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Woods CJ, Hedges LO, Mulholland AJ, Malaisree M, Tosco P, Loeffler HH, Suruzhon M, Burman M, Bariami S, Bosisio S, Calabro G, Clark F, Mey ASJS, Michel J. Sire: An interoperability engine for prototyping algorithms and exchanging information between molecular simulation programs. J Chem Phys 2024; 160:202503. [PMID: 38814008 DOI: 10.1063/5.0200458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 04/29/2024] [Indexed: 05/31/2024] Open
Abstract
Sire is a Python/C++ library that is used both to prototype new algorithms and as an interoperability engine for exchanging information between molecular simulation programs. It provides a collection of file parsers and information converters that together make it easier to combine and leverage the functionality of many other programs and libraries. This empowers researchers to use sire to write a single script that can, for example, load a molecule from a PDBx/mmCIF file via Gemmi, perform SMARTS searches via RDKit, parameterize molecules using BioSimSpace, run GPU-accelerated molecular dynamics via OpenMM, and then display the resulting dynamics trajectory in a NGLView Jupyter notebook 3D molecular viewer. This functionality is built on by BioSimSpace, which uses sire's molecular information engine to interconvert with programs such as GROMACS, NAMD, Amber, and AmberTools for automated molecular parameterization and the running of molecular dynamics, metadynamics, and alchemical free energy workflows. Sire comes complete with a powerful molecular information search engine, plus trajectory loading and editing, analysis, and energy evaluation engines. This, when combined with an in-built computer algebra system, gives substantial flexibility to researchers to load, search for, edit, and combine molecular information from multiple sources and use that to drive novel algorithms by combining functionality from other programs. Sire is open source (GPL3) and is available via conda and at a free Jupyter notebook server at https://try.openbiosim.org. Sire is supported by the not-for-profit OpenBioSim community interest company.
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Affiliation(s)
- Christopher J Woods
- Advanced Computing Research Centre, University of Bristol, Bristol, United Kingdom
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom
- OpenBioSim Community Interest Company, Edinburgh, United Kingdom
| | - Lester O Hedges
- Advanced Computing Research Centre, University of Bristol, Bristol, United Kingdom
- OpenBioSim Community Interest Company, Edinburgh, United Kingdom
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom
| | - Maturos Malaisree
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom
| | | | | | | | - Matthew Burman
- OpenBioSim Community Interest Company, Edinburgh, United Kingdom
| | - Sofia Bariami
- EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh, United Kingdom
| | - Stefano Bosisio
- EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh, United Kingdom
| | - Gaetano Calabro
- EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh, United Kingdom
| | - Finlay Clark
- EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh, United Kingdom
| | - Antonia S J S Mey
- EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh, United Kingdom
| | - Julien Michel
- OpenBioSim Community Interest Company, Edinburgh, United Kingdom
- EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh, United Kingdom
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2
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SAMPL7 blind challenge: quantum-mechanical prediction of partition coefficients and acid dissociation constants for small drug-like molecules. J Comput Aided Mol Des 2021; 35:841-851. [PMID: 34164769 DOI: 10.1007/s10822-021-00402-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/17/2021] [Indexed: 02/02/2023]
Abstract
The physicochemical properties of a drug molecule determine the therapeutic effectiveness of the drug. Thus, the development of fast and accurate theoretical approaches for the prediction of such properties is inevitable. The participation to the SAMPL7 challenge is based on the estimation of logP coefficients and pKa values of small drug-like sulfonamide derivatives. Thereby, quantum mechanical calculations were carried out in order to calculate the free energy of solvation and the transfer energy of 22 drug-like compounds in different environments (water and n-octanol) by employing the SMD solvation model. For logP calculations, we studied eleven different methodologies to calculate the transfer free energies, the lowest RMSE value was obtained for the M06L/def2-TZVP//M06L/def2-SVP level of theory. On the other hand, we employed an isodesmic reaction scheme within the macro pKa framework; this was based on selecting reference molecules similar to the SAMPL7 challenge molecules. Consequently, highly well correlated pKa values were obtained with the M062X/6-311+G(2df,2p)//M052X/6-31+G(d,p) level of theory.
