1
|
Charry J, Tkatchenko A. van der Waals Radii of Free and Bonded Atoms from Hydrogen (Z = 1) to Oganesson (Z = 118). J Chem Theory Comput 2024. [PMID: 39208255 DOI: 10.1021/acs.jctc.4c00784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Reliable numerical values of van der Waals (vdW) radii are required for constructing empirical force fields, vdW-inclusive density functional, and quantum-chemical methods, as well as for implicit solvent models. However, multiple definitions exist for vdW radii, involving either equilibrium or the closest contact distances between free or bonded atoms within molecules or crystals. For the paradigmatic case of the hydrogen atom, its reported vdW radius fluctuates between 2.15 and 3.70 Bohr depending on the definition, leading to a high uncertainty in calculations and different conceptual interpretations of noncovalent interactions. In this work, we systematically review different definitions and methodologies to establish the free and bonded vdW radii for hydrogen, based on equilibrium vdW distances in noncovalently bonded molecules, enveloping electron density cutoffs, noncovalent positron bonds in hydrogen anion dimer, vacuum virtual photon cloud caused by the hydrogen atom, and atomic dipole polarizability. By doing so, we show that the vdW radius of the free hydrogen atom is 3.16 ± 0.06 Bohr. By employing the most general and elegant definition of atomic vdW radius as a function of the atomic polarizability, we tabulate consistent values of vdW radii for all atoms in the periodic table up to Z = 118.
Collapse
Affiliation(s)
- Jorge Charry
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| |
Collapse
|
2
|
Wang X, Wang Y, Guo M, Wang X, Li Y, Zhang JZH. Assessment of an Electrostatic Energy-Based Charge Model for Modeling the Electrostatic Interactions in Water Solvent. J Chem Theory Comput 2023; 19:6294-6312. [PMID: 37656610 DOI: 10.1021/acs.jctc.3c00467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Abstract
The protein force field based on the restrained electrostatic potential (RESP) charges has limitations in accurately describing hydrogen bonding interactions in proteins. To address this issue, we propose an alternative approach called the electrostatic energy-based charges (EEC) model, which shows improved performance in describing electrostatic interactions (EIs) of hydrogen bonds in proteins. In this study, we further investigate the performance of the EEC model in modeling EIs in water solvent. Our findings demonstrate that the fixed EEC model can effectively reproduce the quantum mechanics/molecular mechanics (QM/MM)-calculated EIs between a water molecule and various water solvent environments. However, to achieve the same level of computational accuracy, the electrostatic potential (ESP) charge model needs to fluctuate according to the electrostatic environment. Our analysis indicates that the requirement for charge adjustments depends on the specific mathematical and physical representation of EIs as a function of the environment for deriving charges. By comparing with widely used empirical water models calibrated to reproduce experimental properties, we confirm that the performance of the EEC model in reproducing QM/MM EIs is similar to that of general purpose TIP4P-like water models such as TIP4P-Ew and TIP4P/2005. When comparing the computed 10,000 distinct EI values within the range of -40 to 0 kcal/mol with the QM/MM results calculated at the MP2/aug-cc-pVQZ/TIP3P level, we noticed that the mean unsigned error (MUE) for the EEC model is merely 0.487 kcal/mol, which is remarkably similar to the MUE values of the TIP4P-Ew (0.63 kcal/mol) and TIP4P/2005 (0.579 kcal/mol) models. However, both the RESP method and the TIP3P model exhibit a tendency to overestimate the EIs, as evidenced by their higher MUE values of 1.761 and 1.293 kcal/mol, respectively. EEC-based molecular dynamics simulations have demonstrated that, when combined with appropriate van der Waals parameters, the EEC model can closely reproduce oxygen-oxygen radial distribution function and density of water, showing a remarkable similarity to the well-established TIP4P-like empirical water models. Our results demonstrate that the EEC model has the potential to build force fields with comparable accuracy to more sophisticated empirical TIP4P-like water models.
