1
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Summa CM, Langford DP, Dinshaw SH, Webb J, Rick SW. Calculations of Absolute Free Energies, Enthalpies, and Entropies for Drug Binding. J Chem Theory Comput 2024; 20:2812-2819. [PMID: 38538531 DOI: 10.1021/acs.jctc.4c00057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Computer simulation methods can aid in the rational design of drugs aimed at a specific target, typically a protein. The affinity of a drug for its target is given by the free energy of binding. Binding can be further characterized by the enthalpy and entropy changes in the process. Methods exist to determine exact free energies, enthalpies, and entropies that are dependent only on the quality of the potential model and adequate sampling of conformational degrees of freedom. Entropy and enthalpy are roughly an order of magnitude more difficult to calculate than the free energy. This project combines a replica exchange method for enhanced sampling, designed to be efficient for protein-sized systems, with free energy calculations. This approach, replica exchange with dynamical scaling (REDS), uses two conventional simulations at different temperatures so that the entropy can be found from the temperature dependence of the free energy. A third replica is placed between them, with a modified Hamiltonian that allows it to span the temperature range of the conventional replicas. REDS provides temperature-dependent data and aids in sampling. It is applied to the bromodomain-containing protein 4 (BRD4) system. We find that for the force fields used, the free energies are accurate but the entropies and enthalpies are not, with the entropic contribution being too positive. Reproducing the entropy and enthalpy of binding appears to be a more stringent test of the force fields than reproducing the free energy.
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Affiliation(s)
- Christopher M Summa
- Department of Computer Science, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Dillon P Langford
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Sam H Dinshaw
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Jennifer Webb
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Steven W Rick
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
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2
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Amezcua M, Setiadi J, Mobley DL. The SAMPL9 host-guest blind challenge: an overview of binding free energy predictive accuracy. Phys Chem Chem Phys 2024; 26:9207-9225. [PMID: 38444308 PMCID: PMC10954238 DOI: 10.1039/d3cp05111k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/03/2024] [Indexed: 03/07/2024]
Abstract
We report the results of the SAMPL9 host-guest blind challenge for predicting binding free energies. The challenge focused on macrocycles from pillar[n]-arene and cyclodextrin host families, including WP6, and bCD and HbCD. A variety of methods were used by participants to submit binding free energy predictions. A machine learning approach based on molecular descriptors achieved the highest accuracy (RMSE of 2.04 kcal mol-1) among the ranked methods in the WP6 dataset. Interestingly, predictions for WP6 obtained via docking tended to outperform all methods (RMSE of 1.70 kcal mol-1), most of which are MD based and computationally more expensive. In general, methods applying force fields achieved better correlation with experiments for WP6 opposed to the machine learning and docking models. In the cyclodextrin-phenothiazine challenge, the ATM approach emerged as the top performing method with RMSE less than 1.86 kcal mol-1. Correlation metrics of ranked methods in this dataset were relatively poor compared to WP6. We also highlight several lessons learned to guide future work and help improve studies on the systems discussed. For example, WP6 may be present in other microstates other than its -12 state in the presence of certain guests. Machine learning approaches can be used to fine tune or help train force fields for certain chemistry (i.e. WP6-G4). Certain phenothiazines occupy distinct primary and secondary orientations, some of which were considered individually for accurate binding free energies. The accuracy of predictions from certain methods while starting from a single binding pose/orientation demonstrates the sensitivity of calculated binding free energies to the orientation, and in some cases the likely dominant orientation for the system. Computational and experimental results suggest that guest phenothiazine core traverses both the secondary and primary faces of the cyclodextrin hosts, a bulky cationic side chain will primarily occupy the primary face, and the phenothiazine core substituent resides at the larger secondary face.
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Affiliation(s)
- Martin Amezcua
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, USA.
| | - Jeffry Setiadi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, USA.
