R. B.Theory of hydrophobic bonding. II. The correlation of hydrocarbon solubility in water with solvent cavity surface area. J. Phys. Chem. 1972, 76, 2754–2759.Google Scholar
Reynolds, J. A.; Gilbert, D. B.; Tanford, C.Empirical correlation between hydrophobic free energy and aqueous cavity surface area. Proc. Natl. Acad. Sci. U.S.A. 1974, 71, 2925–2927.Google Scholar
Gallicchio, E.; Kubo, M. M.; Levy, R. M.Enthalpy-entropy and cavity decomposition of alkane hydration free energies: Numerical results and implications for theories of hydrophobic solvation. J. Phys. Chem. B 2000, 104, 6271–6285.Google Scholar
Wagoner, J. A.; Baker, N. A.Assessing implicit models for nonpolar mean solvation forces: the importance of dispersion and volume terms. P. Natl. Acad. Sci. U.S.A. 2006, 103, 8331–8336.Google Scholar
Tan, C.; Tan, Y.-H.; Luo, R.Implicit nonpolar solvent models. J. Phys. Chem. B 2007, 111, 12263–12274.Google Scholar
Levy, R. M.; Zhang, L. Y.; Gallicchio, E.; Felts, T.On the nonpolar hydration free energy of proteins: surface area and continuum solvent models for the solute-solvent interaction energy. J. Am. Chem. Soc. 2003, 125, 9523–9530.Google Scholar
Luo, H.; Sharp, K.On the calculation of absolute macromolecular binding free energies. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 10399–10404.Google Scholar
Brooks, B. R.; Janezic, D.; Karplus, M.Harmonic analysis of large systems. I. Methodology. J. Comput. Chem. 2004, 16, 1522–1542.Google Scholar
Bohm, H. J.The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput. Aid. Mol. Des. 1994, 8, 243–256.Google Scholar
Hnizdo, V.; Tan, J.; Killian, B. J.; Gilson, M. K.Efficient calculation of configurational entropy from molecular simulations by combining the mutual-information expansion and nearest-neighbor methods. J. Comput. Chem. 2008, 29(10), 1605–1614.Google Scholar
Froloff, N.; Windemuth, A.; Honig, B. H.On the calculation of binding free energies using continuum methods: application to MHC class I protein-peptide interactions. Protein Sci. 1997, 6, 1293–301.Google Scholar
Hünenberger, P. H.; Helms, V.; Narayana, N.; Taylor, S. S.; McCammon, J. A.Determinants of ligand binding to camp-dependent protein kinase. Biochemistry 1999, 38, 2358–2366.Google Scholar
Jian Shen, J. W.Electrostatic binding energy calculation using the finite difference solution to the linearized Poisson-Boltzmann equation: assessment of its accuracy. J. Comput. Chem. 1996, 17, 350–357.Google Scholar
Schapira, M.; Torrv, M.Prediction of the binding energy for small molecules, peptides and proteins. J. Mol. Recognit. 1999, 12, 177–190.Google Scholar
Mobley, D. L.; Dill, K. A.; Chodera, J. D.Treating entropy and conformational changes in implicit solvent simulations of small molecules. J. Phys. Chem. B 2008, 112, 938–946.Google Scholar
Olson, M. A.Modeling loop reorganization free energies of acetylcholinesterase: a comparison of explicit and implicit solvent models. Proteins 2004, 57, 645–650.Google Scholar
Brown, S. P.; Muchmore, S. W.High-throughput calculation of protein-ligand binding affinities: modification and adaptation of the MM-PBSA protocol to enterprise grid computing. J. Chem. Inf. Model. 2006, 46, 999–1005.Google Scholar
Bashford, D.; Case, , D. A. Generalized born models of macromolecular solvation effects. Annu. Rev. Phys. Chem. 2000, 51, 129–152.Google Scholar
Onufriev, A.; Bashford, D.; Case, D. A.Modification of the generalized Born model suitable for macromolecules. J. Phys. Chem. B 2000, 104, 3712–3720.Google Scholar
Onufriev, A.; Case, D. A.; Bashford, D.Effective Born radii in the generalized Born approximation: the importance of being perfect. J. Comput. Chem. 2002, 23, 1297–1304.Google Scholar
Feig, M.; Onufriev, A.; Lee, M. S.; Im, W.; Case, D. A.; Brooks, C. L.. Performance comparison of generalized Born and Poisson methods in the calculation of electrostatic solvation energies for protein structures. J. Comput. Chem. 2004, 25, 265–284.Google Scholar
Geney, R.; Layten, M.; Gomperts, R.; Hornak, V.; Simmerling, C.Investigation of salt bridge stability in a generalized born solvent model. J. Chem. Theory Comput. 2006, 2, 115–127.Google Scholar
Nymeyer, H.; Garcia, A. E.Simulation of the folding equilibrium of alpha-helical peptides: a comparison of the generalized born approximation with explicit solvent. Proc. Natl. Acad. Sci. U.S.A. 2003, 100, 13934–13939.Google Scholar
Gilson, M. K.; Honig, B. H.The inclusion of electrostatic hydration energies in molecular mechanics calculations. J. Comput. Aid. Mol. Des. 1991, 5, 5–20.Google Scholar
Head, M. S.; Given, J. A.; Gilson, M. K.“Mining Minima”: direct computation of conformational free energy. J. Phys. Chem. A 1997, 101, 1609–1618.Google Scholar
Kolossváry, I.Evaluation of the molecular configuration integral in all degrees of freedom for the direct calculation of conformational free energies: prediction of the anomeric free energy of monosaccharides. J. Phys. Chem. A 1997, 101(51), 9900–9905.Google Scholar
Ragusa, A.; Hayes, J. M.; Light, M. E.; Kilburn, J. D.Predicting enantioselectivity: computation as an efficient experimental tool for probing enantioselectivity. Eur. J. Org. Chem. 2006, 2006(9), 3545–3549.Google Scholar
Ragusa, A.; Hayes, J. M.; Light, M. E.; Kilburn, J. D.A combined computational and experimental approach for the analysis of the enantioselective potential of a new macrocyclic receptor for n-protected α-amino acids. Chem. Eur. J. 2007, 13(9), 2717–2728.Google Scholar
Potter, M. J.; Gilson, M. K.Coordinate systems and the calculation of molecular properties. J. Phys. Chem. A 2002, 106(3), 563–566.Google Scholar