Collapse
Affiliation(s)
- Xianwei Wang
- College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
| | - Yiying Wang
- College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
| | - Man Guo
- College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
| | - Xuechao Wang
- College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
| | - Yang Li
- College of Information Science and Engineering, Shandong Agricultural University, Tai'an, Shandong 271018, China
| | - John Z H Zhang
- Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| |
Collapse
|
3
|
Müller M, Hagg A, Strickstrock R, Hülsmann M, Asteroth A, Kirschner KN, Reith D. Determining Lennard-Jones Parameters Using Multiscale Target Data through Presampling-Enhanced, Surrogate-Assisted Global Optimization. J Chem Inf Model 2023; 63:1872-1881. [PMID: 36942658 DOI: 10.1021/acs.jcim.2c01231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Force field-based models are a Newtonian mechanics approximation of reality and are inherently noisy. Coupling models from different molecular scale domains (including single, gas-phase molecules up to multimolecule, condensed phase ensembles) is difficult, which is also the case for finding solutions that transfer well between the scales. In this contribution, we introduce a surrogate-assisted algorithm to optimize Lennard-Jones parameters for target data from different scale domains to overcome the difficulties named above. Specifically, our approach combines a surrogate-assisted global evolutionary optimization method with a presampling phase that takes advantage of one scale domain being less computationally expensive to evaluate. The algorithm's components were evaluated individually, elucidating their individual merits. Our findings show that the process of parametrizing force fields can significantly benefit from both the presampling method, which alleviates the need to have a good initial guess for the parameters, and the surrogate model, which improves efficiency.
Collapse
Affiliation(s)
- Max Müller
- Department of Computer Science, Bonn-Rhein-Sieg University of Applied Sciences, 53757 Sankt Augustin, Germany
| | - Alexander Hagg
- Department of Electrical Engineering, Mechanical Engineering and Technical Journalism, Bonn-Rhein-Sieg University of Applied Sciences, 53757 Sankt Augustin, Germany
| | - Robin Strickstrock
- Department of Electrical Engineering, Mechanical Engineering and Technical Journalism, Bonn-Rhein-Sieg University of Applied Sciences, 53757 Sankt Augustin, Germany
| | - Marco Hülsmann
- Department of Computer Science, Bonn-Rhein-Sieg University of Applied Sciences, 53757 Sankt Augustin, Germany
| | - Alexander Asteroth
- Department of Computer Science, Bonn-Rhein-Sieg University of Applied Sciences, 53757 Sankt Augustin, Germany
| | - Karl N Kirschner
- Department of Computer Science, Bonn-Rhein-Sieg University of Applied Sciences, 53757 Sankt Augustin, Germany
| | - Dirk Reith
- Department of Electrical Engineering, Mechanical Engineering and Technical Journalism, Bonn-Rhein-Sieg University of Applied Sciences, 53757 Sankt Augustin, Germany
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| |
Collapse
|
4
|
Zaman N, Azam SS. Quantum Dynamics and Bi Metal Force Field Parameterization Yielding Significant Antileishmanial Targets. J Chem Inf Model 2023; 63:1371-1385. [PMID: 36730993 DOI: 10.1021/acs.jcim.2c01100] [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: 02/04/2023]
Abstract
Amid emerging drug resistance to metal inhibitors, high toxicity, and onerous drug delivery procedures, the computational design of alternate formulations encompassing functional metal-containing compounds greatly relies on large-scale atomistic simulations. Simulations particularly with Au(I), Ag, Bi(V), and Sb(V) pose a major challenge to elucidate their molecular mechanism due to the absence of force field parameters. This study thus quantum mechanically derives force field parameters of Bi(V) as an extension of the previous experimental study conducted on heteroleptic triorganobismuth(V) biscarboxylates of type [BiR3(O2CR')2]. We have modeled two organo-bismuth(V) carboxylates, which are optimized and parameterized along with the famous pentavalent antimonial drug: meglumine antimoniate using quantum mechanics original Seminarian methods with the SBKJC effective core potential (ECP) basis set. Furthermore, molecular dynamics (MD) simulations of bismuth- and antimony-containing compounds in complex with two enzymes, trypanothione synthetase-amidase (TSA) and trypanothione reductase, are performed to target the (T(SH)2) pathway at multiple points. MD simulations provide novel insights into the binding mechanism of TSA and highlight the role of a single residue Arg569 in modulating the ligand dynamics. Moreover, the presence of an ortho group in a ligand is emphasized to facilitate interactions between Arg569 and the active site residue Arg313 for higher inhibitory activity of TSA. This preliminary generation of parameters specific to bismuth validated by simulations in replica will become a preamble of future computational and experimental research work to open avenues for newer and suitable drug targets.