- Department of Chemistry, University of California, Irvine, Irvine, California 92697, USA
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3
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Setiadi J, Boothroyd S, Slochower DR, Dotson DL, Thompson MW, Wagner JR, Wang LP, Gilson MK. Tuning Potential Functions to Host-Guest Binding Data. J Chem Theory Comput 2024; 20:239-252. [PMID: 38147689 PMCID: PMC10838530 DOI: 10.1021/acs.jctc.3c01050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
Software to more rapidly and accurately predict protein-ligand binding affinities is of high interest for early-stage drug discovery, and physics-based methods are among the most widely used technologies for this purpose. The accuracy of these methods depends critically on the accuracy of the potential functions that they use. Potential functions are typically trained against a combination of quantum chemical and experimental data. However, although binding affinities are among the most important quantities to predict, experimental binding affinities have not to date been integrated into the experimental data set used to train potential functions. In recent years, the use of host-guest complexes as simple and tractable models of binding thermodynamics has gained popularity due to their small size and simplicity, relative to protein-ligand systems. Host-guest complexes can also avoid ambiguities that arise in protein-ligand systems such as uncertain protonation states. Thus, experimental host-guest binding data are an appealing additional data type to integrate into the experimental data set used to optimize potential functions. Here, we report the extension of the Open Force Field Evaluator framework to enable the systematic calculation of host-guest binding free energies and their gradients with respect to force field parameters, coupled with the curation of 126 host-guest complexes with available experimental binding free energies. As an initial application of this novel infrastructure, we optimized generalized Born (GB) cavity radii for the OBC2 GB implicit solvent model against experimental data for 36 host-guest systems. This refitting led to a dramatic improvement in accuracy for both the training set and a separate test set with 90 additional host-guest systems. The optimized radii also showed encouraging transferability from host-guest systems to 59 protein-ligand systems. However, the new radii are significantly smaller than the baseline radii and lead to excessively favorable hydration free energies (HFEs). Thus, users of the OBC2 GB model currently may choose between GB cavity radii that yield more accurate binding affinities and GB cavity radii that yield more accurate HFEs. We suspect that achieving good accuracy on both will require more far-reaching adjustments to the GB model. We note that binding free-energy calculations using the OBC2 model in OpenMM gain about a 10× speedup relative to corresponding explicit solvent calculations, suggesting a future role for implicit solvent absolute binding free-energy (ABFE) calculations in virtual compound screening. This study proves the principle of using host-guest systems to train potential functions that are transferrable to protein-ligand systems and provides an infrastructure that enables a range of applications.
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Affiliation(s)
- Jeffry Setiadi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9255 Pharmacy Lane, La Jolla, California 92093, United States
| | - Simon Boothroyd
- Boothroyd Scientific Consulting Ltd., London WC2H 9JQ, U.K
- Psivant Therapeutics, Boston, Massachusetts 02210, United States
| | | | - David L Dotson
- Datryllic LLC, Phoenix, Arizona 85003, United States
- The Open Force Field Consortium, Open Molecular Software Foundation, Davis, California 95616, United States
| | - Matthew W Thompson
- The Open Force Field Consortium, Open Molecular Software Foundation, Davis, California 95616, United States
| | - Jeffrey R Wagner
- The Open Force Field Consortium, Open Molecular Software Foundation, Davis, California 95616, United States
| | - Lee-Ping Wang
- Chemistry Department, University of California Davis, Davis, California 95616, United States
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9255 Pharmacy Lane, La Jolla, California 92093, United States
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4
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Hurley MFD, Raddi RM, Pattis JG, Voelz VA. Expanded ensemble predictions of absolute binding free energies in the SAMPL9 host-guest challenge. Phys Chem Chem Phys 2023; 25:32393-32406. [PMID: 38009066 PMCID: PMC10760931 DOI: 10.1039/d3cp02197a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2023]
Abstract
As part of the SAMPL9 community-wide blind host-guest challenge, we implemented an expanded ensemble workflow to predict absolute binding free energies for 13 small molecules against pillar[6]arene. Notable features of our protocol include consideration of a variety of protonation and enantiomeric states for both host and guests, optimization of alchemical intermediates, and analysis of free energy estimates and their uncertainty using large numbers of simulation replicates performed using distributed computing. Our predictions of absolute binding free energies resulted in a mean absolute error of 2.29 kcal mol-1 and an R2 of 0.54. Overall, results show that expanded ensemble calculations using all-atom molecular dynamics simulations are a valuable and efficient computational tool in predicting absolute binding free energies.
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Affiliation(s)
| | - Robert M Raddi
- Department of Chemistry, Temple University, Philadelphia, PA, USA.
| | - Jason G Pattis
- Department of Chemistry, Temple University, Philadelphia, PA, USA.
| | - Vincent A Voelz
- Department of Chemistry, Temple University, Philadelphia, PA, USA.