Chang, C.-E. A.; Gilson, M. K.Free energy, entropy, and induced fit in host-guest recognition: calculations with the second-generation mining minima algorithm. J. Am. Chem. Soc. 2004, 126(40), 13156–13164.Google Scholar
Chang, C.-E.; Chen, W.; Gilson, M. K.Calculation of cyclodextrin binding affinities: energy, entropy, and implications for drug design. Biophys. J. 2004, 87, 3035–3049.Google Scholar
Chen, W.; Chang, C.-E. A.; Gilson, M. K.Concepts in receptor optimization: Targeting the rgd peptide. J. Am. Chem. Soc. 2006, 128(14), 4675–4684.Google Scholar
Chang, C.-E. A.; Chen, W.; Gilson, M. K.Ligand configurational entropy and protein binding. Proc. Natl. Acad. Sci. U.S.A. 2007, 104(5), 1534–1539.Google Scholar
Killian, B. J.; Yundenfreund Kravitz, J.; Gilson, M. K.Extraction of configurational entropy from molecular simulations via an expansion approximation. J. Chem. Phys. 2007, 127(2), 024107.Google Scholar
Chang, C.-E. A.; Chen, W.; Gilson, M. K.Evaluating the accuracy of the quasiharmonic approximation. J. Chem. Theory Comput. 2005, 1(5), 1017–1028.Google Scholar
Young, T.; Abel, R.; Kim, B.; Berne, B. J.; Friesner, R. A.Motifs for molecular recognition exploiting hydrophobic enclosure in protein-ligand binding. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 808–813.Google Scholar
Zwanzig, R. W.High-temperature equation of state by a perturbation method. I. Nonpolar gases. J. Chem. Phys. 1954, 22(8), 1420–1426.Google Scholar
Shirts, M. R.; Pande, V. S.Comparison of efficiency and bias of free energies computed by exponential averaging, the bennett acceptance ratio, and thermodynamic integration. J. Chem. Phys. 2005, 122, 144107.Google Scholar
Lu, N. D.; Singh, J. K.; Kofke, D. A.Appropriate methods to combine forward and reverse free-energy perturbation averages. J. Chem. Phys. 2003, 118(7), 2977–2984.Google Scholar
Wu, D.; Kofke, D. A.Asymmetric bias in free-energy perturbation measurements using two Hamiltonian-based models. Phys. Rev. E 2005, 70, 066702.Google Scholar
Jarzynski, C.Rare events and the convergence of exponentially averaged work values. Phys. Rev. E 2006, 73, 046105.Google Scholar
Widom, B.Some topics in the theory of fluids. J. Chem. Phys. 1963, 39(11), 2808–2812.Google Scholar
Jorgensen, W. L.; Ravimohan, C.Monte Carlo simulation of differences in free energies of hydration. J. Chem. Phys. 1985, 83(6), 3050–3054.Google Scholar
Resat, H.; Mezei, M.Studies on free energy calculations. I. Thermodynamic integration using a polynomial path. J. Chem. Phys. 1993, 99(8), 6052–6061.Google Scholar
Pitera, J. W.; Gunsteren, W. F.A comparison of non-bonded scaling approaches for free energy calculations. Mol. Simulat. 2002, 28(1–2), 45–65.Google Scholar
Shirts, M. R.; Pitera, J. W.; Swope, W. C.; Pande, V. S.Extremely precise free energy calculations of amino acid side chain analogs: comparison of common molecular mechanics force fields for proteins. J. Chem. Phys. 2003, 119(11), 5740–5761.Google Scholar
Beutler, T. C.; Mark, A. E.; Schaik, R. C.; Gerber, P. R.; Gunsteren, W. F.Avoiding singularities and numerical instabilities in free energy calculations based on molecular simulations. Chem. Phys. Lett. 1994, 222, 529–539.Google Scholar
Mobley, D. L. Unpublished data.
Pearlman, D. A.; Kollman, P. A.The lag between the Hamiltonian and the system configuration in free-energy perturbation calculations. J. Chem. Phys. 1989, 91(12), 7831–7839.Google Scholar
Hendrix, D. A.; Jarzynski, C.A “fast growth” method of computing free energy differences. J. Chem. Phys. 2001, 114(14), 5974–5961.Google Scholar
Oostenbrink, C.; Gunsteren, W. F.Calculating zeros: nonequilibrium free energy calculations. Chem. Phys. 2006, 323, 102–108.Google Scholar
Oostenbrink, C.; Gunsteren, W. F.Free energies of ligand binding for structurally diverse compounds. Proc. Natl. Acad. Sci. U.S.A. 2005, 102(19), 6750–6754.Google Scholar
Crooks, G. E.Path-ensemble averages in systems driven far from equilibrium. Phys. Rev. E 2000, 61(3), 2361–2366.Google Scholar
Bennett, C. H.Efficient estimation of free energy differences from Monte Carlo data. J. Comput. Phys. 1976, 22, 245–268.Google Scholar
Shirts, M. R.; Bair, E.; Hooker, G.; Pande, V. S.Equilibrium free energies from nonequilibrium measurements using maximum-likelihood methods. Phys. Rev. Lett. 2003, 91(14), 140601.Google Scholar
Rick, S. W.Increasing the efficiency of free energy calculations using parallel tempering and histogram reweighting. J. Chem. Theory Comput. 2006, 2, 939–946.Google Scholar
Ferrenberg, A. M.; Swendsen, R. H.Optimized Monte Carlo data analysis. Phys. Rev. Lett. 1989, 63(12), 1195–1198.Google Scholar
Kumar, S.; Bouzida, D.; Swendsen, R. H.; Kollman, P. A.; Rosenberg, J. M.The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J. Comput. Chem. 1992, 13(8), 1011–1021.Google Scholar
Bartels, C.; Karplus, M.Multidimensional adaptive umbrella sampling: applications to main chain and side chain peptide conformations. J. Comput. Chem. 1997, 18(12), 1450–1462.Google Scholar
Gallicchio, E.; Andrec, M.; Felts, A. K.; Levy, R. M.Temperature weighted histogram analysis method, replica exchange, and transition paths. J. Phys. Chem. B 2005, 109, 6722–6731.Google Scholar
Souaille, M.; Roux, B.Extension to the weighted histogram analysis method: combining umbrella sampling with free energy calculations. Comput. Phys. Commun. 2001, 135(1), 40–57.Google Scholar
Wang, J.; Deng, Y.; Roux, B.Absolute binding free energy calculations using molecular dynamics simulations with restraining potentials. Biophys. J. 2006, 91, 2798–2814.Google Scholar
Shirts, M. R.; Chodera, J. D.Statistically optimal analysis of samples from multiple equilibrium states. J. Chem. Phys. 2008, 129, 129105.Google Scholar