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3
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Nelson L, Bariami S, Ringrose C, Horton JT, Kurdekar V, Mey ASJS, Michel J, Cole DJ. Implementation of the QUBE Force Field in SOMD for High-Throughput Alchemical Free-Energy Calculations. J Chem Inf Model 2021; 61:2124-2130. [PMID: 33886305 DOI: 10.1021/acs.jcim.1c00328] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The quantum mechanical bespoke (QUBE) force-field approach has been developed to facilitate the automated derivation of potential energy function parameters for modeling protein-ligand binding. To date, the approach has been validated in the context of Monte Carlo simulations of protein-ligand complexes. We describe here the implementation of the QUBE force field in the alchemical free-energy calculation molecular dynamics simulation package SOMD. The implementation is validated by demonstrating the reproducibility of absolute hydration free energies computed with the QUBE force field across the SOMD and GROMACS software packages. We further demonstrate, by way of a case study involving two series of non-nucleoside inhibitors of HIV-1 reverse transcriptase, that the availability of QUBE in a modern simulation package that makes efficient use of graphics processing unit acceleration will facilitate high-throughput alchemical free-energy calculations.
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Affiliation(s)
- Lauren Nelson
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Sofia Bariami
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Chris Ringrose
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Joshua T Horton
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Vadiraj Kurdekar
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Antonia S J S Mey
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Daniel J Cole
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
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4
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Scheen J, Wu W, Mey ASJS, Tosco P, Mackey M, Michel J. Hybrid Alchemical Free Energy/Machine-Learning Methodology for the Computation of Hydration Free Energies. J Chem Inf Model 2020; 60:5331-5339. [PMID: 32639733 DOI: 10.1021/acs.jcim.0c00600] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration and requires a fraction of the training set size to outperform standalone FEP calculations. The ML-derived correction terms are further shown to be transferable to a range of related FEP simulation protocols. The approach may be used to inexpensively improve the accuracy of FEP calculations and to flag molecules which will benefit the most from bespoke force field parametrization efforts.
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Affiliation(s)
- Jenke Scheen
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Wilson Wu
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Antonia S J S Mey
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Paolo Tosco
- Cresset Group, New Cambridge House, Bassingbourn Road, Litlington, Cambridgeshire SG8 0SS, United Kingdom
| | - Mark Mackey
- Cresset Group, New Cambridge House, Bassingbourn Road, Litlington, Cambridgeshire SG8 0SS, United Kingdom
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
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5
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Mey ASJS, Allen BK, Macdonald HEB, Chodera JD, Hahn DF, Kuhn M, Michel J, Mobley DL, Naden LN, Prasad S, Rizzi A, Scheen J, Shirts MR, Tresadern G, Xu H. Best Practices for Alchemical Free Energy Calculations [Article v1.0]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2020; 2:18378. [PMID: 34458687 PMCID: PMC8388617 DOI: 10.33011/livecoms.2.1.18378] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing alchemical intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly. While these methods are highly flexible, care must be taken in avoiding common pitfalls to ensure that computed free energy differences can be robust and reproducible for the chosen force field, and that appropriate corrections are included to permit direct comparison with experimental data. In this paper, we review current best practices for several popular application domains of alchemical free energy calculations performed with equilibrium simulations, in particular relative and absolute small molecule binding free energy calculations to biomolecular targets.