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5
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Conflitti P, Raniolo S, Limongelli V. Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness. J Chem Theory Comput 2023; 19:6047-6061. [PMID: 37656199 PMCID: PMC10536999 DOI: 10.1021/acs.jctc.3c00641] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Indexed: 09/02/2023]
Abstract
Computational techniques applied to drug discovery have gained considerable popularity for their ability to filter potentially active drugs from inactive ones, reducing the time scale and costs of preclinical investigations. The main focus of these studies has historically been the search for compounds endowed with high affinity for a specific molecular target to ensure the formation of stable and long-lasting complexes. Recent evidence has also correlated the in vivo drug efficacy with its binding kinetics, thus opening new fascinating scenarios for ligand/protein binding kinetic simulations in drug discovery. The present article examines the state of the art in the field, providing a brief summary of the most popular and advanced ligand/protein binding kinetics techniques and evaluating their current limitations and the potential solutions to reach more accurate kinetic models. Particular emphasis is put on the need for a paradigm change in the present methodologies toward ligand and protein parametrization, the force field problem, characterization of the transition states, the sampling issue, and algorithms' performance, user-friendliness, and data openness.
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Affiliation(s)
- Paolo Conflitti
- Faculty
of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana (USI), 6900 Lugano, Switzerland
| | - Stefano Raniolo
- Faculty
of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana (USI), 6900 Lugano, Switzerland
| | - Vittorio Limongelli
- Faculty
of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana (USI), 6900 Lugano, Switzerland
- Department
of Pharmacy, University of Naples “Federico
II”, 80131 Naples, Italy
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6
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Amezcua M, Setiadi J, Ge Y, Mobley DL. An overview of the SAMPL8 host-guest binding challenge. J Comput Aided Mol Des 2022; 36:707-734. [PMID: 36229622 PMCID: PMC9596595 DOI: 10.1007/s10822-022-00462-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/21/2022] [Indexed: 11/23/2022]
Abstract
The SAMPL series of challenges aim to focus the community on specific modeling challenges, while testing and hopefully driving progress of computational methods to help guide pharmaceutical drug discovery. In this study, we report on the results of the SAMPL8 host–guest blind challenge for predicting absolute binding affinities. SAMPL8 focused on two host–guest datasets, one involving the cucurbituril CB8 (with a series of common drugs of abuse) and another involving two different Gibb deep-cavity cavitands. The latter dataset involved a previously featured deep cavity cavitand (TEMOA) as well as a new variant (TEETOA), both binding to a series of relatively rigid fragment-like guests. Challenge participants employed a reasonably wide variety of methods, though many of these were based on molecular simulations, and predictive accuracy was mixed. As in some previous SAMPL iterations (SAMPL6 and SAMPL7), we found that one approach to achieve greater accuracy was to apply empirical corrections to the binding free energy predictions, taking advantage of prior data on binding to these hosts. Another approach which performed well was a hybrid MD-based approach with reweighting to a force matched QM potential. In the cavitand challenge, an alchemical method using the AMOEBA-polarizable force field achieved the best success with RMSE less than 1 kcal/mol, while another alchemical approach (ATM/GAFF2-AM1BCC/TIP3P/HREM) had RMSE less than 1.75 kcal/mol. The work discussed here also highlights several important lessons; for example, retrospective studies of reference calculations demonstrate the sensitivity of predicted binding free energies to ethyl group sampling and/or guest starting pose, providing guidance to help improve future studies on these systems.