Steinbrecher, T.; Mobley, D. L.; Case, D. A.Nonlinear scaling schemes for Lennard-Jones interactions in free energy calculations. J. Chem. Phys. 2007, 127(21).Google Scholar
Pearlman, D. A.; Connelly, P. R.Determination of the differential effects of hydrogen bonding and water release on the binding of FK506 to native and TYR82 → PHE82 FKBP-12 proteins using free energy simulations. J. Mol. Biol. 1995, 248(3), 696–717.Google Scholar
Wang, L.; Hermans, J.Change of bond length in free-energy simulations: algorithmic improvements, but when is it necessary?J. Chem. Phys. 1994, 100(12), 9129–9139.Google Scholar
Boresch, S.; Karplus, M.The Jacobian factor in free energy simulations. J. Chem. Phys. 1996, 105(12), 5145–5154.Google Scholar
Zacharias, M.; Straatsma, T. P.; McCammon, J. A.Separation-shifted scaling, a new scaling method for lennard-jones interactions in thermodynamic integration. J. Phys. Chem. 1994, 100(12), 9025–9031.Google Scholar
Shirts, M. R.; Pande, V. S.Solvation free energies of amino acid side chains for common molecular mechanics water models. J. Chem. Phys. 2005, 122, 134508.Google Scholar
Blondel, A.Ensemble variance in free energy calculations by thermodynamic integration: theory, optimal alchemical path, and practical solutions. J. Comput. Chem. 2004, 25(7), 985–993.Google Scholar
Rodinger, T.; Howell, P. L.; Pomès, R.Absolute free energy calcualtions by thermodynamic integration in four spatial dimensions. J. Chem. Phys. 2005, 123, 034104.Google Scholar
Lee, M. S.; Olson, M. A.Calculation of absolute protein-ligand binding affinity using path and endpoint approaches. Biophys. J. 2006, 90, 864–877.Google Scholar
Woo, H.-J.; Roux, B.Calculation of absolute protein-ligand binding free energy from computer simulation. Proc. Natl. Acad. Sci. U.S.A. 2005, 102, 6825–6830.Google Scholar
Ytreberg, F. M.Absolute FKBP binding affinities obtained by nonequilibrium unbinding simulations. J. Chem. Phys. 2009, 130, 164906-8.Google Scholar
Gan, W.; Roux, B.Binding specificity of SH2 domains: insight from free energy simulations. Proteins 2008, 74, 996–1007.Google Scholar
Torrie, G. M.; Valleau, J. P.Non-physical sampling distributions in Monte-Carlo free-energy estimation: umbrella sampling. J. Comput. Phys. 1977, 23(2), 187–199.Google Scholar
Mobley, D. L.; Chodera, J. D.; Dill, K. A.Confine-and-release method: obtaining correct binding free energies in the presence of protein conformational change. J. Chem. Theory Comput. 2007, 3(4), 1231–1235.Google Scholar
Mobley, D. L.; Graves, A. P.; Chodera, J. D.; McReynolds, A. C.; Shoichet, B. K.; Dill, K. A.Predicting absolute ligand binding free energies to a simple model site. J. Mol. Biol. 2007, 371(4), 1118–1134.Google Scholar
Okamoto, Y.Generalized-ensemble algorithms: enhanced sampling techniques for Monte Carlo and molecular dynamics simulations. J. Mol. Graph. Model. 2004, 22, 425–439.Google Scholar
Pitera, J. W.; Kollman, P. A.Exhaustive mutagenesis in silico: multicoordinate free energy calculations in proteins and peptides. Proteins 2000, 41, 385–397.Google Scholar
Roux, B.; Faraldo-Gómez, J. D.Characterization of conformational equilibria through hamiltonian and temperature replica-exchange simulations: assessing entropic and environmental effects. J. Comput. Chem. 2007, 28(10), 1634–1647.Google Scholar
Woods, C. J.; Essex, J. W.; King, M. A.Enhanced configurational sampling in binding free energy calculations. J. Phys. Chem. B 2003, 107, 13711–13718.Google Scholar
Banba, S.; Guo, Z.; Brooks, C. L.. Efficient sampling of ligand orientations and conformations in free energy calculations using the lambda-dynamics method. J. Phys. Chem. B, 2000, 104(29),6903–6910.Google Scholar
Bitetti-Putzer, R.; Yang, W.; Karplus, M.Generalized ensembles serve to improve the convergence of free energy simulations. Chem. Phys. Lett. 2003, 377, 633–641.Google Scholar
Hritz, J.; Oostenbrink, C.Hamiltonian replica exchange molecular dynamics using soft-core interactions. J. Chem. Phys. 2008, 128(14), 144121.Google Scholar
Guo, Z.; Brooks, C. L., Kong, X.Efficient and flexible algorithm for free energy calculations using the λ-dynamics approach. J. Phys. Chem. B 1998, 102, 2032–2036.Google Scholar
Kong, X.; Brooks, C. L.. λ-dynamics: a new approach to free energy calculations. J. Chem. Phys. 1996, 105(6), 2414–2423.Google Scholar
Li, H.; Fajer, M.; Yang, W.Simulated scaling method for localized enhanced sampling and simultaneous “alchemical” free energy simulations: a general method for molecular mechanical, quantum mechanical, and quantum mechanical/molecular mechanical simulations. J. Chem. Phys. 2007, 126, 024106.Google Scholar
Zheng, L.; Carbone, I. O.; Lugovskoy, A.; Berg, B. A.; Yang, W.A hybrid recursion method to robustly ensure convergence efficiencies in the simulated scaling based free energy simulations. J. Chem. Phys. 2008, 129(3), 034105.Google Scholar
Zheng, L.; Yang, W.Essential energy space random walks to accelerate molecular dynamics simulations: convergence improvements via an adaptive-length self-healing strategy. J. Chem. Phys. 2008, 129(1), 014105.Google Scholar
Min, D.; Yang, W.Energy difference space random walk to achieve fast free energy calculations. J. Chem. Phys. 2008, 128(19).Google Scholar
Li, H.; Yang, W.Forging the missing link in free energy estimations: lambda-WHAM in thermodynamic integration, overlap histogramming, and free energy perturbation. Chem. Phys. Lett. 2007, 440(1–3), 155–159.Google Scholar
Min, D.; Li, H.; Li, G.; Bitetti-Putzer, R.; Yang, W.Synergistic approach to improve “alchemical” free energy calculation in rugged energy surface. J. Chem. Phys. 2007, 126(14), 144109.Google Scholar