Collapse
Affiliation(s)
- Naila Zaman
- Computational Biology Lab, National Center for Bioinformatics, Quaid-i-Azam University, Islamabad45320, Pakistan
| | - Syed Sikander Azam
- Computational Biology Lab, National Center for Bioinformatics, Quaid-i-Azam University, Islamabad45320, Pakistan
| |
Collapse
|
5
|
Csizi K, Reiher M. Universal
QM
/
MM
approaches for general nanoscale applications. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
| | - Markus Reiher
- Laboratorium für Physikalische Chemie ETH Zürich Zürich Switzerland
| |
Collapse
|
6
|
Thürlemann M, Böselt L, Riniker S. Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions. J Chem Theory Comput 2023; 19:562-579. [PMID: 36633918 PMCID: PMC9878731 DOI: 10.1021/acs.jctc.2c00661] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Indexed: 01/13/2023]
Abstract
Simulations of molecular systems using electronic structure methods are still not feasible for many systems of biological importance. As a result, empirical methods such as force fields (FF) have become an established tool for the simulation of large and complex molecular systems. The parametrization of FF is, however, time-consuming and has traditionally been based on experimental data. Recent years have therefore seen increasing efforts to automatize FF parametrization or to replace FF with machine-learning (ML) based potentials. Here, we propose an alternative strategy to parametrize FF, which makes use of ML and gradient-descent based optimization while retaining a functional form founded in physics. Using a predefined functional form is shown to enable interpretability, robustness, and efficient simulations of large systems over long time scales. To demonstrate the strength of the proposed method, a fixed-charge and a polarizable model are trained on ab initio potential-energy surfaces. Given only information about the constituting elements, the molecular topology, and reference potential energies, the models successfully learn to assign atom types and corresponding FF parameters from scratch. The resulting models and parameters are validated on a wide range of experimentally and computationally derived properties of systems including dimers, pure liquids, and molecular crystals.
Collapse
Affiliation(s)
- Moritz Thürlemann
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Lennard Böselt
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| |
Collapse
|
7
|
Blunt NS, Camps J, Crawford O, Izsák R, Leontica S, Mirani A, Moylett AE, Scivier SA, Sünderhauf C, Schopf P, Taylor JM, Holzmann N. Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications. J Chem Theory Comput 2022; 18:7001-7023. [PMID: 36355616 DOI: 10.1021/acs.jctc.2c00574] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Computational chemistry is an essential tool in the pharmaceutical industry. Quantum computing is a fast evolving technology that promises to completely shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. This perspective illustrates the near-future applicability of quantum computation of molecules to pharmaceutical problems. We briefly summarize and compare the scaling properties of state-of-the-art quantum algorithms and provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex involving the drug Ibrutinib. Carrying out these calculations requires an error-corrected quantum architecture that we describe. Our estimates showcase that recent developments on quantum phase estimation algorithms have dramatically reduced the quantum resources needed to run fully quantum calculations in active spaces of around 50 orbitals and electrons, from estimated over 1000 years using the Trotterization approach to just a few days with sparse qubitization, painting a picture of fast and exciting progress in this nascent field.