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Affiliation(s)
- Martin Amezcua
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
| | - Jeffry Setiadi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, 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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7
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Oliveira MP, Gonçalves YMH, Ol Gheta SK, Rieder SR, Horta BAC, Hünenberger PH. Comparison of the United- and All-Atom Representations of (Halo)alkanes Based on Two Condensed-Phase Force Fields Optimized against the Same Experimental Data Set. J Chem Theory Comput 2022; 18:6757-6778. [PMID: 36190354 DOI: 10.1021/acs.jctc.2c00524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The level of accuracy that can be achieved by a force field is influenced by choices made in the interaction-function representation and in the relevant simulation parameters. These choices, referred to here as functional-form variants (FFVs), include for example the model resolution, the charge-derivation procedure, the van der Waals combination rules, the cutoff distance, and the treatment of the long-range interactions. Ideally, assessing the effect of a given FFV on the intrinsic accuracy of the force-field representation requires that only the specific FFV is changed and that this change is performed at an optimal level of parametrization, a requirement that may prove extremely challenging to achieve in practice. Here, we present a first attempt at such a comparison for one specific FFV, namely the choice of a united-atom (UA) versus an all-atom (AA) resolution in a force field for saturated acyclic (halo)alkanes. Two force-field versions (UA vs AA) are optimized in an automated way using the CombiFF approach against 961 experimental values for the pure-liquid densities ρliq and vaporization enthalpies ΔHvap of 591 compounds. For the AA force field, the torsional and third-neighbor Lennard-Jones parameters are also refined based on quantum-mechanical rotational-energy profiles. The comparison between the UA and AA resolutions is also extended to properties that have not been included as parameterization targets, namely the surface-tension coefficient γ, the isothermal compressibility κT, the isobaric thermal-expansion coefficient αP, the isobaric heat capacity cP, the static relative dielectric permittivity ϵ, the self-diffusion coefficient D, the shear viscosity η, the hydration free energy ΔGwat, and the free energy of solvation ΔGche in cyclohexane. For the target properties ρliq and ΔHvap, the UA and AA resolutions reach very similar levels of accuracy after optimization. For the nine other properties, the AA representation leads to more accurate results in terms of η; comparably accurate results in terms of γ, κT, αP, ϵ, D, and ΔGche; and less accurate results in terms of cP and ΔGwat. This work also represents a first step toward the calibration of a GROMOS-compatible force field at the AA resolution.
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Affiliation(s)
- Marina P Oliveira
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Yan M H Gonçalves
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - S Kashef Ol Gheta
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Salomé R Rieder
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Bruno A C Horta
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Philippe H Hünenberger
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
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8
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Sun Z, Zheng L, Wang K, Huai Z, Liu Z. Primary vs secondary: Directionalized guest coordination in β-cyclodextrin derivatives. Carbohydr Polym 2022; 297:120050. [DOI: 10.1016/j.carbpol.2022.120050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/17/2022] [Accepted: 08/25/2022] [Indexed: 02/01/2023]
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9
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Patel LA, Chau P, Debesai S, Darwin L, Neale C. Drug Discovery by Automated Adaptation of Chemical Structure and Identity. J Chem Theory Comput 2022; 18:5006-5024. [PMID: 35834740 DOI: 10.1021/acs.jctc.1c01271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.
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10
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Piñeiro Á, Pipkin J, Antle V, Garcia-Fandino R. Aggregation versus inclusion complexes to solubilize drugs with cyclodextrins. A case study using sulphobutylether-β-cyclodextrins and remdesivir. J Mol Liq 2021; 343:117588. [PMID: 34548723 PMCID: PMC8447550 DOI: 10.1016/j.molliq.2021.117588] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/11/2021] [Accepted: 09/14/2021] [Indexed: 11/18/2022]
Abstract
The formation of small hybrid aggregates between excipient and drug molecules is one of the mechanisms that contributes to the solubilization of active principles in pharmaceutical formulations. The characterization of the formation, governing interactions and structure of such entities is not trivial since they are highly flexible and dynamic, quickly exchanging molecules from one to another. In the case of cyclodextrins, this mechanism and the formation of inclusion complexes synergistically cooperate to favour the bioavailability of drugs. In a previous study we reported a detailed characterization of the possible formation of inclusion complexes with 1:1 stoichiometry between remdesivir, the only antiviral medication currently approved by the United States Food and Drug Administration for treating COVID-19, and sulphobutylether-β-cyclodextrins. Here we extend our study to assess the role of the spontaneous aggregation in the solubilization of the same drug, by molecular dynamics simulations at different relative concentrations of both compounds. The number of sulphobutylether substitutions in the cyclodextrin structure and two different protonation states of the remdesivir molecule are considered. We aim to shed light in the solubilization mechanism of sulphobutylether-β-cyclodextrins, broadly used as an excipient in many pharmaceutical formulations, in particular in the case of remdesivir as an active compound.