Oostenbrink, C.; Gunsteren, W. F.Free energies of binding of polychlorinated biphenyls to the estrogen receptor from a single simulation. Proteins 2004, 54(2), 237–246.Google Scholar
Oostenbrink, C.; Gunsteren, W. F.Single-step perturbations to calculate free energy differences from unphysical reference states: limits on size, flexibility, and character. J. Comput. Chem. 2003, 24(14), 1730–1739.Google Scholar
Schwab, F.; Gunsteren, W. F.; Zagrovic, B.Computational study of the mechanism and the relative free energies of binding of anticholesteremic inhibitors to squalene-hopene cyclase. Biochemistry 2008, 47(9), 2945–2951.Google Scholar
Srinivasan, J.; Cheatham, T. E.; Cieplak, P.; Kollman, P. A.; Case, D. A.Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate-DNA helices. J. Am. Chem. Soc. 1998, 120, 9401–9409.Google Scholar
Cheatham, T. E.; Srinivasan, J.; Case, D. A.; Kollman, P. A.Molecular dynamics and continuum solvent studies of the stability of polyG-polyC and polyA-polyT DNA duplexes in solution. J. Biomol. Struct. Dyn. 1998, 16, 265–280.Google Scholar
Chong, L.; Duan, Y.; Wang, L.; Massova, I.; Kollman, P. A.Molecular dynamics and free-energy calculations applied to affinity maturation in antibody 48g7. Proc. Natl. Acad. Sci. U.S.A. 1995, 96, 14330–14335.Google Scholar
Foloppe, N; Fisher, L. M.; Howes, R; Kierstan, P; Potter, A; Robertson, A. G.; Surgenor, A. E.Structure-based design of novel CHK1 inhibitors: insights into hydrogen bonding and protein-ligand affinity. J. Med. Chem. 2005, 48, 4332–4345.Google Scholar
Bryce, R. A.; Hillier, I. H.; Naismith, J. H.Carbohydrate-protein recognition: molecular dynamics simulations and free energy analysis of oligosaccharide binding to concanavalin a. Biophys J., 2001, 81, 1373–1388.Google Scholar
Kuhn, B.; Kollman, P. A.; Stahl, M.Prediction of pka shifts in proteins using a combination of molecular mechanical and continuum solvent calculations. J. Comput. Chem. 2004, 25, 1865–1872.Google Scholar
Ferrara, P.; Gohlke, H.; Price, D. J.; Klebe, G.; Brooks, C. L.. Assessing scoring functions for protein-ligand interactions. J. Med. Chem. 2004, 47, 3032–3047.Google Scholar
Massova, I.; Kollman, P. A.Computational alanine scanning to probe protein-protein interactions: a novel approach to evaluate binding free energies. J. Am. Chem. Soc. 1999, 121, 8133–8143.Google Scholar
Kuhn, B.; Kollman, P. A.A ligand that is predicted to bind better to avidin than biotin: insights from computational fluorine scanning. J. Am. Chem. Soc. 2000, 122, 3909–3916.Google Scholar
Kuhn, B.; Kollman, P. A.Binding of a diverse set of ligands to avidin and streptavidin: an accurate quantitative prediction of their relative affinities by a combination of molecular mechanics and continuum solvent models. J. Med. Chem. 2000, 43, 3786–3791.Google Scholar
Huo, S.; Wang, J.; Cieplak, P.; Kollman, P. A.; Kuntz, I. D.Molecular dynamics and free energy analyses of cathepsin d-inhibitor interactions: insight into structure-based ligand design. J. Med. Chem. 2002, 45, 1412–1419.Google Scholar
Mardis, K. L.; Luo, R.; Gilson, M. K.Interpreting trends in the binding of cyclic ureas to HIV-1 protease. J. Mol. Biol. 2001, 309, 507–517.Google Scholar
Schwarzl, S. M.; Tschopp, T. B.; Smith, J. C.; Fischer, S.Can the calculation of ligand binding free energies be improved with continuum solvent electrostatics and an ideal-gas entropy correction?J. Comput. Chem. 2002, 23, 1143–1149.Google Scholar
Rizzo, R. C.; Toba, S; Kuntz, I. D.A molecular basis for the selectivity of thiadiazole urea inhibitors with stromelysin-1 and gelatinase-A from generalized born molecular dynamics simulations. J. Med. Chem. 2004, 47, 3065–3074.Google Scholar
Brown, S. P.; Muchmore, S. W.Rapid estimation of relative protein-ligand binding affinities using a high-throughput version of MM-PBSA. J. Chem. Inf. Model. 2007, 47, 1493–1503.Google Scholar
Kuhn, B.; Gerber, P. R.; Schulz-Gasch, T.; Stahl, M.Validation and use of the MM-PBSA approach for drug discovery. J. Med. Chem. 2005, 48, 4040–4048.Google Scholar
Pearlman, D. A.Evaluating the molecular mechanics Poisson-Boltzmann surface area free energy method using a congeneric series of ligands to p38 map kinase. J. Med. Chem. 2005, 48, 7796–7807.Google Scholar
Weis, A.; Katebzadeh, K.; Söderhjelm, P.; Nilsson, I.; Ryde, U.Ligand affinities predicted with the MM/PBSA method: dependence on the simulation method and the force field. J. Med. Chem. 2006, 49, 6596–6606.Google Scholar
Rafi, S. B.; Cui, G; Song, K; Cheng, X; Tonge, P. J.; Simmerling, C.Insight through molecular mechanics Poisson-Boltzmann surface area calculations into the binding affinity of triclosan and three analogues for FabI, the E. Coli enoyl reductase. J. Med. Chem. 2006, 49, 4574–4580.Google Scholar
Donini, O.; Kollman, P. A.Calculation and prediction of binding free energies for the matrix metalloproteinases. J. Med. Chem. 2000, 43, 4180–4188.Google Scholar
Tembe, B. L.; McCammon, J. A.Ligand-receptor interactions. Comput. Chem. 1984, 8(4), 281–284.Google Scholar
Wong, C. F.; McCammon, J. A.Dynamics and design of enzymes and inhibitors. J. Am. Chem. Soc. 1986, 108(13), 3830–3832.Google Scholar
Hermans, J.; Subramaniam, S.The free energy of xenon binding to myoglobin from molecular dynamics simulation. Israel J. Chem. 1986, 27, 225–227.Google Scholar
Pearlman, D. A.Free energy calculations: methods for estimating ligand binding affinities. In: Free Energy Calculations in Rational Drug Design, Rami Reddy, M.; Erion, M. D.; Eds. Academic/Plenum; New York, NY: Kluwer, 2001.
