Calculating the Sensitivity and Robustness of Binding Free Energy Calculations to Force Field Parameters

Rocklin, Gabriel J.; Mobley, David L.; Dill, Ken A.

doi:10.1021/ct400315q

Cited by 30 publications

(33 citation statements)

References 54 publications

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“…This is in effect what was reported in Ref. 9 . One can see that if the second set of terms on the right hand side of both single point formulae were to be zero, this naïve sensitivity would in fact be the correct sensitivity.…”

Section: Self-consistent Iterative (Sci) Single Point Formulasupporting

confidence: 91%

Predicting the Sensitivity of Multiscale Coarse-Grained Models to their Underlying Fine-Grained Model Parameters

Wagner

Dama

Voth

2015

J. Chem. Theory Comput.

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The sensitivity of a coarse-grained (CG) force field to changes in the underlying fine-grained (FG) model from which it was derived provides modeling insight for improving transferability across interaction parameters, transferability across temperature, and the calculation of thermodynamic derivatives. Methods in the literature, such as multi-trajectory finite differences and reweighted finite differences, are either too computationally demanding to calculate within acceptable noise tolerances or are too biased for practical accuracy. This work presents a new reweighting-free, single-simulation formula that allows for practical, high signal-to-noise calculations of CG model sensitivity with respect to FG model interaction parameters and thermodynamic state points. This formula, the self-consistent basis (SCB) single point formula, determines the many-body sensitivity in a single step by approximating the derivative of the many-body potential projected onto the same set of trial functions as the sensitivity. A related diagnostic formula also derived in this paper is the self-consistent iterative (SCI) single point formula, which is useful for identifying the importance of many-body sources of error and verifying CG representability of observables. The SCI formula determines the many-body sensitivity iteratively via a series of partially self-consistent, variational approximations to the complete many-body sensitivity. The new, computationally efficient SCB formula shows substantially less noise than previous methods when applied to single site methanol and solvent-free sodium chloride CG models, though bias can remain a problem. It represents a novel method for calculating alchemical transferability across interaction parameters at low computational cost and with high fidelity, and the results point to new understanding of the current limits of CG model transferability.

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Section: Self-consistent Iterative (Sci) Single Point Formulasupporting

confidence: 91%

Predicting the Sensitivity of Multiscale Coarse-Grained Models to their Underlying Fine-Grained Model Parameters

Wagner

Dama

Voth

2015

J. Chem. Theory Comput.

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“…1–6 The computational prediction of binding free energies is however complex and challeng-ing, 7,8 and its outcomes can depend strongly on the molecular modeling technique used. 9 In particular, accuracy of solvation and binding free energies calculations depends critically on the quality of the underlying solvent model. 10–12 …”

Section: Introductionmentioning

confidence: 99%

Protein–Ligand Electrostatic Binding Free Energies from Explicit and Implicit Solvation

Izadi

Aguilar

Onufriev

2015

J. Chem. Theory Comput.

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Accurate yet efficient computational models of solvent environment are central for most calculations that rely on atomistic modeling, such as prediction of protein-ligand binding affinities. In this study, we evaluate the accuracy of a recently developed generalized Born implicit solvent model, GBNSR6 (Aguilar et al. J. Chem. Theory Comput. 2010, 6, 3613–3639), in estimating the electrostatic solvation free energies (ΔGpol) and binding free energies (ΔΔGpol) for small protein-ligand complexes. We also compare estimates based on three different explicit solvent models (TIP3P, TIP4PEw and OPC). The two main findings are as follows. First, the deviation (RMSD=7.04 kcal/mol) of GBNSR6 binding affinities from commonly used TIP3P reference values is comparable to the deviations between explicit models themselves, e.g. TIP4PEw vs. TIP3P (RMSD=5.30 kcal/mol). A simple uniform adjustment of the atomic radii by a single scaling factor reduces the RMS deviation of GBNSR6 from TIP3P to within the above “error margin” – differences between ΔΔGpol estimated by different common explicit solvent models. The simple radii scaling virtually eliminates the systematic deviation (ΔΔGpol) between GBNSR6 and two out of the three explicit water models, and significantly reduces the deviation from the third explicit model. Second, the differences between electrostatic binding energy estimates from different explicit models is disturbingly large; for example, the deviation between TIP4PEw and TIP3P estimates of ΔΔGpol values can be up to ~50% in relative error, or ~9 kcal/mol in absolute error, which is significantly larger than “chemical accuracy” goal of ~1 kcal/mol. The absolute ΔGpol calculated with different explicit models could differ by tens of kcal/mol. These discrepancies point to unacceptably high sensitivity of binding affinity estimates to the choice of common explicit water models. The absence of a clear “gold standard” among these models strengthens the case for the use of accurate implicit solvation models for binding energetics, which may be orders of magnitude faster.

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“…Another example is free energy perturbation where free energy differences are calculated by investigating the effects of alchemical mutations in a molecule [4,5]. Usually only a small number of molecular interactions are modified at a time, while the errors in the rest of the system largely cancel [6]. This can lead to reduced error in predictions as well.…”

Section: Introductionmentioning

confidence: 99%

Fragment-based error estimation in biomolecular modeling

Faver¹,

Merz²

2014

Drug Discovery Today

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Computer simulations are becoming an increasingly more important component of drug discovery. Computational models are now often able to reproduce and sometimes even predict outcomes of experiments. Still, potential energy models such as force fields contain significant amounts of bias and imprecision. We have shown how even small uncertainties in potential energy models can propagate to yield large errors, and have devised some general error-handling protocols for biomolecular modeling with imprecise energy functions. Herein we discuss those protocols within the contexts of protein–ligand binding and protein folding.

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Calculating the Sensitivity and Robustness of Binding Free Energy Calculations to Force Field Parameters

Cited by 30 publications

References 54 publications

Predicting the Sensitivity of Multiscale Coarse-Grained Models to their Underlying Fine-Grained Model Parameters

Predicting the Sensitivity of Multiscale Coarse-Grained Models to their Underlying Fine-Grained Model Parameters

Protein–Ligand Electrostatic Binding Free Energies from Explicit and Implicit Solvation

Fragment-based error estimation in biomolecular modeling

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