Collapse
Affiliation(s)
- Nick S Blunt
- Riverlane, St. Andrews House, 59 St. Andrews Street, Cambridge CB2 3BZ, United Kingdom
| | - Joan Camps
- Riverlane, St. Andrews House, 59 St. Andrews Street, Cambridge CB2 3BZ, United Kingdom
| | - Ophelia Crawford
- Riverlane, St. Andrews House, 59 St. Andrews Street, Cambridge CB2 3BZ, United Kingdom
| | - Róbert Izsák
- Riverlane, St. Andrews House, 59 St. Andrews Street, Cambridge CB2 3BZ, United Kingdom
| | - Sebastian Leontica
- Riverlane, St. Andrews House, 59 St. Andrews Street, Cambridge CB2 3BZ, United Kingdom
| | - Arjun Mirani
- Riverlane, St. Andrews House, 59 St. Andrews Street, Cambridge CB2 3BZ, United Kingdom
| | - Alexandra E Moylett
- Riverlane, St. Andrews House, 59 St. Andrews Street, Cambridge CB2 3BZ, United Kingdom
| | - Sam A Scivier
- Riverlane, St. Andrews House, 59 St. Andrews Street, Cambridge CB2 3BZ, United Kingdom
| | - Christoph Sünderhauf
- Riverlane, St. Andrews House, 59 St. Andrews Street, Cambridge CB2 3BZ, United Kingdom
| | - Patrick Schopf
- Astex Pharmaceuticals, 436 Cambridge Science Park, Cambridge CB4 0QA, United Kingdom
| | - Jacob M Taylor
- Riverlane, St. Andrews House, 59 St. Andrews Street, Cambridge CB2 3BZ, United Kingdom
| | - Nicole Holzmann
- Riverlane, St. Andrews House, 59 St. Andrews Street, Cambridge CB2 3BZ, United Kingdom.,Astex Pharmaceuticals, 436 Cambridge Science Park, Cambridge CB4 0QA, United Kingdom
| |
Collapse
|
8
|
González D, Macaya L, Castillo-Orellana C, Verstraelen T, Vogt-Geisse S, Vöhringer-Martinez E. Nonbonded Force Field Parameters from Minimal Basis Iterative Stockholder Partitioning of the Molecular Electron Density Improve CB7 Host-Guest Affinity Predictions. J Chem Inf Model 2022; 62:4162-4174. [PMID: 35959540 DOI: 10.1021/acs.jcim.2c00316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Binding affinity prediction by means of computer simulation has been increasingly incorporated in drug discovery projects. Its wide application, however, is limited by the prediction accuracy of the free energy calculations. The main error sources are force fields used to describe molecular interactions and incomplete sampling of the configurational space. Organic host-guest systems have been used to address force field quality because they share similar interactions found in ligands and receptors, and their rigidity facilitates configurational sampling. Here, we test the binding free energy prediction accuracy for 14 guests with an aromatic or adamantane core and the CB7 host using molecular electron density derived nonbonded force field parameters. We developed a computational workflow written in Python to derive atomic charges and Lennard-Jones parameters with the Minimal Basis Iterative Stockholder method using the polarized electron density of several configurations of each guest in the bound and unbound states. The resulting nonbonded force field parameters improve binding affinity prediction, especially for guests with an adamantane core in which repulsive exchange and dispersion interactions to the host dominate.
Collapse
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
| | - Carlos Castillo-Orellana
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Toon Verstraelen
- Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnnarde 46, B-9052 Ghent, Belgium
| | - Stefan Vogt-Geisse
- 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
| |
Collapse
|
9
|
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.