Chipot, C.Free energy calculations in biomolecular simulations: how useful are they in practice? In: Lecture Notes in Computational Science and Engineering: New Algorithms for Molecular Simulation, Leimkuhler, B.; Chipot, C.; Elber, R.; Laaksonen, A.; Mark, A. E.; Schlick, T.; Schütte, C.; Skeel, R.; Eds. Vol. 49. New York: Springer, 2005, 183–209.
Rizzo, R. C.; Wang, D. P.; Tirado-Rives, J.; Jorgensen, W. L.Validation of a model for the complex of HIV-1 reverse transcriptase with sustiva through computation of resistance profiles. J. Am. Chem. Soc. 2000, 122(51), 12898–12900.Google Scholar
Wang, D.-P.; Rizzo, R. C.; Tirado-Rives, J.; Jorgensen, W. L.Antiviral drug design: computational analyses of the effects of the l100i mutation for HIV-RT on the binding of nnrtis. Bioorgan. Med. Chem. Lett. 2001, 11(21), 2799–2802.Google Scholar
Udier-Blagovic, M.; Tirado-Rives, J.; Jorgensen, W. L.Structural and energetic analyses of the effects of the K103N mutation of HIV-1 reverse transcriptase on efavirenz analogues. J. Med. Chem. 2004, 47(9), 2389–2392.Google Scholar
Kim, J. T.; Hamilton, A. D.; Bailey, C. M.; Domoal, R. A.; Wang, L.; Anderson, K. S.; Jorgensen, W. L.Fep-guided selection of bicyclic heterocycles in lead optimization for non-nucleoside inhibitors of HIV-1 reverse transcriptase. J. Am. Chem. Soc. 2006, 128(48), 15372–15373.Google Scholar
Jorgensen, W. L.; Ruiz-Caro, J.; Tirado-Rives, J.; Basavapathruni, A.; Anderson, K. S.; Hamilton, A. D.Computer-aided design of non-nucleoside inhibitors of HIV-1 reverse transcriptase. Bioorg. Med. Chem. Lett. 2006, 16(3), 663–667.Google Scholar
Kroeger Smith, M. B.; Rader, L. H.; Franklin, A. M.; Taylor, E. V.; Smith, K. D.; Tirado-Rives, J.; Jorgensen, W. L.Energetic effects for observed and unobserved HIV-1 reverse transcriptase mutations of residues l100, v106, and y181 in the presence of nevirapine and efavirenz. Bioorg. Med. Chem. Lett. 2008, 18(3), 969–972.Google Scholar
Zeevaart, J. G.; Wang, L.; Thakur, V. V.; Leung, C. S.; Tirado-Rives, J.; Bailey, C. M.; Domaoal, R. A.; Anderson, K. S.; Jorgensen, W. L.Optimization of azoles as anti-human immunodeficiency virus agents guided by free-energy calculations. J. Am. Chem. Soc. 2008, 120, 9492–9499.Google Scholar
Henin, J.; Maigret, B.; Tarek, M.; Escrieut, C.; Fourmy, D.; Chipot, C.Probing a model of a GPCR/ligand complex in an explicit membrane environment: the human cholecystokinin-1 receptor. Biophys. J. 2006, 90(4), 1232–1240.Google Scholar
Yang, W.; Gao, Y. Q.; Cui, Q.; Ma, J.; Karplus, M.The missing link between thermodynamics and structure in f1-atpase. Proc. Natl. Acad. Sci. U.S.A. 2003, 100(3), 874–879.Google Scholar
Banerjee, A.; Yang, W.; Karplus, M.; Verdine, G. L.Structure of a repair enzyme interrogating undamaged DNA elucidates recognition of damaged DNA. Nature 2005 434, 612–618.Google Scholar
Oostenbrink, C.; Pitera, J. W.; Lipzig, M. M. H.; Meerman, J. H. N.; Gunsteren, W. F.Simulations of the estrogen receptor ligand-binding domain: affinity of natural ligands and xenoestrogens. J. Med. Chem. 2000, 43(24), 4594–4605.Google Scholar
Rami Reddy, M.; Erion, M. D.Calculation of relative binding free energy differences for fructose 1,6-bisphosphatase inhibitors using the thermodynamic cycle perturbation approach. J. Am. Chem. Soc. 2001, 123, 6246–6252.Google Scholar
Steinbrecher, T.; Case, D. A.; Labahn, A.A multistep approach to structure-based drug design: studying ligand binding at the human neutrophil elastase. J. Med. Chem. 2006, 49, 1837–1844.Google Scholar
Gouda, H.; Kuntz, I. D.; Case, D. A.; Kollman, P. A.Free energy calculations for theophylline binding to an RNA aptamer: comparison of MM-PBSA and thermodynamic integration methods. Biopolymers 2003, 68, 16–34.Google Scholar
Tanida, Y.; Ito, M.; Fujitani, H.Calculation of absolute free energy of binding for theophylline and its analogs to RNA aptamer using nonequilibrium work values. Chem. Phys. 2007, 337(1–3), 135–143.Google Scholar
Jiao, D.; Golubkov, P. A.; Darden, T. A.; Ren, P.Calculation of protein-ligand binding free energy using a polarizable potential. Proc. Natl. Acad. Sci. U.S.A. 2008, 105(17), 6290–6295.Google Scholar
Khoruzhii, O.; Donchev, A. G.; Galkin, N.; Illarionov, A.; Olevanov, M.; Ozrin, V.; Queen, C.; Tarasov, V.Application of a polarizable force field to calculations of relative protein-ligand binding affinities. Proc. Natl. Acad. Sci. U.S.A. 2008, 105(30), 10378–10383.Google Scholar
Price, D. J.; Jorgensen, W. L.Improved convergence of binding affinities with free energy perturbation: application to nonpeptide ligands with pp60src SH2 domain. J. Comput. Aided. Mol. Des. 2001, 15, 681–695.Google Scholar
