1
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Schmitz B, Frieg B, Homeyer N, Jessen G, Gohlke H. Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin. Arch Pharm (Weinheim) 2024; 357:e2300612. [PMID: 38319801 DOI: 10.1002/ardp.202300612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/18/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024]
Abstract
Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.
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Affiliation(s)
- Birte Schmitz
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Benedikt Frieg
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
| | - Nadine Homeyer
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gisela Jessen
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich, Jülich, Germany
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2
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Horton JT, Boothroyd S, Behara PK, Mobley DL, Cole DJ. A transferable double exponential potential for condensed phase simulations of small molecules. DIGITAL DISCOVERY 2023; 2:1178-1187. [PMID: 38013814 PMCID: PMC10408570 DOI: 10.1039/d3dd00070b] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/07/2023] [Indexed: 11/29/2023]
Abstract
The Lennard-Jones potential is the most widely-used function for the description of non-bonded interactions in transferable force fields for the condensed phase. This is not because it has an optimal functional form, but rather it is a legacy resulting from when computational expense was a major consideration and this potential was particularly convenient numerically. At present, it persists because the effort that would be required to re-write molecular modelling software and train new force fields has, until now, been prohibitive. Here, we present Smirnoff-plugins as a flexible framework to extend the Open Force Field software stack to allow custom force field functional forms. We deploy Smirnoff-plugins with the automated Open Force Field infrastructure to train a transferable, small molecule force field based on the recently-proposed double exponential functional form, on over 1000 experimental condensed phase properties. Extensive testing of the resulting force field shows improvements in transfer free energies, with acceptable conformational energetics, run times and convergence properties compared to state-of-the-art Lennard-Jones based force fields.
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Affiliation(s)
- Joshua T Horton
- School of Natural and Environmental Sciences, Newcastle University Newcastle upon Tyne NE1 7RU UK
| | | | - Pavan Kumar Behara
- Department of Pharmaceutical Sciences, University of California Irvine California 92697 USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California Irvine California 92697 USA
- Department of Chemistry, University of California Irvine California 92697 USA
| | - Daniel J Cole
- School of Natural and Environmental Sciences, Newcastle University Newcastle upon Tyne NE1 7RU UK
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3
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Alibay I, Magarkar A, Seeliger D, Biggin PC. Evaluating the use of absolute binding free energy in the fragment optimisation process. Commun Chem 2022; 5:105. [PMID: 36697714 PMCID: PMC9814858 DOI: 10.1038/s42004-022-00721-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/10/2022] [Indexed: 02/01/2023] Open
Abstract
Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman's r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely Nwat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.
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Affiliation(s)
- Irfan Alibay
- grid.4991.50000 0004 1936 8948Department of Biochemistry, The University of Oxford, South Parks Road, OX1 3QU Oxford, UK
| | - Aniket Magarkar
- grid.420061.10000 0001 2171 7500Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach an de Riß, Germany
| | - Daniel Seeliger
- grid.420061.10000 0001 2171 7500Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach an de Riß, Germany ,Present Address: Exscientia Inc, Office 400E, 2125 Biscayne Blvd, Miami, FL 33137 USA
| | - Philip Charles Biggin
- grid.4991.50000 0004 1936 8948Department of Biochemistry, The University of Oxford, South Parks Road, OX1 3QU Oxford, UK
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4
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Mattei A, Hong RS, Dietrich H, Firaha D, Helfferich J, Liu YM, Sasikumar K, Abraham NS, Miglani Bhardwaj R, Neumann MA, Sheikh AY. Efficient Crystal Structure Prediction for Structurally Related Molecules with Accurate and Transferable Tailor-Made Force Fields. J Chem Theory Comput 2022; 18:5725-5738. [PMID: 35930763 PMCID: PMC9476662 DOI: 10.1021/acs.jctc.2c00451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Crystal structure prediction (CSP) his generally used to complement experimental solid form screening and applied to individual molecules in drug development. The fast development of algorithms and computing resources offers the opportunity to use CSP earlier and for a broader range of applications in the drug design cycle. This study presents a novel paradigm of CSP specifically designed for structurally related molecules, referred to as Quick-CSP. The approach prioritizes more accurate physics through robust and transferable tailor-made force fields (TMFFs), such that significant efficiency gains are achieved through the reduction of expensive ab initio calculations. The accuracy of the TMFF is increased by the introduction of electrostatic multipoles, and the fragment-based force field parameterization scheme is demonstrated to be transferable for a family of chemically related molecules. The protocol is benchmarked with structurally related compounds from the Bromodomain and Extraterminal (BET) domain inhibitors series. A new convergence criterion is introduced that aims at performing only as many ab initio optimizations of crystal structures as required to locate the bottom of the crystal energy landscape within a user-defined accuracy. The overall approach provides significant cost savings ranging from three- to eight-fold less than the full-CSP workflow. The reported advancements expand the scope and utility of the underlying CSP building blocks as well as their novel reassembly to other applications earlier in the drug design cycle to guide molecule design and selection.