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Affiliation(s)
- Ángel Piñeiro
- Departamento de Física de Aplicada, Facultade de Física, Universidade de Santiago de Compostela, E-15782 Santiago de Compostela, Spain
| | - James Pipkin
- Ligand Pharmaceuticals Incorporated, 3911 Sorrento Valley Boulevard, San Diego, CA, USA
| | - Vince Antle
- Ligand Pharmaceuticals Incorporated, 3911 Sorrento Valley Boulevard, San Diego, CA, USA
| | - Rebeca Garcia-Fandino
- Departamento de Química Orgánica, Center for Research in Biological Chemistry and Molecular Materials, Universidade de Santiago de Compostela, Campus Vida s/n, E-15782 Santiago de Compostela, Spain
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11
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González D, Macaya L, Vöhringer-Martinez E. Molecular Environment-Specific Atomic Charges Improve Binding Affinity Predictions of SAMPL5 Host-Guest Systems. J Chem Inf Model 2021; 61:4462-4474. [PMID: 34464129 DOI: 10.1021/acs.jcim.1c00655] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Host-guest systems are widely used in benchmarks as model systems to improve computational methods for absolute binding free energy predictions. Recent advances in sampling algorithms for alchemical free energy calculations and the increase in computational power have made their binding affinity prediction primarily dependent on the quality of the force field. Here, we propose a new methodology to derive the atomic charges of host-guest systems based on quantum mechanics/molecular mechanics calculations and minimal basis iterative stockholder (MBIS) partitioning of the polarized electron density. A newly developed interface between the OpenMM and ORCA software packages provides D-MBIS charges that represent the guest's average electrostatic interactions in the hosts or the solvent. The simulation workflow also calculates the average energy required to polarize the guest in the bound and unbound state. Alchemical free energy calculations using the general Amber force field parameters with D-MBIS charges improve the binding affinity prediction of six guests bound to two octa acid hosts compared to the AM1-BCC charge set after correction with the average energetic polarization cost. This correction originates from the difference in potential energy that is required to polarize the guest in the bound and unbound state and contributes significantly to the binding affinity of anionic guests.
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Affiliation(s)
- Duván González
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Luis Macaya
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Esteban Vöhringer-Martinez
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
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12
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Amezcua M, El Khoury L, Mobley DL. SAMPL7 Host-Guest Challenge Overview: assessing the reliability of polarizable and non-polarizable methods for binding free energy calculations. J Comput Aided Mol Des 2021; 35:1-35. [PMID: 33392951 PMCID: PMC8121194 DOI: 10.1007/s10822-020-00363-5] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 11/24/2020] [Indexed: 12/15/2022]
Abstract
The SAMPL challenges focus on testing and driving progress of computational methods to help guide pharmaceutical drug discovery. However, assessment of methods for predicting binding affinities is often hampered by computational challenges such as conformational sampling, protonation state uncertainties, variation in test sets selected, and even lack of high quality experimental data. SAMPL blind challenges have thus frequently included a component focusing on host-guest binding, which removes some of these challenges while still focusing on molecular recognition. Here, we report on the results of the SAMPL7 blind prediction challenge for host-guest affinity prediction. In this study, we focused on three different host-guest categories-a familiar deep cavity cavitand series which has been featured in several prior challenges (where we examine binding of a series of guests to two hosts), a new series of cyclodextrin derivatives which are monofunctionalized around the rim to add amino acid-like functionality (where we examine binding of two guests to a series of hosts), and binding of a series of guests to a new acyclic TrimerTrip host which is related to previous cucurbituril hosts. Many predictions used methods based on molecular simulations, and overall success was mixed, though several methods stood out. As in SAMPL6, we find that one strategy for achieving reasonable accuracy here was to make empirical corrections to binding predictions based on previous data for host categories which have been studied well before, though this can be of limited value when new systems are included. Additionally, we found that alchemical free energy methods using the AMOEBA polarizable force field had considerable success for the two host categories in which they participated. The new TrimerTrip system was also found to introduce some sampling problems, because multiple conformations may be relevant to binding and interconvert only slowly. Overall, results in this challenge tentatively suggest that further investigation of polarizable force fields for these challenges may be warranted.
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Affiliation(s)
- Martin Amezcua
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
| | - Léa El Khoury
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, 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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13
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Decherchi S, Cavalli A. Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation. Chem Rev 2020; 120:12788-12833. [PMID: 33006893 PMCID: PMC8011912 DOI: 10.1021/acs.chemrev.0c00534] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Indexed: 12/19/2022]
Abstract
Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.