Fowler, P. W.; Jha, S.; Coveney, P. V.Grid-based steered thermodynamic integration accelerates the calculation of binding free energies. Philos. T. R. Soc. B., 2005, 363, 1999–2015.Google Scholar
Chipot, C.; Rozanska, X.; Dixit, S. B.Can free energy calculations be fast and accurate at the same time? Binding of low-affinity, non-peptide inhibitors to the SH2 domain of the src protein. J. Comput. Aided Mol. Des. 2005, 19, 765–770.Google Scholar
Fowler, P. W.; Geroult, S.; Jha, S.; Waksman, G.; Coveney, P. V.Rapid, accurate, and precise calculation of relative binding affinities for the SH2 domain using a computational grid. J. Chem. Theory Comput. 2007, 3(3), 1193–1202.Google Scholar
Pearlman, D. A.; Charifson, P. S.Are free energy calculations useful in practice? A comparison with rapid scoring functions for the p38 map kinase protein system. J. Med. Chem. 2001, 44, 3417–3423.Google Scholar
Mobley, D. L.; Chodera, J. D.; Dill, K. A.On the use of orientational restraints and symmetry corrections in alchemical free energy calculations. J. Chem. Phys. 2006, 125, 084902.Google Scholar
Gilson, M. K.; Given, J. A.; Bush, B. L., McCammon, J. A.A statistical-thermodynamic basis for computation of binding affinities: a critical review. Biophys. J. 1997, 72(3), 1047–1069.Google Scholar
Deng, Y.; Roux, B.Computations of standard binding free energies with molecular dynamics simulations. J. Phys. Chem. B 2008, in press.Google Scholar
Helms, V.; Wade, R. C.Hydration energy landscape of the active site cavity in cytochrome P450cam. Proteins 1998, 32(3), 381–396.Google Scholar
Helms, V.; Wade, R. C.Computational alchemy to calculate absolute protein-ligand binding free energy. J. Am. Chem. Soc. 1998, 120(12), 2710–2713.Google Scholar
Deng, Y.; Roux, B.Computation of binding free energy with molecular dynamics and grand canonical Monte Carlo simulations. J. Chem. Phys. 2008, 128(11).Google Scholar
Zhang, L.; Hermans, J.Hydrophilicity of cavities in proteins. Proteins 1996, 24(4), 433–438.Google Scholar
Olano, L. R.; Rick, S. W.Hydration free energies and entropies for water in protein interiors. J. Am. Chem. Soc. 2004, 126(25), 7991–8000.Google Scholar
Barillari, C.; Taylor, J.; Viner, R.; Essex, J. W.Classification of water molecules in protein binding sites. J. Am. Chem. Soc. 2007, 129(9), 2577–2587.Google Scholar
Hamelberg, D.; McCammon, J. A.Standard free energy of releasing a localized water molecule from the binding pockets of proteins: double-decoupling method. J. Am. Chem. Soc. 2004, 126, 7683–7689.Google Scholar
Lu, Y.; Wang, C.-Y.; Wang, S.Binding free energy contributions of interfacial waters in HIV-1 protease/inhibitor complexes. J. Am. Chem. Soc. 2006, 128, 11830–11839.Google Scholar
Roux, B.; Nina, M.; Pomès, R.; Smith, J. C.Thermodynamic stability of water molecules in the bacteriorhodopsin proton channel: a molecular dynamics free energy perturbation study. Biophys. J. 1996, 71(2), 670–681.Google Scholar
Simone, A.; Dobson, G. G.; Verma, C. S.; Zagari, A.; Fraternali, F.Prion and water: tight and dynamical hydration sites have a key role in structural stability. Proc. Natl. Acad. Sci. U.S.A. 2005, 102(21), 7535–7540.Google Scholar
Pan, C.; Mezei, M.; Mujtaba, S.; Muller, M.; Zeng, L.; Li, J.; Wang, Z.; Zhou, M. M.Structure-guided optimization of small molecules inhibiting human immunodeficiency virus 1 tat association with the human coactivator p300/CREB binding protein-associated factor. J. Med. Chem. 2007, 50(10), 2285–2288.Google Scholar
Eriksson, A. E.; Baase, W. A.; Zhang, X. J.; Heinz, D. W.; Blaber, M.; Baldwin, E. P.; Matthews, B. W.Response of a protein structure to cavity-creating mutations and its relation to the hydrophobic effect. Science 1992, 255, 178–183.Google Scholar
Eriksson, A. E.; Baase, W. A.; Matthews, B. W.Similar hydrophobic replacements of leu99 and phe153 within the core of T4 lysozyme have different structural and thermodynamic consequences. J. Mol. Biol. 1993, 229, 747–769.Google Scholar
Morton, A.; Matthews, B. W.Specificity of ligand binding in a buried nonpolar cavity of T4 lysozyme: linkage of dynamics and structural plasticity. Biochemistry 1995, 34, 8576–8588.Google Scholar
Morton, A.; Baase, W. A.; Matthews, B. W.Energetic origins of specificity of ligand binding in an interior nonpolar cavity of T4 lysozyme. Biochemistry 1995, 34, 8564–8575.Google Scholar
Wei, B. Q.; Baase, W. A.; Weaver, L. H.; Matthews, B. W.; Shoichet, B. K.A model binding site for testing scoring functions in molecular docking. J. Mol. Biol. 2002, 322, 339–355.Google Scholar
Wei, B. Q.; Weaver, L. H.; Ferrari, A. M.; Matthews, B. W.; Shoichet, B. K.Testing a flexible-receptor docking algorithm in a model binding site. J. Mol. Biol. 2004, 337, 1161–1182.Google Scholar
