Calculating binding free energies of host–guest systems using the AMOEBA polarizable force field

Bell, David R.; Qi, Rui; Jing, Zhifeng; Xiang, Jin Yu; Mejias, Christopher; Schnieders, Michael J.; Ponder, Jay W.; Ren, Pengyu

doi:10.1039/c6cp02509a

Cited by 48 publications

(62 citation statements)

References 62 publications

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“…That the CB8 guests in SAMPL6 were particularly challenging is corroborated by the comparison between the performance of DDM-AMOEBA and the results obtained by BAR-560, which also uses the double decoupling method and the AMOEBA polarizable force field, on the CB7 guests in SAMPL4 [14]. In this case as well, only offset statistics are available for comparison as SAMPL4 accepted exclusively relative free energy predictions.…”

Section: Methodsmentioning

confidence: 91%

Overview of the SAMPL6 host–guest binding affinity prediction challenge

Rizzi

Murkli

McNeill

et al. 2018

J Comput Aided Mol Des

141

200

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Accurately predicting the binding affinities of small organic molecules to biological macro-molecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macro-molecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from 10 participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host-guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states) may be required to further enhance predictive accuracy.

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Section: Methodsmentioning

confidence: 91%

Overview of the SAMPL6 host–guest binding affinity prediction challenge

Rizzi

Murkli

McNeill

et al. 2018

J Comput Aided Mol Des

141

200

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“…Parameters and starting poses for 12 molecules of the sampl4 dataset were generated as described previously . Structures of the sampl4 ligands utilized in this study are shown in Figure .…”

Section: Methodsmentioning

confidence: 99%

“…Previous work using AMOEBA force field has shown an accurate recapitulation of experimental free energies in small molecules hydration, metal ion hydration, as well as ligand binding in synthetic hosts, and protein systems . The inclusion of a complex electrostatic force leads to increasing computational cost, so that potential it can benefit even more from parallel computing of protein‐scale systems consisting of tens of thousands of atoms.…”

Section: Introductionmentioning

confidence: 99%

Tinker‐OpenMM: Absolute and relative alchemical free energies using AMOEBA on GPUs

et al. 2017

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The capabilities of the polarizable force fields for alchemical free energy calculations have been limited by the high computational cost and complexity of the underlying potential energy functions. In this work, we present a GPU based general alchemical free energy simulation platform for polarizable potential AMOEBA. Tinker-OpenMM, the OpenMM implementation of the AMOEBA simulation engine has been modified to enable both absolute and relative alchemical simulations on GPUs, which leads to a ~200-fold improvement in simulation speed over a single CPU core. We show that free energy values calculated using this platform agree with the results of Tinker simulations for the hydration of organic compounds and binding of host-guest systems within the statistical errors. In addition to absolute binding, we designed a relative alchemical approach for computing relative binding affinities of ligands to the same host, where a special path was applied to avoid numerical instability due to polarization between the different ligands that bind to the same site. This scheme is general and does not require ligands to have similar scaffolds. We show that relative hydration and binding free energy calculated using this approach match those computed from the absolute free energy approach.

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“…575 The explanation for this inequality is likely to be found in the complexity of the guest sets rather than a 576 methodological regression as SAMPL3 featured only two relatively simple fragment-like binders while the 577 latest round of the challenge included compounds of moderate size and/or complex stereochemistry (e.g., 578 gallamine triethiodate, quinine). 579 That the CB8 guests in SAMPL6 were particularly challenging is corroborated by the comparison between 580 the performance of DDM-AMOEBA and the results obtained by BAR-560, which also uses the double 581 decoupling method and the AMOEBA polarizable force field, on the CB7 guests in SAMPL4 [14]. In this case 582 as well, only offset statistics are available for comparison as SAMPL4 accepted exclusively relative free energy 583 predictions.…”

mentioning

confidence: 91%

Overview of the SAMPL6 host-guest binding affinity prediction challenge

Rizzi

Murkli

McNeill

et al. 2018

Preprint

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The ability to accurately predict the binding affinities of small organic molecules to biological 15 macromolecules would greatly accelerate drug discovery by reducing the number of compounds that must 16 be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the 17 accuracy of current quantitative physical and empirical modeling approaches to affinity prediction against 18 binding data to biological macromolecules is frustrated by several challenges, such as slow conformational 19 dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several 20 SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to 21 circumvent these challenges in assessing the predictive performance of current-generation quantitative 22 modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present 23 an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolec-24 ular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), 25 along with 21 small organic guest molecules. A total of 119 entries were received from 10 participating 26 groups employing a variety of methods that spanned electronic structure and movable type calculations 27 in implicit solvent to alchemical and potential of mean force strategies using empirical force fields and 28 explicit solvent models. While empirical models tended to obtain better performance, it was not possible 29 to identify a single approach consistently providing superior predictions across all host-guest systems and 30 statistical metrics, and the accuracy of the methodologies generally displayed a substantial dependence on 31 the systems considered, arguing for the importance of considering a diverse set of hosts in blind evaluations. 32 Several entries exploited previous experimental measurements of similar host-guest systems in an effort 33 to improve their physical-based predictions via some manner of rudimentary machine learning; while this 34 strategy succeeded in reducing systematic errors, it was not able to generated a corresponding improvement 35 of correlation statistics. Comparison to previous rounds of the host-guest binding free energy challenge 36 highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA 37 systems, but a surprising lack of improvement in root mean square error over the past several challenge 38 rounds. The data suggests that further refinement of force field parameters and improved treatment of 39 chemical effects (e.g., buffer salt conditions, protonation states) may be required to continue to enhance 40 predictive accuracy. 41 42 1 of 35 Preprint ahead of submission -July 18, 2018 48 Assessment of how much of this inaccuracy can be attributed to fundamental limitations of the force 49 field in accurately modeling energetics ...

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Calculating binding free energies of host–guest systems using the AMOEBA polarizable force field

Cited by 48 publications

References 62 publications

Overview of the SAMPL6 host–guest binding affinity prediction challenge

Overview of the SAMPL6 host–guest binding affinity prediction challenge

Tinker‐OpenMM: Absolute and relative alchemical free energies using AMOEBA on GPUs

Overview of the SAMPL6 host-guest binding affinity prediction challenge

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