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Hudson PS, Aviat F, Meana-Pañeda R, Warrensford L, Pollard BC, Prasad S, Jones MR, Woodcock HL, Brooks BR. Obtaining QM/MM binding free energies in the SAMPL8 drugs of abuse challenge: indirect approaches. J Comput Aided Mol Des 2022; 36:263-277. [PMID: 35597880 PMCID: PMC9148874 DOI: 10.1007/s10822-022-00443-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/17/2022] [Indexed: 11/28/2022]
Abstract
Accurately predicting free energy differences is essential in realizing the full potential of rational drug design. Unfortunately, high levels of accuracy often require computationally expensive QM/MM Hamiltonians. Fortuitously, the cost of employing QM/MM approaches in rigorous free energy simulation can be reduced through the use of the so-called “indirect” approach to QM/MM free energies, in which the need for QM/MM simulations is avoided via a QM/MM “correction” at the classical endpoints of interest. Herein, we focus on the computation of QM/MM binding free energies in the context of the SAMPL8 Drugs of Abuse host–guest challenge. Of the 5 QM/MM correction coupled with force-matching submissions, PM6-D3H4/MM ranked submission proved the best overall QM/MM entry, with an RMSE from experimental results of 2.43 kcal/mol (best in ranked submissions), a Pearson’s correlation of 0.78 (second-best in ranked submissions), and a Kendall \documentclass[12pt]{minimal}
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Affiliation(s)
- Phillip S Hudson
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA.
| | - Félix Aviat
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - Rubén Meana-Pañeda
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - Luke Warrensford
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, USA
| | - Benjamin C Pollard
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, USA
| | - Samarjeet Prasad
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - Michael R Jones
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - H Lee Woodcock
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
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Krämer A, Ghysels A, Wang E, Venable RM, Klauda JB, Brooks BR, Pastor RW. Membrane permeability of small molecules from unbiased molecular dynamics simulations. J Chem Phys 2020; 153:124107. [PMID: 33003739 PMCID: PMC7519415 DOI: 10.1063/5.0013429] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/31/2020] [Indexed: 12/26/2022] Open
Abstract
Permeation of many small molecules through lipid bilayers can be directly observed in molecular dynamics simulations on the nano- and microsecond timescale. While unbiased simulations provide an unobstructed view of the permeation process, their feasibility for computing permeability coefficients depends on various factors that differ for each permeant. The present work studies three small molecules for which unbiased simulations of permeation are feasible within less than a microsecond, one hydrophobic (oxygen), one hydrophilic (water), and one amphiphilic (ethanol). Permeabilities are computed using two approaches: counting methods and a maximum-likelihood estimation for the inhomogeneous solubility diffusion (ISD) model. Counting methods yield nearly model-free estimates of the permeability for all three permeants. While the ISD-based approach is reasonable for oxygen, it lacks precision for water due to insufficient sampling and results in misleading estimates for ethanol due to invalid model assumptions. It is also demonstrated that simulations using a Langevin thermostat with collision frequencies of 1/ps and 5/ps yield oxygen permeabilities and diffusion constants that are lower than those using Nosé-Hoover by statistically significant margins. In contrast, permeabilities from trajectories generated with Nosé-Hoover and the microcanonical ensemble do not show statistically significant differences. As molecular simulations become more affordable and accurate, calculation of permeability for an expanding range of molecules will be feasible using unbiased simulations. The present work summarizes theoretical underpinnings, identifies pitfalls, and develops best practices for such simulations.
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Affiliation(s)
- Andreas Krämer
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - An Ghysels
- IBiTech - BioMMeda, Ghent University, Corneel Heymanslaan 10, Block B - Entrance 36, 9000 Gent, Belgium
| | - Eric Wang
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland 20740, USA
| | - Richard M. Venable
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Jeffery B. Klauda
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland 20740, USA
| | - Bernard R. Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Richard W. Pastor
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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Işık M, Bergazin TD, Fox T, Rizzi A, Chodera JD, Mobley DL. Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge. J Comput Aided Mol Des 2020; 34:335-370. [PMID: 32107702 PMCID: PMC7138020 DOI: 10.1007/s10822-020-00295-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 01/24/2020] [Indexed: 12/12/2022]
Abstract
The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol-water partition coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol-water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol-water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.
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Affiliation(s)
- Mehtap Işık
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA.
| | | | - Thomas Fox
- Computational Chemistry, Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397, Biberach, Germany
| | - Andrea Rizzi
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
- Department of Chemistry, University of California, Irvine, CA, 92697, USA
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