Graves, A. P.; Brenk, R.; Shoichet, B. K.Decoys for docking. J. Med. Chem. 2005, 48, 3714–3728.Google Scholar
Hermans, J.; Wang, L.Inclusion of the loss of translational and rotational freedom in theoretical estimates of free energies of binding. Application to a complex of benzene and mutant T4 lysozyme. J. Am. Chem. Soc. 1997, 119, 2707–2714.Google Scholar
Mann, G.; Hermans, J.Modeling protein-small molecule interactions: structure and thermodynamics of noble gases binding in a cavity in mutant phage T4 lysozyme L99A. J. Mol. Biol. 2000, 302, 979–989.Google Scholar
Boresch, S.; Tettinger, F.; Leitgeb, M.; Karplus, M.Absolute binding free energies: a quantitative approach for their calculation. J. Phys. Chem. A 2003, 107(35), 9535–9551.Google Scholar
Deng, Y.; Roux, B.Calculation of standard binding free energies: aromatic molecules in the T4 lysozyme l99A mutant. J. Chem. Theory Comput. 2006, 2, 1255–1273.Google Scholar
Clark, M.; Guarnieri, F.; Shkurko, I.; Wiseman, J.Grand canonical Monte Carlo simulation of ligand-protein binding. J. Chem. Info. Model. 2006, 46(1), 231–242.Google Scholar
Mobley, D. L.; Dumont, È.; Chodera, J. D.; Dill, K. A.Comparison of charge models for fixed-charge force fields: small-molecule hydration free energies in explicit solvent. J. Phys. Chem. B 2007, 111(9), 2242–2254.Google Scholar
Holt, D. A.; Luengo, J. I.; Yamashita, D. S.; Oh, H. J.; Konialian, A. L.; Yen, H. K.; Rozamus, L. W.; Brandt, M.; Bossard, M. J.; Levy, M. A.; Eggleston, D. S.; Liang, J.; Schultz, L. W.; Stout, T. J.; Clardy, J.Design, synthesis, and kinetic evaluation of high-affinity FKBP ligands and the x-ray crystal structures of their complexes with FKBP12. J. Am. Chem. Soc. 1993, 115(22), 9925–9938.Google Scholar
Shirts, M. R.Calculating precise and accurate free energies in biomolecular systems. Ph.D. dissertation, Stanford, January 2005.
Fujitani, H.; Tanida, Y.; Ito, M.; Shirts, M. R.; Jayachandran, G.; Snow, C. D.; Sorin, E. J.; Pande, V. S.Direct calculation of the binding free energies of FKBP ligands. J. Chem. Phys. 2005, 123, 84–108.Google Scholar
Jayachandran, G.; Shirts, M. R.; Park, S.; Pande, V. S.Parallelized-over-parts computation of absolute binding free energy with docking and molecular dynamics. J. Chem. Phys. 2006, 125, 084901.Google Scholar
Dixit, S. B.; Chipot, C.Can absolute free energies of association be estimated from molecular mechanical simulations? The biotin-streptavidin system revisited. J. Phys. Chem. A 2001, 105(42), 9795–9799.Google Scholar
Hess, B.; Vegt, N. F. A.Hydration thermodynamic properties of amino acid analogues: a comparison of biomolecular force fields and water models. J. Phys. Chem. B 2006, 110, 17616–17626.Google Scholar
Nicholls, A.; Mobley, D. L.; Guthrie, P. J.; Chodera, J. D.; Pande, V. S.Predicting small-molecule solvation free energies: an informal blind test for computational chemistry. J. Med. Chem. 2008, 51, 769–779.Google Scholar
Steinbrecher, T.; Hrenn, A.; Dormann, K. L.; Merfort, I.; Labahn, A.Bornyl (3,4,5-trihydroxy)-cinnamate: an optimized human neutrophil elastase inhibitor designed by free energy calculations. Bioorgan. Med. Chem. 2008, 16(5), 2385–2390.Google Scholar
Talhout, R.; Villa, A.; Mark, A. E.; Engberts, J. B.Understanding binding affinity: a combined isothermal titration calorimetry/molecular dynamics study of the binding of a series of hydrophobically modified benzamidinium chloride inhibitors to trypsin. J. Am. Chem. Soc. 2003, 125(35), 10570–10579.Google Scholar
Mao, H. Z.; Weber, J.Identification of the betatp site in the x-ray structure of f1-atpase as the high-affinity catalytic site. Proc. Natl. Acad. Sci. U.S.A. 2007, 104(47), 18478–18483.Google Scholar
Zagrovic, B.; Gunsteren, W. F.Computational analysis of the mechanism and thermodynamics of inhibition of phosphodiesterase 5a by synthetic ligands. J. Chem. Theory Comput. 2007, 3, 301–311.Google Scholar
Leitgeb, M.; Schröder, C.; Boresch, S.Alchemical free energy calculations and multiple conformational substates. J. Chem. Phys. 2005, 122, 084109.Google Scholar
Graaf, C.; Oostenbrink, C.; Keizers, P. H. J.; Vugt-Lussenburg, B. M. A.; Commandeur, J. N. M.; Vermeulen, N. P. E.Free energies of binding of R- and S- propranolol to wild-type and f483a mutant cytochrome p450 d26 from molecular dynamics simulations. Eur. Biophys. J. 2007, 36(6), 589–599.Google Scholar
Bosch, M.; Swart, M.; Snijders, J. G.; Berendsen, H. J. C.; Mark, A. E.; Oostenbrink, C.; Gunsteren, W. F.; Canters, G. W.Calculation of the redox potential of the protein azurin and some mutants. ChemBioChem 2005, 6(4), 738–746.Google Scholar
