Predicting Small-Molecule Solvation Free Energies: An Informal Blind Test for Computational Chemistry

Nicholls, Anthony; Mobley, David L.; Guthrie, J. Peter; Chodera, John D.; Bayly, Christopher I.; Cooper, Matthew; Pande, Vijay S.

doi:10.1021/jm070549+

Cited by 275 publications

(466 citation statements)

References 69 publications

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“…However, predominantly the latter approach was used—experimental measurements were determined by combining both experimental values for aqueous hydration free energies and partition coefficients measured between water and nonaqueous liquids 40. The average uncertainty in experimental values of solvation free energies reported by the authors of the Minnesota solvation database is ∼ 0.2 kcal mol −1 for the subset used in this study 40, 68, 69. However, this uncertainty is likely to be non‐normally distributed amongst the members of the database, such that individual molecules may have larger or smaller errors in their experimental Δ G estimates.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field

2016

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The effects of electronic polarization in biomolecular interactions will differ depending on the local dielectric constant of the environment, such as in solvent, DNA, proteins, and membranes. Here the performance of the AMOEBA polarizable force field is evaluated under nonaqueous conditions by calculating the solvation free energies of small molecules in four common organic solvents. Results are compared with experimental data and equivalent simulations performed with the GAFF pairwise‐additive force field. Although AMOEBA results give mean errors close to “chemical accuracy,” GAFF performs surprisingly well, with statistically significantly more accurate results than AMOEBA in some solvents. However, for both models, free energies calculated in chloroform show worst agreement to experiment and individual solutes are consistently poor performers, suggesting non‐potential‐specific errors also contribute to inaccuracy. Scope for the improvement of both potentials remains limited by the lack of high quality experimental data across multiple solvents, particularly those of high dielectric constant. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

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Section: Resultsmentioning

confidence: 99%

Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field

2016

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“…61 For our test set, we took as a starting point the experimental data compiled by Rizzo et al 19 and removed the charged molecules. In an attempt to construct a set of all neutral compounds with known hydration free energies, we added (neutral) amino acid side chain analogues and other compounds from our previous studies in explicit solvent, 22,35 and various small molecules from another set of 52 small molecules and associated references provided by J. Peter Guthrie, 41 removing redundancies. The result is a set containing 504 neutral small molecules.…”

Section: B Test Set Selectionmentioning

confidence: 99%

Treating Entropy and Conformational Changes in Implicit Solvent Simulations of Small Molecules

2008

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Implicit solvent models are increasingly popular for estimating aqueous solvation (hydration) free energies in molecular simulations and other applications. In many cases, parameters for these models are derived to reproduce experimental values for small molecule hydration free energies. Often, these hydration free energies are computed for a single solute conformation, neglecting solute conformational changes upon solvation. Here, we incorporate these effects using alchemical free energy methods. We find significant errors when hydration free energies are estimated using only a single solute conformation, even for relatively small, simple, rigid solutes. For example, we find conformational entropy (TΔS) changes of up to 2.3 kcal/mol upon hydration. Interestingly, these changes in conformational entropy correlate poorly (R 2 = 0.03) with the number of rotatable bonds. The present study illustrates that implicit solvent modeling can be improved by eliminating the approximation that solutes are rigid.

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“…The root mean square error (RMSE) of the computation results is 1.77 kcal/mol and the average error is 1.42 kcal/mol. In comparison, Nicholls et al [39] reported a RMSE of 1.87 kcal/mol for the same set of molecules by the linear Poisson model and further, the RMSE was reduced to 1.71 ± 0.05 kcal/mol [39] by their explicit solvent approach, which is much more expensive. Additionally, the RMSE in the current work is only slightly greater than the one (RSME=1.76 kcal/mol) reported in [18], where the biomolecular surface is defined and generated from a nonlinear mean curvature flow equation so extra computational efforts are necessary.…”

Section: Applicationsmentioning

confidence: 99%

High-order fractional partial differential equation transform for molecular surface construction

Chen

Wei

2012

Computational and Mathematical Biophysics

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Fractional derivative or fractional calculus plays a significant role in theoretical modeling of scientific and engineering problems. However, only relatively low order fractional derivatives are used at present. In general, it is not obvious what role a high fractional derivative can play and how to make use of arbitrarily high-order fractional derivatives. This work introduces arbitrarily high-order fractional partial differential equations (PDEs) to describe fractional hyperdiffusions. The fractional PDEs are constructed via fractional variational principle. A fast fractional Fourier transform (FFFT) is proposed to numerically integrate the high-order fractional PDEs so as to avoid stringent stability constraints in solving high-order evolution PDEs. The proposed high-order fractional PDEs are applied to the surface generation of proteins. We first validate the proposed method with a variety of test examples in two and three-dimensional settings. The impact of high-order fractional derivatives to surface analysis is examined. We also construct fractional PDE transform based on arbitrarily high-order fractional PDEs. We demonstrate that the use of arbitrarily high-order derivatives gives rise to time-frequency localization, the control of the spectral distribution, and the regulation of the spatial resolution in the fractional PDE transform. Consequently, the fractional PDE transform enables the mode decomposition of images, signals, and surfaces. The effect of the propagation time on the quality of resulting molecular surfaces is also studied. Computational efficiency of the present surface generation method is compared with the MSMS approach in Cartesian representation. We further validate the present method by examining some benchmark indicators of macromolecular surfaces, i.e., surface area, surface enclosed volume, surface electrostatic potential and solvation free energy. Extensive numerical experiments and comparison with an established surface model indicate that the proposed high-order fractional PDEs are robust, stable and efficient for biomolecular surface generation.

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Predicting Small-Molecule Solvation Free Energies: An Informal Blind Test for Computational Chemistry

Cited by 275 publications

References 69 publications

Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field

Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field

Treating Entropy and Conformational Changes in Implicit Solvent Simulations of Small Molecules

High-order fractional partial differential equation transform for molecular surface construction

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