Collapse
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.
| |
Collapse
|
10
|
Szabadi A, Schröder C. Recent Developments in Polarizable Molecular Dynamics Simulations of Electrolyte Solutions. JOURNAL OF COMPUTATIONAL BIOPHYSICS AND CHEMISTRY 2022. [DOI: 10.1142/s2737416521420035] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Polarizable molecular dynamics simulations are a fast progressing field in the scientific research of ionic liquids. The fundamentals of polarizable simulations, as well as their application to ionic liquids, were summarized in a review [Bedrov, D.; Piquemal, J.-P.; Borodin, O.; MacKerell, Jr., A. D.; Roux, B.; Schröder, C. Molecular Dynamics Simulations of Ionic Liquids and Electrolytes Using Polarizable Force Fields. Chem. Rev. 2019, 119, 7940–7995] in 2019. Since then, new methods to treat intermolecular interaction of induced dipoles in these highly charged systems were developed. This concerns the damping of these interactions and additional charge transfer as well as the prediction of ionic materials with ultrahigh refractive indices. In addition to the progress of the polarizable force fields, also thermostats and barostats for polarizable simulations evolved recently.
Collapse
Affiliation(s)
- András Szabadi
- University of Vienna, Faculty of Chemistry, Department of Computational Biological Chemistry, A-1090 Vienna, Austria
| | - Christian Schröder
- University of Vienna, Faculty of Chemistry, Department of Computational Biological Chemistry, A-1090 Vienna, Austria
| |
Collapse
|
11
|
P. Oliveira M, Hünenberger PH. Systematic optimization of a fragment-based force field against experimental pure-liquid properties considering large compound families: application to oxygen and nitrogen compounds. Phys Chem Chem Phys 2021; 23:17774-17793. [PMID: 34350931 PMCID: PMC8386690 DOI: 10.1039/d1cp02001c] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 06/30/2021] [Indexed: 12/04/2022]
Abstract
The CombiFF approach is a workflow for the automated refinement of force-field parameters against experimental condensed-phase data, considering entire classes of organic molecules constructed using a fragment library via combinatorial isomer enumeration. One peculiarity of this approach is that it relies on an electronegativity-equalization scheme to account for induction effects within molecules, with values of the atomic hardness and electronegativity as electrostatic parameters, rather than the partial charges themselves. In a previous article [M. P. Oliveira, M. Andrey, S. R. Rieder, L. Kern, D. F. Hahn, S. Riniker, B. A. C. Horta and P. H. Hünenberger, J. Chem. Theory. Comput. 2020, 16, 7525], CombiFF was introduced and applied to calibrate a GROMOS-compatible united-atom force field for the saturated acyclic (halo-)alkane family. Here, this scheme is employed for the construction of a corresponding force field for saturated acyclic compounds encompassing eight common chemical functional groups involving oxygen and/or nitrogen atoms, namely: ether, aldehyde, ketone, ester, alcohol, carboxylic acid, amine, and amide. Monofunctional as well as homo-polyfunctional compounds are considered. A total of 1712 experimental liquid densities ρliq and vaporization enthalpies ΔHvap concerning 1175 molecules are used for the calibration (339 molecules) and validation (836 molecules) of the 102 non-bonded interaction parameters of the force field. Using initial parameter values based on the GROMOS 2016H66 parameter set, convergence is reached after five iterations. Given access to one processor per simulated system, this operation only requires a few days of wall-clock computing time. After optimization, the root-mean-square deviations from experiment are 29.9 (22.4) kg m-3 for ρliq and 4.1 (5.5) kJ mol-1 for ΔHvap for the calibration (validation) set. Thus, a very good level of agreement with experiment is achieved in terms of these two properties, although the errors are inhomogeneously distributed across the different chemical functional groups.