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Affiliation(s)
- Antonia S. J. S. Mey
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | | | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY, USA
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY, USA
| | - David F. Hahn
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Maximilian Kuhn
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
- Cresset, Cambridgeshire, UK
| | - Julien Michel
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | - David L. Mobley
- Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine, Irvine, USA
| | - Levi N. Naden
- Molecular Sciences Software Institute, Blacksburg VA, USA
| | | | - Andrea Rizzi
- Silicon Therapeutics, Boston, MA, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Jenke Scheen
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | | | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
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6
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Papadourakis M, Bosisio S, Michel J. Blinded predictions of standard binding free energies: lessons learned from the SAMPL6 challenge. J Comput Aided Mol Des 2018; 32:1047-1058. [PMID: 30159717 DOI: 10.1007/s10822-018-0154-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Accepted: 08/24/2018] [Indexed: 10/28/2022]
Abstract
In the context of the SAMPL6 challenges, series of blinded predictions of standard binding free energies were made with the SOMD software for a dataset of 27 host-guest systems featuring two octa-acids hosts (OA and TEMOA) and a cucurbituril ring (CB8) host. Three different models were used, ModelA computes the free energy of binding based on a double annihilation technique; ModelB additionally takes into account long-range dispersion and standard state corrections; ModelC additionally introduces an empirical correction term derived from a regression analysis of SAMPL5 predictions previously made with SOMD. The performance of each model was evaluated with two different setups; buffer explicitly matches the ionic strength from the binding assays, whereas no-buffer merely neutralizes the host-guest net charge with counter-ions. ModelC/no-buffer shows the lowest mean-unsigned error for the overall dataset (MUE 1.29 < 1.39 < 1.50 kcal mol-1, 95% CI), while explicit modelling of the buffer improves significantly results for the CB8 host only. Correlation with experimental data ranges from excellent for the host TEMOA (R2 0.91 < 0.94 < 0.96), to poor for CB8 (R2 0.04 < 0.12 < 0.23). Further investigations indicate a pronounced dependence of the binding free energies on the modelled ionic strength, and variable reproducibility of the binding free energies between different simulation packages.
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Affiliation(s)
- Michail Papadourakis
- EaStCHEM School of Chemistry, Joseph Black Building, King's Buildings, Edinburgh, EH9 3FJ, UK
| | - Stefano Bosisio
- EaStCHEM School of Chemistry, Joseph Black Building, King's Buildings, Edinburgh, EH9 3FJ, UK
| | - Julien Michel
- EaStCHEM School of Chemistry, Joseph Black Building, King's Buildings, Edinburgh, EH9 3FJ, UK.
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7
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König G, Reetz MT, Thiel W. 1-Butanol as a Solvent for Efficient Extraction of Polar Compounds from Aqueous Medium: Theoretical and Practical Aspects. J Phys Chem B 2018; 122:6975-6988. [PMID: 29897756 DOI: 10.1021/acs.jpcb.8b02877] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The extraction of polar molecules from aqueous solution is a challenging task in organic synthesis. 1-Butanol has been used sporadically as an eluent for polar molecules, but it is unclear which molecular features drive its efficiency. Here, we employ free energy simulations to study the partitioning of 15 solutes between water and 1-butanol. The simulations demonstrate that the high affinity of polar molecules to the wet 1-butanol phase is associated with its nanostructure. Small inverse micelles of water are able to accommodate polar solutes and locally mimic an aqueous environment. We verify the simulations based on partition coefficients between water and 1-octanol, and include a blind prediction of the water/1-butanol partition coefficient of cyclohexane-1,2-diol. The calculations are in excellent agreement with experiment, reaching root-mean-square deviations below 0.7 kcal/mol. Actual extractions of cyclohexane-1,2-diol from buffer solutions that mimic cell lysates and suspensions in biocatalytic reactions further exemplify our findings. The yields highlight that extractions with 1-butanol can be significantly more efficient than the conventional protocol based on ethyl acetate.