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Affiliation(s)
- Alessandra Mattei
- Solid State Chemistry, Research & Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, Illinois 60064, United States
| | - Richard S Hong
- Solid State Chemistry, Research & Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, Illinois 60064, United States
| | - Hanno Dietrich
- Avant-garde Materials Simulation, GmbH, Alte Str. 2, 79249 Merzhausen, Germany
| | - Dzmitry Firaha
- Avant-garde Materials Simulation, GmbH, Alte Str. 2, 79249 Merzhausen, Germany
| | - Julian Helfferich
- Avant-garde Materials Simulation, GmbH, Alte Str. 2, 79249 Merzhausen, Germany
| | - Yifei Michelle Liu
- Avant-garde Materials Simulation, GmbH, Alte Str. 2, 79249 Merzhausen, Germany
| | - Kiran Sasikumar
- Avant-garde Materials Simulation, GmbH, Alte Str. 2, 79249 Merzhausen, Germany
| | - Nathan S Abraham
- Solid State Chemistry, Research & Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, Illinois 60064, United States
| | - Rajni Miglani Bhardwaj
- Solid State Chemistry, Research & Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, Illinois 60064, United States
| | - Marcus A Neumann
- Avant-garde Materials Simulation, GmbH, Alte Str. 2, 79249 Merzhausen, Germany
| | - Ahmad Y Sheikh
- Solid State Chemistry, Research & Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, Illinois 60064, United States
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5
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Ringrose C, Horton JT, Wang LP, Cole DJ. Exploration and validation of force field design protocols through QM-to-MM mapping. Phys Chem Chem Phys 2022; 24:17014-17027. [PMID: 35792069 DOI: 10.1039/d2cp02864f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The scale of the parameter optimisation problem in traditional molecular mechanics force field construction means that design of a new force field is a long process, and sub-optimal choices made in the early stages can persist for many generations. We hypothesise that careful use of quantum mechanics to inform molecular mechanics parameter derivation (QM-to-MM mapping) should be used to significantly reduce the number of parameters that require fitting to experiment and increase the pace of force field development. Here, we design and train a collection of 15 new protocols for small, organic molecule force field derivation, and test their accuracy against experimental liquid properties. Our best performing model has only seven fitting parameters, yet achieves mean unsigned errors of just 0.031 g cm-3 and 0.69 kcal mol-1 in liquid densities and heats of vaporisation, compared to experiment. The software required to derive the designed force fields is freely available at https://github.com/qubekit/QUBEKit.
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Affiliation(s)
- Chris Ringrose
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
| | - Joshua T Horton
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
| | - Lee-Ping Wang
- Department of Chemistry, The University of California at Davis, Davis, California 95616, USA
| | - Daniel J Cole
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
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6
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Reif MM, Zacharias M. Computational Tools for Accurate Binding Free-Energy Prediction. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2385:255-292. [PMID: 34888724 DOI: 10.1007/978-1-0716-1767-0_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
A quantitative thermodynamic understanding of the noncovalent association of (bio)molecules is of central importance in molecular life sciences. An important quantity characterizing (bio)molecular association is the binding affinity or absolute binding free energy. In recent years, the computational prediction of absolute binding free energies has evolved considerably in terms of accuracy, computational speed, and user-friendliness. In this chapter, we first give an overview of how absolute free energies are defined and how they can be determined with computational means. We proceed with an outline of the theoretical basis of the two most reliable methods, potential of mean force, and double decoupling calculations. In particular, we describe how the sampling problem can be alleviated by application of restraints. Finally, we provide step-by-step instructions of how to set up corresponding molecular simulations with a commonly employed molecular dynamics simulation engine.