Collapse
Affiliation(s)
- Marina P. Oliveira
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCICH-8093 ZürichSwitzerland+41 44 632 5503
| | - Philippe H. Hünenberger
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCICH-8093 ZürichSwitzerland+41 44 632 5503
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Oliveira MP, Andrey M, Rieder SR, Kern L, Hahn DF, Riniker S, Horta BAC, Hünenberger PH. Systematic Optimization of a Fragment-Based Force Field against Experimental Pure-Liquid Properties Considering Large Compound Families: Application to Saturated Haloalkanes. J Chem Theory Comput 2020; 16:7525-7555. [DOI: 10.1021/acs.jctc.0c00683] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Marina P. Oliveira
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Maurice Andrey
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Salomé R. Rieder
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Leyla Kern
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| | - David F. Hahn
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Sereina Riniker
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Bruno A. C. Horta
- Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil
| | - Philippe H. Hünenberger
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| |
Collapse
|
14
|
Kashefolgheta S, Oliveira MP, Rieder SR, Horta BAC, Acree WE, Hünenberger PH. Evaluating Classical Force Fields against Experimental Cross-Solvation Free Energies. J Chem Theory Comput 2020; 16:7556-7580. [DOI: 10.1021/acs.jctc.0c00688] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Sadra Kashefolgheta
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Marina P. Oliveira
- 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
- Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil
| | - William E. Acree
- Department of Chemistry, University of North Texas, 1155 Union Circle Drive #305070, Denton, Texas 76203, United States
| | - Philippe H. Hünenberger
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| |
Collapse
|
15
|
Visscher KM, Geerke DP. Deriving a Polarizable Force Field for Biomolecular Building Blocks with Minimal Empirical Calibration. J Phys Chem B 2020; 124:1628-1636. [PMID: 32073849 PMCID: PMC7061328 DOI: 10.1021/acs.jpcb.9b10903] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 01/23/2020] [Indexed: 12/31/2022]
Abstract
Force field parametrization involves a complex set of linked optimization problems, with the goal of describing complex molecular interactions by using simple classical potential-energy functions that model Coulomb interactions, dispersion, and exchange repulsion. These functions comprise a set of atomic (and molecular) parameters and together with the bonded terms they constitute the molecular mechanics force field. Traditionally, many of these parameters have been fitted in a calibration approach in which experimental measures for thermodynamic and other relevant properties of small-molecule compounds are used for fitting and validation. As these approaches are laborious and time-consuming and represent an underdetermined optimization problem, we study methods to fit and derive an increasing number of parameters directly from electronic structure calculations, in order to greatly reduce possible parameter space for the remaining free parameters. In the current work we investigate a polarizable model with a higher order dispersion term for use in biomolecular simulation. Results for 49 biochemically relevant molecules are presented including updated parameters for hydrocarbon side chains. We show that our recently presented set of QM/MM derived atomic polarizabilities can be used in direct conjunction with partial charges and a higher order dispersion model that are quantum-mechanically determined, to freeze nearly all (i.e., 132 out of 138) nonbonded parameters to their quantum determined values.
Collapse
Affiliation(s)
- Koen M. Visscher
- AIMMS Division of Molecular
Toxicology, De Boelelaan
1108, 1081 HV Amsterdam, The Netherlands
| | - Daan P. Geerke
- AIMMS Division of Molecular
Toxicology, De Boelelaan
1108, 1081 HV Amsterdam, The Netherlands
| |
Collapse
|
16
|
Kantonen SM, Muddana HS, Schauperl M, Henriksen NM, Wang LP, Gilson MK. Data-Driven Mapping of Gas-Phase Quantum Calculations to General Force Field Lennard-Jones Parameters. J Chem Theory Comput 2020; 16:1115-1127. [PMID: 31917572 PMCID: PMC7101068 DOI: 10.1021/acs.jctc.9b00713] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular dynamics simulations are helpful tools for a range of applications, ranging from drug discovery to protein structure determination. The successful use of this technology largely depends on the potential function, or force field, used to determine the potential energy at each configuration of the system. Most force fields encode all of the relevant parameters to be used in distinct atom types, each associated with parameters for all parts of the force field, typically bond stretches, angle bends, torsions, and nonbonded terms accounting for van der Waals and electrostatic interactions. Much attention has been paid to the nonbonded parameters and their derivation, which are important in particular due to their governance of noncovalent interactions, such as protein-ligand binding. Parametrization involves adjusting the nonbonded parameters to minimize the error between simulation results and experimental properties, such as heats of vaporization and densities of neat liquids. In this setting, determining the best set of atom types is far from trivial, and the large number of parameters to be fit for the atom types in a typical force field can make it difficult to approach a true optimum. Here, we utilize a previously described Minimal Basis Iterative Stockholder (MBIS) method to carry out an atoms-in-molecules partitioning of electron densities. Information from these atomic densities is then mapped to Lennard-Jones parameters using a set of mapping parameters much smaller than the typical number of atom types in a force field. This approach is advantageous in two ways: it eliminates atom types by allowing each atom to have unique Lennard-Jones parameters, and it greatly reduces the number of parameters to be optimized. We show that this approach yields results comparable to those obtained with the typed GAFF 1.7 force field, even when trained on a relatively small amount of experimental data.