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Affiliation(s)
- Gerhard König
- Max-Planck-Institut für Kohlenforschung , 45470 Mülheim an der Ruhr , Germany.,Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry and Chemical Biology , Rutgers University , Piscataway , New Jersey 08854 , United States
| | - Manfred T Reetz
- Max-Planck-Institut für Kohlenforschung , 45470 Mülheim an der Ruhr , Germany.,Department of Chemistry , Philipps-University Marburg , 35032 Marburg , Germany
| | - Walter Thiel
- Max-Planck-Institut für Kohlenforschung , 45470 Mülheim an der Ruhr , Germany
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8
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Rodil A, Bosisio S, Ayoup MS, Quinn L, Cordes DB, Slawin AMZ, Murphy CD, Michel J, O'Hagan D. Metabolism and hydrophilicity of the polarised 'Janus face' all- cis tetrafluorocyclohexyl ring, a candidate motif for drug discovery. Chem Sci 2018; 9:3023-3028. [PMID: 29732086 PMCID: PMC5916015 DOI: 10.1039/c8sc00299a] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 02/17/2018] [Indexed: 01/07/2023] Open
Abstract
The metabolism and polarity of the all-cis tetra-fluorocyclohexane motif is explored in the context of its potential as a motif for inclusion in drug discovery programmes. Biotransformations of phenyl all-cis tetra-, tri- and di- fluoro cyclohexanes with the human metabolism model organism Cunninghamella elegans illustrates various hydroxylated products, but limited to benzylic hydroxylation for the phenyl all-cis tetrafluorocyclohexyl ring system. Evaluation of the lipophilicities (log P) indicates a significant and progressive increase in polarity with increasing fluorination on the cyclohexane ring system. Molecular dynamics simulations indicate that water associates much more closely with the hydrogen face of these Janus face cyclohexyl rings than the fluorine face owing to enhanced hydrogen bonding interactions with the polarised hydrogens and water.
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Affiliation(s)
- Andrea Rodil
- EaStChem School of Chemistry , University of St Andrews , North Haugh, St Andrews, Fife KY16 9ST , UK .
| | - Stefano Bosisio
- EaStChem School of Chemistry , University of Edinburgh , Joseph Black Building, David Brewster Road , Edinburgh , EH9 3FJ , UK .
| | - Mohammed Salah Ayoup
- EaStChem School of Chemistry , University of St Andrews , North Haugh, St Andrews, Fife KY16 9ST , UK . .,Department of Chemistry , Faculty of Science , Alexandria University , P.B. 426 Ibrahimia , Egypt
| | - Laura Quinn
- UCD School of Biomolecular and Biomedical Sciences , University College Dublin , Belfield , Dublin , Ireland .
| | - David B Cordes
- EaStChem School of Chemistry , University of St Andrews , North Haugh, St Andrews, Fife KY16 9ST , UK .
| | - Alexandra M Z Slawin
- EaStChem School of Chemistry , University of St Andrews , North Haugh, St Andrews, Fife KY16 9ST , UK .
| | - Cormac D Murphy
- UCD School of Biomolecular and Biomedical Sciences , University College Dublin , Belfield , Dublin , Ireland .
| | - Julien Michel
- EaStChem School of Chemistry , University of Edinburgh , Joseph Black Building, David Brewster Road , Edinburgh , EH9 3FJ , UK .
| | - David O'Hagan
- EaStChem School of Chemistry , University of St Andrews , North Haugh, St Andrews, Fife KY16 9ST , UK .
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9
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Mey ASJS, Jiménez JJ, Michel J. Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations. J Comput Aided Mol Des 2018; 32:199-210. [PMID: 29134431 PMCID: PMC5767197 DOI: 10.1007/s10822-017-0083-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Accepted: 11/03/2017] [Indexed: 11/28/2022]
Abstract
The Drug Design Data Resource (D3R) consortium organises blinded challenges to address the latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy calculations. Within the context of the second D3R Grand Challenge several blinded binding free energies predictions were made for two congeneric series of Farsenoid X Receptor (FXR) inhibitors with a semi-automated alchemical free energy calculation workflow featuring FESetup and SOMD software tools. Reasonable performance was observed in retrospective analyses of literature datasets. Nevertheless, blinded predictions on the full D3R datasets were poor due to difficulties encountered with the ranking of compounds that vary in their net-charge. Performance increased for predictions that were restricted to subsets of compounds carrying the same net-charge. Disclosure of X-ray crystallography derived binding modes maintained or improved the correlation with experiment in a subsequent rounds of predictions. The best performing protocols on D3R set1 and set2 were comparable or superior to predictions made on the basis of analysis of literature structure activity relationships (SAR)s only, and comparable or slightly inferior, to the best submissions from other groups.
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Affiliation(s)
- Antonia S J S Mey
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Jordi Juárez Jiménez
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK.