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Affiliation(s)
- Maria M Reif
- Physics Department (T38), Technische Universität München, Garching, Germany
| | - Martin Zacharias
- Physics Department (T38), Technische Universität München, Garching, Germany.
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7
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Accurate absolute free energies for ligand-protein binding based on non-equilibrium approaches. Commun Chem 2021; 4:61. [PMID: 36697634 PMCID: PMC9814727 DOI: 10.1038/s42004-021-00498-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 03/24/2021] [Indexed: 01/28/2023] Open
Abstract
The accurate calculation of the binding free energy for arbitrary ligand-protein pairs is a considerable challenge in computer-aided drug discovery. Recently, it has been demonstrated that current state-of-the-art molecular dynamics (MD) based methods are capable of making highly accurate predictions. Conventional MD-based approaches rely on the first principles of statistical mechanics and assume equilibrium sampling of the phase space. In the current work we demonstrate that accurate absolute binding free energies (ABFE) can also be obtained via theoretically rigorous non-equilibrium approaches. Our investigation of ligands binding to bromodomains and T4 lysozyme reveals that both equilibrium and non-equilibrium approaches converge to the same results. The non-equilibrium approach achieves the same level of accuracy and convergence as an equilibrium free energy perturbation (FEP) method enhanced by Hamiltonian replica exchange. We also compare uni- and bi-directional non-equilibrium approaches and demonstrate that considering the work distributions from both forward and reverse directions provides substantial accuracy gains. In summary, non-equilibrium ABFE calculations are shown to yield reliable and well-converged estimates of protein-ligand binding affinity.
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8
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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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9
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Song LF, Merz KM. Evolution of Alchemical Free Energy Methods in Drug Discovery. J Chem Inf Model 2020; 60:5308-5318. [DOI: 10.1021/acs.jcim.0c00547] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Lin Frank Song
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M. Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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10
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Pecina A, Eyrilmez SM, Köprülüoğlu C, Miriyala VM, Lepšík M, Fanfrlík J, Řezáč J, Hobza P. SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design. Chempluschem 2020; 85:2362-2371. [PMID: 32609421 DOI: 10.1002/cplu.202000120] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/27/2020] [Indexed: 12/17/2022]
Abstract
Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.
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Affiliation(s)
- Adam Pecina
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Saltuk M Eyrilmez
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Palacky University, 771 46, Olomouc, Czech Republic
| | - Cemal Köprülüoğlu
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Palacky University, 771 46, Olomouc, Czech Republic
| | - Vijay Madhav Miriyala
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Martin Lepšík
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Jindřich Fanfrlík
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Jan Řezáč
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Pavel Hobza
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Palacky University, 771 46, Olomouc, Czech Republic
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11
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Wang DD, Zhu M, Yan H. Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions. Brief Bioinform 2020; 22:5860693. [PMID: 32591817 DOI: 10.1093/bib/bbaa107] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 04/20/2020] [Accepted: 05/05/2020] [Indexed: 12/18/2022] Open
Abstract
Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions. In this paper, we review these two classes of methods, following a number of thermodynamic cycles for the free energy-based simulations and a feature-representation taxonomy for the machine learning-based scoring functions. More recent deep learning-based predictions, where a hierarchy of feature representations are generally extracted, are also reviewed. Strengths and weaknesses of the two classes of methods, coupled with future directions for improvements, are comparatively discussed.