Collapse
Affiliation(s)
- Sophie M Kantonen
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , 9500 Gilman Drive , La Jolla , California 92093-0736 , United States
| | - Hari S Muddana
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , 9500 Gilman Drive , La Jolla , California 92093-0736 , United States
- OpenEye Scientific Software, Inc. , 9 Bisbee Court, Suite D , Santa Fe , New Mexico 87508 , United States
| | - Michael Schauperl
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , 9500 Gilman Drive , La Jolla , California 92093-0736 , United States
| | - Niel M Henriksen
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , 9500 Gilman Drive , La Jolla , California 92093-0736 , United States
- AtomWise, Inc. , 717 Market Street, Suite 800 , San Francisco , California 94103 , United States
| | - Lee-Ping Wang
- Department of Chemistry , University of California Davis , One Shields Avenue , Davis , California 95616 , United States
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , 9500 Gilman Drive , La Jolla , California 92093-0736 , United States
| |
Collapse
|
17
|
Inakollu VS, Geerke DP, Rowley CN, Yu H. Polarisable force fields: what do they add in biomolecular simulations? Curr Opin Struct Biol 2020; 61:182-190. [PMID: 32044671 DOI: 10.1016/j.sbi.2019.12.012] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 12/18/2019] [Accepted: 12/19/2019] [Indexed: 12/11/2022]
Abstract
The quality of biomolecular simulations critically depends on the accuracy of the force field used to calculate the potential energy of the molecular configurations. Currently, most simulations employ non-polarisable force fields, which describe electrostatic interactions as the sum of Coulombic interactions between fixed atomic charges. Polarisation of these charge distributions is incorporated only in a mean-field manner. In the past decade, extensive efforts have been devoted to developing simple, efficient, and yet generally applicable polarisable force fields for biomolecular simulations. In this review, we summarise the latest developments in accounting for key biomolecular interactions with polarisable force fields and applications to address challenging biological questions. In the end, we provide an outlook for future development in polarisable force fields.
Collapse
Affiliation(s)
- Vs Sandeep Inakollu
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong NSW 2522, Australia; Molecular Horizons, University of Wollongong, Wollongong NSW 2522 Australia; Illawarra Health and Medical Research Institute, Wollongong NSW 2522, Australia
| | - Daan P Geerke
- AIMMS Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, the Netherlands.
| | - Christopher N Rowley
- Department of Chemistry, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada.
| | - Haibo Yu
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong NSW 2522, Australia; Molecular Horizons, University of Wollongong, Wollongong NSW 2522 Australia; Illawarra Health and Medical Research Institute, Wollongong NSW 2522, Australia.
| |
Collapse
|
18
|
Abstract
Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.
Collapse
Affiliation(s)
- Martin Kotev
- Global Research Informatics/Cheminformatics and Drug Design, Evotec (France) SAS, Toulouse, France
| | - Laurie Sarrat
- Global Research Informatics/Cheminformatics and Drug Design, Evotec (France) SAS, Toulouse, France
| | | |
Collapse
|
19
|
Horton JT, Allen AEA, Cole DJ. Modelling flexible protein–ligand binding in p38α MAP kinase using the QUBE force field. Chem Commun (Camb) 2020; 56:932-935. [DOI: 10.1039/c9cc08574b] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The accuracy of quantum mechanical bespoke (QUBE) force fields for protein–ligand binding free energy calculations are benchmarked against experiment.