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Affiliation(s)
- Sergio Decherchi
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
| | - Andrea Cavalli
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
- Department
of Pharmacy and Biotechnology, University
of Bologna, 40126 Bologna, Italy
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14
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Khalak Y, Tresadern G, de Groot BL, Gapsys V. Non-equilibrium approach for binding free energies in cyclodextrins in SAMPL7: force fields and software. J Comput Aided Mol Des 2020; 35:49-61. [PMID: 33230742 PMCID: PMC7862541 DOI: 10.1007/s10822-020-00359-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 11/07/2020] [Indexed: 11/24/2022]
Abstract
In the current work we report on our participation in the SAMPL7 challenge calculating absolute free energies of the host–guest systems, where 2 guest molecules were probed against 9 hosts-cyclodextrin and its derivatives. Our submission was based on the non-equilibrium free energy calculation protocol utilizing an averaged consensus result from two force fields (GAFF and CGenFF). The submitted prediction achieved accuracy of \documentclass[12pt]{minimal}
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\begin{document}$${1.38}\,\hbox {kcal}/\hbox {mol}$$\end{document}1.38kcal/mol in terms of the unsigned error averaged over the whole dataset. Subsequently, we further report on the underlying reasons for discrepancies between our calculations and another submission to the SAMPL7 challenge which employed a similar methodology, but disparate ligand and water force fields. As a result we have uncovered a number of issues in the dihedral parameter definition of the GAFF 2 force field. In addition, we identified particular cases in the molecular topologies where different software packages had a different interpretation of the same force field. This latter observation might be of particular relevance for systematic comparisons of molecular simulation software packages. The aforementioned factors have an influence on the final free energy estimates and need to be considered when performing alchemical calculations.
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Affiliation(s)
- Yuriy Khalak
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077, Göttingen, Germany
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077, Göttingen, Germany
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077, Göttingen, Germany.
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15
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Lee TS, Allen BK, Giese TJ, Guo Z, Li P, Lin C, McGee TD, Pearlman DA, Radak BK, Tao Y, Tsai HC, Xu H, Sherman W, York DM. Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery. J Chem Inf Model 2020; 60:5595-5623. [PMID: 32936637 PMCID: PMC7686026 DOI: 10.1021/acs.jcim.0c00613] [Citation(s) in RCA: 163] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.
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Affiliation(s)
- Tai-Sung Lee
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
| | - Bryce K. Allen
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Timothy J. Giese
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
| | - Zhenyu Guo
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Pengfei Li
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Charles Lin
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - T. Dwight McGee
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - David A. Pearlman
- QSimulate Incorporated, Cambridge, Massachusetts 02139, United States
| | - Brian K. Radak
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Yujun Tao
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
| | - Hsu-Chun Tsai
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
| | - Huafeng Xu
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Woody Sherman
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Darrin M. York
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
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16
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Schauperl M, Kantonen SM, Wang LP, Gilson MK. Data-driven analysis of the number of Lennard-Jones types needed in a force field. Commun Chem 2020; 3:173. [PMID: 34295996 PMCID: PMC8294475 DOI: 10.1038/s42004-020-00395-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 09/28/2020] [Indexed: 01/12/2023] Open
Abstract
Force fields used in molecular simulations contain numerical parameters, such as Lennard-Jones (LJ) parameters, which are assigned to the atoms in a molecule based on a classification of their chemical environments. The number of classes, or types, should be no more than needed to maximize agreement with experiment, as parsimony avoids overfitting and simplifies parameter optimization. However, types have historically been crafted based largely on chemical intuition, so current force fields may contain more types than needed. In this study, we seek the minimum number of LJ parameter types needed to represent key properties of organic liquids. We find that highly competitive force field accuracy is obtained with minimalist sets of LJ types; e.g. two H types and one type apiece for C, O, and N atoms. We also find that the fitness surface has multiple minima, which can lead to local trapping of the optimizer.