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Affiliation(s)
- Debby D Wang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology
| | - Mengxu Zhu
- Department of Electrical Engineering, City University of Hong Kong
| | - Hong Yan
- College of Science and Engineering, City University of Hong Kong
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12
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Prentice JCA, Aarons J, Womack JC, Allen AEA, Andrinopoulos L, Anton L, Bell RA, Bhandari A, Bramley GA, Charlton RJ, Clements RJ, Cole DJ, Constantinescu G, Corsetti F, Dubois SMM, Duff KKB, Escartín JM, Greco A, Hill Q, Lee LP, Linscott E, O'Regan DD, Phipps MJS, Ratcliff LE, Serrano ÁR, Tait EW, Teobaldi G, Vitale V, Yeung N, Zuehlsdorff TJ, Dziedzic J, Haynes PD, Hine NDM, Mostofi AA, Payne MC, Skylaris CK. The ONETEP linear-scaling density functional theory program. J Chem Phys 2020; 152:174111. [PMID: 32384832 DOI: 10.1063/5.0004445] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
We present an overview of the onetep program for linear-scaling density functional theory (DFT) calculations with large basis set (plane-wave) accuracy on parallel computers. The DFT energy is computed from the density matrix, which is constructed from spatially localized orbitals we call Non-orthogonal Generalized Wannier Functions (NGWFs), expressed in terms of periodic sinc (psinc) functions. During the calculation, both the density matrix and the NGWFs are optimized with localization constraints. By taking advantage of localization, onetep is able to perform calculations including thousands of atoms with computational effort, which scales linearly with the number or atoms. The code has a large and diverse range of capabilities, explored in this paper, including different boundary conditions, various exchange-correlation functionals (with and without exact exchange), finite electronic temperature methods for metallic systems, methods for strongly correlated systems, molecular dynamics, vibrational calculations, time-dependent DFT, electronic transport, core loss spectroscopy, implicit solvation, quantum mechanical (QM)/molecular mechanical and QM-in-QM embedding, density of states calculations, distributed multipole analysis, and methods for partitioning charges and interactions between fragments. Calculations with onetep provide unique insights into large and complex systems that require an accurate atomic-level description, ranging from biomolecular to chemical, to materials, and to physical problems, as we show with a small selection of illustrative examples. onetep has always aimed to be at the cutting edge of method and software developments, and it serves as a platform for developing new methods of electronic structure simulation. We therefore conclude by describing some of the challenges and directions for its future developments and applications.
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Affiliation(s)
- Joseph C A Prentice
- Department of Materials, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Jolyon Aarons
- Department of Physics, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
| | - James C Womack
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Alice E A Allen
- TCM Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - Lampros Andrinopoulos
- Department of Physics, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Lucian Anton
- UKAEA, Culham Science Centre, Abingdon OX14 3DB, United Kingdom
| | - Robert A Bell
- TCM Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - Arihant Bhandari
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Gabriel A Bramley
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Robert J Charlton
- Department of Materials, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Rebecca J Clements
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Daniel J Cole
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Gabriel Constantinescu
- TCM Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - Fabiano Corsetti
- Department of Materials, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Simon M-M Dubois
- Institute of Condensed Matter and Nanosciences, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Kevin K B Duff
- TCM Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - José María Escartín
- TCM Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - Andrea Greco
- Department of Physics, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Quintin Hill
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Louis P Lee
- TCM Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - Edward Linscott
- TCM Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - David D O'Regan
- School of Physics, AMBER, and CRANN Institute, Trinity College Dublin, The University of Dublin, Dublin 2, Ireland
| | - Maximillian J S Phipps
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Laura E Ratcliff
- Department of Materials, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Álvaro Ruiz Serrano
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Edward W Tait
- TCM Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - Gilberto Teobaldi
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Valerio Vitale
- Department of Materials, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Nelson Yeung