Dolenc, J.; Oostenbrink, C.; Koller, J.; Gunsteren, W. F.Molecular dynamics simulations and free energy calculations of netropsin and distamycin binding to an aaaaa DNA binding site. Nucleic Acids Res. 2005, 33(2), 725–733.Google Scholar
Donnini, S.; Juffer, A. H.Calculations of affinities of peptides for proteins. J. Comput. Chem. 2004, 25, 393–411.Google Scholar
Oostenbrink, C.; Villa, A.; Mark, A. E.; Gunsteren, W. F.A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53a5 and 53a6. J. Comput. Chem. 2004, 25(13), 1656–1676.Google Scholar
Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A. E.; Simmerling, C.Comparison of multiple amber force fields and development of improved protein backbone parameters. Proteins 2006, 65(3), 712–725.Google Scholar
Kaminski, G.; Friesner, R. A.; Rives, J.; Jorgensen, W. L.Evaluation and reparametrization of the opls-aa force field for proteins via comparison with accurate quantum chemical calculations on peptides. J. Phys. Chem. B 2001, 105(28), 6474–6487.Google Scholar
Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A.Development and testing of a general amber force field. J. Comput. Chem. 2004, 25(9), 1157–1174.Google Scholar
Jakalian, A.; Bush, B. L.; Jack, D. B.; Bayly, C. I.Fast, efficient generation of high-quality atomic charges, AM1-BCC model. I. Method. J. Comput. Chem. 2000, 21(2), 132–146.Google Scholar
Jakalian, A.; Jack, D. B.; Bayly, C. I.Fast, efficient generation of high-quality atomic charges, AM1-BCC model. II. Parameterization and validation. J. Comput. Chem. 2002, 23(16), 1623–1641.Google Scholar
Ponder, J. W.; Case, D. A.Force fields for protein simulations, inAdvances in Protein Chemistry, Dagget, V.; Ed. San Diego, CA: Academic Press 2003, 66, 27–86.
Michel, J.; Verdonk, M. L.; Essex, J. W.Protein-ligand binding free energy predictions by implicit solvent simulation: a tool for lead optimization?J. Med. Chem. 2006, 49, 7427–7439.Google Scholar
Li, L.; Dantzer, J. J.; Nowacki, J.; O'Callaghan, B. J.; Meroueh, S. O.PDBCAL: a comprehensive dataset for receptor-ligand interactions with three-dimensional structures and binding thermodynamics from isothermal titration calorimetry. Chem. Bio. Drug Des. 2008, 71(6), 529–532.Google Scholar
Rami Reddy, M.; Erion, M. D., Eds. Free Energy Calculations in Rational Drug Design. Amsterdam: Kluwer Academic, 2001.
Marti-Renom, M. A.; Madhusudhan, M. S.; Fiser, A.; Rost, B.; Sali, A.Reliability of assessment of protein structure prediction methods. Structure 2002, 10(3), 435–440.Google Scholar
Méndez, R.; Leplae, R.; Lesink, M. F.; Wodak, S. J.Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures. Proteins 2005, 60(2), 150–169.Google Scholar
Day, G.M.; Motherwell, W. D. S.; Ammon, H. L.; Boerrigter, S. X. M.; Della Valle, R. G.; Venuti, E.; Dzyabchenko, A.; Dunitz, J. D.; Schweizer, B.; Eijck, B. P.; Erk, P.; Facelli, J. C.; Bazterra, V. E.; Ferraro, M. B.; Hofmann, D. W. M.; Leusen, F. J. J.; Liang, C.; Pantelides, C. C.; Karamertzanis, P. G.; Price, S. L.; Lewis, T. C.; Nowell, H.; Torrisi, A.; Scheraga, H. A.; Arnautova, Y. A., Schmidt, M. U.; and Verwer, P.. A third blind test of crystal structure prediction. Acta Crystall. B-Struc., 2005, 61(5), 511–527.Google Scholar
Parker, C. N.McMaster University data-mining and docking competition: computational models on the catwalk. J. Biomol. Screen. 2005, 10(7), 647–648.Google Scholar
Villa, A.; Zangi, R.; Pieffet, G.; Mark, A. E.Sampling and convergence in free energy calculations of protein-ligand interactions: the binding of triphenoxypyridine derivatives to factor xa and trypsin. J. Comput. Aided Mol. Des. 2003, 17(10), 673–686.Google Scholar
Naim, M.; Bhat, S.; Rankin, K. N.; Dennis, S.; Chowdhury, S. F.; Siddiqi, I.; Drabik, P.; Sulea, T.; Bayly, C. I.; Jakalian, A.; Purisima, E. O.Solvated interaction energy (sie) for scoring protein-ligand binding affinities. 1. Exploring the parameter space. J. Chem. Info. Model. 2007, 47(1), 122–133.Google Scholar
Jorgensen, W. L.The many roles of computation in drug discovery. Science 2004, 303(5665), 1813–1818.Google Scholar
Chipot, C.; Pearlman, D. A.Free energy calculations: the long and winding gilded road. Mol. Simulat. 2002, 28(1–2), 1–12.Google Scholar
Mobley, D. L.; Dill, K. A.Binding of small-molcule ligand to proteins. “What you see” is not always “what you get.” Structure 2009, 17, 489–498.Google Scholar
Kollman, P. A.Free energy calculations: applications to chemical and biochemical phenomena. Chem. Rev. 1993, 7, 2395–2417.Google Scholar
Jorgensen, W. L.Free energy calculations: a breakthrough for modeling organic chemistry in solution. Accounts Chem. Res. 1989, 22(5), 184–189.Google Scholar

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