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10
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Bannan CC, Burley KH, Chiu M, Shirts MR, Gilson MK, Mobley DL. Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge. J Comput Aided Mol Des 2016; 30:927-944. [PMID: 27677750 PMCID: PMC5209301 DOI: 10.1007/s10822-016-9954-8] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 08/22/2016] [Indexed: 12/14/2022]
Abstract
In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty-how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided.
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Affiliation(s)
- Caitlin C Bannan
- Department of Chemistry, University of California, 147 Bison Modular, Irvine, CA, 92697, USA
| | - Kalistyn H Burley
- Department of Pharmaceutical Sciences, University of California, 147 Bison Modular, Irvine, CA, 92697, USA
| | - Michael Chiu
- Qualcomm Institute, University of California, San Diego, CA, USA
| | - Michael R Shirts
- Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | - David L Mobley
- Department of Chemistry, University of California, 147 Bison Modular, Irvine, CA, 92697, USA.
- Department of Pharmaceutical Sciences, University of California, 147 Bison Modular, Irvine, CA, 92697, USA.
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11
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Bosisio S, Mey ASJS, Michel J. Blinded predictions of distribution coefficients in the SAMPL5 challenge. J Comput Aided Mol Des 2016; 30:1101-1114. [PMID: 27677751 PMCID: PMC5206288 DOI: 10.1007/s10822-016-9969-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Accepted: 09/08/2016] [Indexed: 12/15/2022]
Abstract
In the context of the SAMPL5 challenge water-cyclohexane distribution coefficients for 53 drug-like molecules were predicted. Four different models based on molecular dynamics free energy calculations were tested. All models initially assumed only one chemical state present in aqueous or organic phases. Model A is based on results from an alchemical annihilation scheme; model B adds a long range correction for the Lennard Jones potentials to model A; model C adds charging free energy corrections; model D applies the charging correction from model C to ionizable species only. Model A and B perform better in terms of mean-unsigned error ([Formula: see text] D units - 95 % confidence interval) and determination coefficient [Formula: see text], while charging corrections lead to poorer results with model D ([Formula: see text] and [Formula: see text]). Because overall errors were large, a retrospective analysis that allowed co-existence of ionisable and neutral species of a molecule in aqueous phase was investigated. This considerably reduced systematic errors ([Formula: see text] and [Formula: see text]). Overall accurate [Formula: see text] predictions for drug-like molecules that may adopt multiple tautomers and charge states proved difficult, indicating a need for methodological advances to enable satisfactory treatment by explicit-solvent molecular simulations.
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Affiliation(s)
- Stefano Bosisio
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Antonia S J S Mey
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK.
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12
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Bosisio S, Mey ASJS, Michel J. Blinded predictions of host-guest standard free energies of binding in the SAMPL5 challenge. J Comput Aided Mol Des 2016; 31:61-70. [PMID: 27503495 DOI: 10.1007/s10822-016-9933-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Accepted: 07/27/2016] [Indexed: 12/13/2022]
Abstract
In the context of the SAMPL5 blinded challenge standard free energies of binding were predicted for a dataset of 22 small guest molecules and three different host molecules octa-acids (OAH and OAMe) and a cucurbituril (CBC). Three sets of predictions were submitted, each based on different variations of classical molecular dynamics alchemical free energy calculation protocols based on the double annihilation method. The first model (model A) yields a free energy of binding based on computed free energy changes in solvated and host-guest complex phases; the second (model B) adds long range dispersion corrections to the previous result; the third (model C) uses an additional standard state correction term to account for the use of distance restraints during the molecular dynamics simulations. Model C performs the best in terms of mean unsigned error for all guests (MUE [Formula: see text]-95 % confidence interval) for the whole data set and in particular for the octa-acid systems (MUE [Formula: see text]). The overall correlation with experimental data for all models is encouraging ([Formula: see text]). The correlation between experimental and computational free energy of binding ranks as one of the highest with respect to other entries in the challenge. Nonetheless the large MUE for the best performing model highlights systematic errors, and submissions from other groups fared better with respect to this metric.
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Affiliation(s)
- Stefano Bosisio
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Antonia S J S Mey
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK.
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