- Department of Physics, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
| | - Tim J Zuehlsdorff
- Chemistry and Chemical Biology, University of California Merced, Merced, California 95343, USA
| | - Jacek Dziedzic
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Peter D Haynes
- Department of Materials, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Nicholas D M Hine
- Department of Physics, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
| | - Arash A Mostofi
- Department of Materials, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Mike C Payne
- TCM Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - Chris-Kriton Skylaris
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
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13
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Wallraven K, Holmelin FL, Glas A, Hennig S, Frolov AI, Grossmann TN. Adapting free energy perturbation simulations for large macrocyclic ligands: how to dissect contributions from direct binding and free ligand flexibility. Chem Sci 2020; 11:2269-2276. [PMID: 32180932 PMCID: PMC7057854 DOI: 10.1039/c9sc04705k] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 01/20/2020] [Indexed: 11/25/2022] Open
Abstract
Large and flexible ligands gain increasing interest in the development of bioactive agents. They challenge the applicability of computational ligand optimization strategies originally developed for small molecules. Free energy perturbation (FEP) is often used for predicting binding affinities of small molecule ligands, however, its use for more complex ligands remains limited. Herein, we report the structure-based design of peptide macrocycles targeting the protein binding site of human adaptor protein 14-3-3. We observe a surprisingly strong dependency of binding affinities on relatively small variations in substituent size. FEP was performed to rationalize observed trends. To account for insufficient convergence of FEP, restrained calculations were performed and complemented with extensive REST MD simulations of the free ligands. These calculations revealed that changes in affinity originate both from altered direct interactions and conformational changes of the free ligand. In addition, MD simulations provided the basis to rationalize unexpected trends in ligand lipophilicity. We also verified the anticipated interaction site and binding mode for one of the high affinity ligands by X-ray crystallography. The introduced fully-atomistic simulation protocol can be used to rationalize the development of structurally complex ligands which will support future ligand maturation efforts.
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Affiliation(s)
- Kerstin Wallraven
- Department of Chemistry & Pharmaceutical Sciences , VU University Amsterdam , De Boelelaan 1083 , 1081 HV Amsterdam , The Netherlands .
| | - Fredrik L Holmelin
- Medicinal Chemistry, Research and Early Development Cardiovascular, Renal and Metabolism , BioPharmaceuticals R&D , AstraZeneca , Pepparedsleden 1, Mölndal , 431 83 , Sweden .
| | - Adrian Glas
- Department of Chemistry & Pharmaceutical Sciences , VU University Amsterdam , De Boelelaan 1083 , 1081 HV Amsterdam , The Netherlands .
| | - Sven Hennig
- Department of Chemistry & Pharmaceutical Sciences , VU University Amsterdam , De Boelelaan 1083 , 1081 HV Amsterdam , The Netherlands .
| | - Andrey I Frolov
- Medicinal Chemistry, Research and Early Development Cardiovascular, Renal and Metabolism , BioPharmaceuticals R&D , AstraZeneca , Pepparedsleden 1, Mölndal , 431 83 , Sweden .
| | - Tom N Grossmann
- Department of Chemistry & Pharmaceutical Sciences , VU University Amsterdam , De Boelelaan 1083 , 1081 HV Amsterdam , The Netherlands .
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14
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Abstract
There is significant potential for electronic structure methods to improve the quality of the predictions furnished by the tools of computer-aided drug design, which typically rely on empirically derived functions. In this perspective, we consider some recent examples of how quantum mechanics has been applied in predicting protein-ligand geometries, protein-ligand binding affinities and ligand strain on binding. We then outline several significant developments in quantum mechanics methodology likely to influence these approaches: in particular, we note the advent of more computationally expedient ab initio quantum mechanical methods that can provide chemical accuracy for larger molecular systems than hitherto possible. We highlight the emergence of increasingly accurate semiempirical quantum mechanical methods and the associated role of machine learning and molecular databases in their development. Indeed, the convergence of improved algorithms for solving and analyzing electronic structure, modern machine learning methods, and increasingly comprehensive benchmark data sets of molecular geometries and energies provides a context in which the potential of quantum mechanics will be increasingly realized in driving future developments and applications in structure-based drug discovery.
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Affiliation(s)
- Richard A Bryce
- Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, UK.