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Affiliation(s)
- Michael Schauperl
- Skaggs School of Pharmacy and Pharmaceutical Sciences, 9500 Gilman Drive, MC0751, University of California, San Diego, CA 92093-0751 USA
| | - Sophie M Kantonen
- Skaggs School of Pharmacy and Pharmaceutical Sciences, 9500 Gilman Drive, MC0751, University of California, San Diego, CA 92093-0751 USA
| | - Lee-Ping Wang
- Department of Chemistry, University of California, Davis, CA 95616 USA
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, 9500 Gilman Drive, MC0751, University of California, San Diego, CA 92093-0751 USA
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17
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Ametovski A, Macdonald C, Manning JJ, Haneef SAS, Santiago M, Martin L, Sparkes E, Reckers A, Gerona RR, Connor M, Glass M, Banister SD. Exploring Stereochemical and Conformational Requirements at Cannabinoid Receptors for Synthetic Cannabinoids Related to SDB-006, 5F-SDB-006, CUMYL-PICA, and 5F-CUMYL-PICA. ACS Chem Neurosci 2020; 11:3672-3682. [PMID: 33054155 DOI: 10.1021/acschemneuro.0c00591] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Synthetic cannabinoid receptor agonists (SCRAs) represent the most rapidly expanding class of new psychoactive substances (NPSs). Despite the prevalence and potency of recent chiral indole-3-carboxamide SCRAs, few pharmacological data are available regarding the enantiomeric bias of these NPSs toward human CB1 and CB2 receptors. A series of homochiral indole-3-carboxamides derived from (S)- and (R)-α-methylbenzylamine and featuring variation of the 1-alkyl substituent were prepared, pharmacologically evaluated, and compared to related achiral congeners derived from cumyl- and benzylamine. Competitive binding assays demonstrated that all analogues derived from either enantiomer of α-methylbenzylamine (14-17) showed affinities for CB1 (Ki = 47.9-813 nM) and CB2 (Ki = 47.9-347 nM) that were intermediate to that of the corresponding benzylic (10-13, CB1 Ki = 550 nM to >10 μM; CB2 Ki = 61.7 nM to >10 μM) and cumyl derivatives (6-9, CB1 Ki = 12.6-21.4 nM; CB2 Ki = 2.95-24.5 nM). In a fluorometric membrane potential assay, all α-methylbenzyl analogues (excluding 17) were potent, efficacious agonists of CB1 (EC50 = 32-464 nM; Emax = 89-104%) and low efficacy agonists of CB2 (EC50 = 54-500 nM; Emax = 52-77%), with comparable or greater potency than the benzyl analogues and much lower potency than the cumyl derivatives, consistent with binding trends. The relatively greater affinity and potency of (S)-14-17 compared to (R)-14-17 analogues at CB1 highlighted an enantiomeric bias for this series of SCRAs. Molecular dynamics simulations provided a conformational basis for the observed differences in agonist potency at CB1 pending benzylic substitution.
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Affiliation(s)
- Adam Ametovski
- The Lambert Initiative for Cannabinoid Therapeutics, Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
- School of Chemistry, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Christa Macdonald
- School of Medical Sciences, The University of Auckland, Auckland 1142, New Zealand
| | - Jamie J. Manning
- Department of Pharmacology and Toxicology, University of Otago, Dunedin 9016, New Zealand
| | - S. A. Syed Haneef
- Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Marina Santiago
- Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Lewis Martin
- The Lambert Initiative for Cannabinoid Therapeutics, Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
| | - Eric Sparkes
- The Lambert Initiative for Cannabinoid Therapeutics, Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
- School of Chemistry, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Andrew Reckers
- Clinical Toxicology and Environmental Biomonitoring Laboratory, University of California, San Francisco, California 94143, United States
| | - Roy R. Gerona
- Clinical Toxicology and Environmental Biomonitoring Laboratory, University of California, San Francisco, California 94143, United States
| | - Mark Connor
- Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Michelle Glass
- Department of Pharmacology and Toxicology, University of Otago, Dunedin 9016, New Zealand
| | - Samuel D. Banister
- The Lambert Initiative for Cannabinoid Therapeutics, Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
- School of Chemistry, The University of Sydney, Camperdown, NSW 2006, Australia
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18
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He X, Man VH, Yang W, Lee TS, Wang J. A fast and high-quality charge model for the next generation general AMBER force field. J Chem Phys 2020; 153:114502. [PMID: 32962378 DOI: 10.1063/5.0019056] [Citation(s) in RCA: 190] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The General AMBER Force Field (GAFF) has been broadly used by researchers all over the world to perform in silico simulations and modelings on diverse scientific topics, especially in the field of computer-aided drug design whose primary task is to accurately predict the affinity and selectivity of receptor-ligand binding. The atomic partial charges in GAFF and the second generation of GAFF (GAFF2) were originally developed with the quantum mechanics derived restrained electrostatic potential charge, but in practice, users usually adopt an efficient charge method, Austin Model 1-bond charge corrections (AM1-BCC), based on which, without expensive ab initio calculations, the atomic charges could be efficiently and conveniently obtained with the ANTECHAMBER module implemented in the AMBER software package. In this work, we developed a new set of BCC parameters specifically for GAFF2 using 442 neutral organic solutes covering diverse functional groups in aqueous solution. Compared to the original BCC parameter set, the new parameter set significantly reduced the mean unsigned error (MUE) of hydration free energies from 1.03 kcal/mol to 0.37 kcal/mol. More excitingly, this new AM1-BCC model also showed excellent performance in the solvation free energy (SFE) calculation on diverse solutes in various organic solvents across a range of different dielectric constants. In this large-scale test with totally 895 neutral organic solvent-solute systems, the new parameter set led to accurate SFE predictions with the MUE and the root-mean-square-error of 0.51 kcal/mol and 0.65 kcal/mol, respectively. This newly developed charge model, ABCG2, paved a promising path for the next generation GAFF development.
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Affiliation(s)
- Xibing He
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA
| | - Viet H Man
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA
| | - Wei Yang
- Department of Chemistry and Biochemistry and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, USA
| | - Tai-Sung Lee
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Junmei Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA
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19
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García-Simón C, Colomban C, Çetin YA, Gimeno A, Pujals M, Ubasart E, Fuertes-Espinosa C, Asad K, Chronakis N, Costas M, Jiménez-Barbero J, Feixas F, Ribas X. Complete Dynamic Reconstruction of C60, C70, and (C59N)2 Encapsulation into an Adaptable Supramolecular Nanocapsule. J Am Chem Soc 2020; 142:16051-16063. [DOI: 10.1021/jacs.0c07591] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Cristina García-Simón
- Institut de Quı́mica Computacional i Catàlisi (IQCC) and Departament de Quı́mica, Universitat de Girona, Campus Montilivi, Girona E-17003, Catalonia, Spain
| | - Cédric Colomban
- Institut de Quı́mica Computacional i Catàlisi (IQCC) and Departament de Quı́mica, Universitat de Girona, Campus Montilivi, Girona E-17003, Catalonia, Spain
| | - Yarkin Aybars Çetin
- Institut de Quı́mica Computacional i Catàlisi (IQCC) and Departament de Quı́mica, Universitat de Girona, Campus Montilivi, Girona E-17003, Catalonia, Spain
| | - Ana Gimeno
- CIC bioGUNE, Bizkaia Technology Park, Building 801A, 48170 Derio, Spain
| | - Míriam Pujals
- Institut de Quı́mica Computacional i Catàlisi (IQCC) and Departament de Quı́mica, Universitat de Girona, Campus Montilivi, Girona E-17003, Catalonia, Spain
| | - Ernest Ubasart
- Institut de Quı́mica Computacional i Catàlisi (IQCC) and Departament de Quı́mica, Universitat de Girona, Campus Montilivi, Girona E-17003, Catalonia, Spain
| | - Carles Fuertes-Espinosa
- Institut de Quı́mica Computacional i Catàlisi (IQCC) and Departament de Quı́mica, Universitat de Girona, Campus Montilivi, Girona E-17003, Catalonia, Spain
| | - Karam Asad
- Department of Chemistry, University of Cyprus, University str. 1, Building No. 13, 2109 Aglantzia, Nicosia, Cyprus
| | - Nikos Chronakis
- Department of Chemistry, University of Cyprus, University str. 1, Building No. 13, 2109 Aglantzia, Nicosia, Cyprus
| | - Miquel Costas
- Institut de Quı́mica Computacional i Catàlisi (IQCC) and Departament de Quı́mica, Universitat de Girona, Campus Montilivi, Girona E-17003, Catalonia, Spain
| | - Jesús Jiménez-Barbero
- CIC bioGUNE, Bizkaia Technology Park, Building 801A, 48170 Derio, Spain
- Ikerbasque, Basque Foundation for Science, Maria Diaz de Haro 13, 48009 Bilbao, Spain
- Department of Organic Chemistry II, Faculty of Science & Technology, University of the Basque Country, 48940 Leioa, Spain
| | - Ferran Feixas
- Institut de Quı́mica Computacional i Catàlisi (IQCC) and Departament de Quı́mica, Universitat de Girona, Campus Montilivi, Girona E-17003, Catalonia, Spain
| | - Xavi Ribas
- Institut de Quı́mica Computacional i Catàlisi (IQCC) and Departament de Quı́mica, Universitat de Girona, Campus Montilivi, Girona E-17003, Catalonia, Spain
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20
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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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