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15
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Slochower DR, Henriksen NM, Wang LP, Chodera JD, Mobley DL, Gilson MK. Binding Thermodynamics of Host-Guest Systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative. J Chem Theory Comput 2019; 15:6225-6242. [PMID: 31603667 PMCID: PMC7328435 DOI: 10.1021/acs.jctc.9b00748] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Designing ligands that bind their target biomolecules with high affinity and specificity is a key step in small-molecule drug discovery, but accurately predicting protein-ligand binding free energies remains challenging. Key sources of errors in the calculations include inadequate sampling of conformational space, ambiguous protonation states, and errors in force fields. Noncovalent complexes between a host molecule with a binding cavity and a druglike guest molecule have emerged as powerful model systems. As model systems, host-guest complexes reduce many of the errors in more complex protein-ligand binding systems, as their small size greatly facilitates conformational sampling, and one can choose systems that avoid ambiguities in protonation states. These features, combined with their ease of experimental characterization, make host-guest systems ideal model systems to test and ultimately optimize force fields in the context of binding thermodynamics calculations. The Open Force Field Initiative aims to create a modern, open software infrastructure for automatically generating and assessing force fields using data sets. The first force field to arise out of this effort, named SMIRNOFF99Frosst, has approximately one tenth the number of parameters, in version 1.0.5, compared to typical general small molecule force fields, such as GAFF. Here, we evaluate the accuracy of this initial force field, using free energy calculations of 43 α and β-cyclodextrin host-guest pairs for which experimental thermodynamic data are available, and compare with matched calculations using two versions of GAFF. For all three force fields, we used TIP3P water and AM1-BCC charges. The calculations are performed using the attach-pull-release (APR) method as implemented in the open source package, pAPRika. For binding free energies, the root-mean-square error of the SMIRNOFF99Frosst calculations relative to experiment is 0.9 [0.7, 1.1] kcal/mol, while the corresponding results for GAFF 1.7 and GAFF 2.1 are 0.9 [0.7, 1.1] kcal/mol and 1.7 [1.5, 1.9] kcal/mol, respectively, with 95% confidence ranges in brackets. These results suggest that SMIRNOFF99Frosst performs competitively with existing small molecule force fields and is a parsimonious starting point for optimization.
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Affiliation(s)
- David R Slochower
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
| | - Niel M Henriksen
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
| | - Lee-Ping Wang
- Department of Chemistry , University of California , Davis , California 95616 , United States
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute , Memorial Sloan Kettering Cancer Center , New York , New York 10065 , United States
| | - David L Mobley
- Department of Pharmaceutical Sciences and Department of Chemistry , University of California , Irvine , California 92697 , United States
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
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16
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Allen AA, Robertson MJ, Payne MC, Cole DJ. Development and Validation of the Quantum Mechanical Bespoke Protein Force Field. ACS OMEGA 2019; 4:14537-14550. [PMID: 31528808 PMCID: PMC6740169 DOI: 10.1021/acsomega.9b01769] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 07/30/2019] [Indexed: 06/10/2023]
Abstract
Molecular mechanics force field parameters for macromolecules, such as proteins, are traditionally fit to reproduce experimental properties of small molecules, and thus, they neglect system-specific polarization. In this paper, we introduce a complete protein force field that is designed to be compatible with the quantum mechanical bespoke (QUBE) force field by deriving nonbonded parameters directly from the electron density of the specific protein under study. The main backbone and sidechain protein torsional parameters are rederived in this work by fitting to quantum mechanical dihedral scans for compatibility with QUBE nonbonded parameters. Software is provided for the preparation of QUBE input files. The accuracy of the new force field, and the derived torsional parameters, is tested by comparing the conformational preferences of a range of peptides and proteins with experimental measurements. Accurate backbone and sidechain conformations are obtained in molecular dynamics simulations of dipeptides, with NMR J coupling errors comparable to the widely used OPLS force field. In simulations of five folded proteins, the secondary structure is generally retained, and the NMR J coupling errors are similar to standard transferable force fields, although some loss of the experimental structure is observed in certain regions of the proteins. With several avenues for further development, the use of system-specific nonbonded force field parameters is a promising approach for next-generation simulations of biological molecules.
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Affiliation(s)
- Alice
E. A. Allen
- TCM
Group, Cavendish Laboratory, 19 JJ Thomson Ave, Cambridge CB3 0HE, United Kingdom
| | - Michael J. Robertson
- Department of Molecular and Cellular Physiology and Department of Structural Biology Stanford University School of Medicine, 279 Campus Drive, Stanford, California 94305, United States
| | - Michael C. Payne
- TCM
Group, Cavendish Laboratory, 19 JJ Thomson Ave, Cambridge CB3 0HE, United Kingdom
| | - Daniel J. Cole
- School
of Natural and Environmental Sciences, Newcastle
University, Newcastle
upon Tyne NE1 7RU, United
Kingdom
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17
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18
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Sokka IK, Ekholm FS, Johansson MP. Increasing the Potential of the Auristatin Cancer-Drug Family by Shifting the Conformational Equilibrium. Mol Pharm 2019; 16:3600-3608. [PMID: 31199662 PMCID: PMC6750905 DOI: 10.1021/acs.molpharmaceut.9b00437] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
![]()
Monomethyl auristatin E and monomethyl
auristatin F are widely
used cytotoxic agents in antibody–drug conjugates (ADCs), a
group of promising cancer drugs. The ADCs specifically target cancer
cells, releasing the auristatins inside, which results in the prevention
of mitosis. The auristatins suffer from a potentially serious flaw,
however. In solution, the molecules exist in an equal mixture of two
conformers, cis and trans. Only the trans-isomer is biologically active
and the isomerization process, i.e., the conversion of cis to trans
is slow. This significantly diminishes the efficiency of the drugs
and their corresponding ADCs, and perhaps more importantly, raises
concerns over drug safety. The potency of the auristatins would be
enhanced by decreasing the amount of the biologically inactive isomer,
either by stabilizing the trans-isomer or destabilizing the cis-isomer.
Here, we follow the computer-aided design strategy of shifting the
conformational equilibrium and employ high-level quantum chemical
modeling to identify promising candidates for improved auristatins.
Coupled cluster calculations predict that a simple halogenation in
the norephedrine/phenylalanine residues shifts the isomer equilibrium
almost completely toward the active trans-conformation, due to enhanced
intramolecular interactions specific to the active isomer.
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Affiliation(s)
- Iris K Sokka
- Department of Chemistry , University of Helsinki , P.O. Box 55, FI-00014 Helsinki , Finland
| | - Filip S Ekholm
- Department of Chemistry , University of Helsinki , P.O. Box 55, FI-00014 Helsinki , Finland
| | - Mikael P Johansson
- Department of Chemistry , University of Helsinki , P.O. Box 55, FI-00014 Helsinki , Finland.,Helsinki Institute of Sustainability Science, HELSUS , FI-00014 Helsinki , Finland
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19
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Manz TA, Chen T, Cole DJ, Limas NG, Fiszbein B. New scaling relations to compute atom-in-material polarizabilities and dispersion coefficients: part 1. Theory and accuracy. RSC Adv 2019; 9:19297-19324. [PMID: 35519408 PMCID: PMC9064874 DOI: 10.1039/c9ra03003d] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 06/03/2019] [Indexed: 11/21/2022] Open
Abstract
A new method was developed to compute atom-in-material polarizabilities and dispersion coefficients for diverse material types.
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Affiliation(s)
- Thomas A. Manz
- Department of Chemical & Materials Engineering
- New Mexico State University
- Las Cruces
- USA
| | - Taoyi Chen
- Department of Chemical & Materials Engineering
- New Mexico State University
- Las Cruces
- USA
| | - Daniel J. Cole
- School of Natural and Environmental Sciences
- Newcastle University
- Newcastle upon Tyne NE1 7RU
- UK
| | - Nidia Gabaldon Limas
- Department of Chemical & Materials Engineering
- New Mexico State University
- Las Cruces
- USA
| | - Benjamin Fiszbein
- Department of Chemical & Materials Engineering
- New Mexico State University
- Las Cruces
- USA
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