Collapse
Affiliation(s)
- Joshua T. Horton
- School of Natural and Environmental Sciences
- Newcastle University
- Newcastle upon Tyne NE1 7RU
- UK
| | | | - Daniel J. Cole
- School of Natural and Environmental Sciences
- Newcastle University
- Newcastle upon Tyne NE1 7RU
- UK
| |
Collapse
|
20
|
Vincent MA, Silva AF, Popelier PLA. Atomic Partitioning of the MPn (n = 2, 3, 4) Dynamic Electron Correlation Energy by the Interacting Quantum Atoms Method: A Fast and Accurate Electrostatic Potential Integral Approach. J Comput Chem 2019; 40:2793-2800. [PMID: 31373709 PMCID: PMC6900022 DOI: 10.1002/jcc.26037] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 07/10/2019] [Accepted: 07/11/2019] [Indexed: 11/13/2022]
Abstract
Recently, the quantum topological energy partitioning method called interacting quantum atoms (IQA) has been extended to MPn (n = 2, 3, 4) wave functions. This enables the extraction of chemical insight related to dynamic electron correlation. The large computational expense of the IQA-MPn approach is compensated by the advantages that IQA offers compared to older nontopological energy decomposition schemes. This expense is problematic in the construction of a machine learning training set to create kriging models for topological atoms. However, the algorithm presented here markedly accelerates the calculation of atomically partitioned electron correlation energies. Then again, the algorithm cannot calculate pairwise interatomic energies because it applies analytical integrals over whole space (rather than over atomic volumes). However, these pairwise energies are not needed in the quantum topological force field FFLUX, which only uses the energy of an atom interacting with all remaining atoms of the system that it is part of. Thus, it is now feasible to generate accurate and sizeable training sets at MPn level of theory. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Mark A. Vincent
- Manchester Institute of BiotechnologyThe University of ManchesterManchesterM1 7DNUK
- School of ChemistryThe University of ManchesterManchesterM13 9PLUK
| | - Arnaldo F. Silva
- Manchester Institute of BiotechnologyThe University of ManchesterManchesterM1 7DNUK
- School of ChemistryThe University of ManchesterManchesterM13 9PLUK
| | - Paul L. A. Popelier
- Manchester Institute of BiotechnologyThe University of ManchesterManchesterM1 7DNUK
- School of ChemistryThe University of ManchesterManchesterM13 9PLUK
| |
Collapse
|
21
|
Chen T, Manz TA. A collection of forcefield precursors for metal-organic frameworks. RSC Adv 2019; 9:36492-36507. [PMID: 35539031 PMCID: PMC9075174 DOI: 10.1039/c9ra07327b] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 10/25/2019] [Indexed: 12/14/2022] Open
Abstract
A host of important performance properties for metal-organic frameworks (MOFs) and other complex materials can be calculated by modeling statistical ensembles. The principle challenge is to develop accurate and computationally efficient interaction models for these simulations. Two major approaches are (i) ab initio molecular dynamics in which the interaction model is provided by an exchange-correlation theory (e.g., DFT + dispersion functional) and (ii) molecular mechanics in which the interaction model is a parameterized classical force field. The first approach requires further development to improve computational speed. The second approach requires further development to automate accurate forcefield parameterization. Because of the extreme chemical diversity across thousands of MOF structures, this problem is still mostly unsolved today. For example, here we show structures in the 2014 CoRE MOF database contain more than 8 thousand different atom types based on first and second neighbors. Our results showed that atom types based on both first and second neighbors adequately capture the chemical environment, but atom types based on only first neighbors do not. For 3056 MOFs, we used density functional theory (DFT) followed by DDEC6 atomic population analysis to extract a host of important forcefield precursors: partial atomic charges; atom-in-material (AIM) C6, C8, and C10 dispersion coefficients; AIM dipole and quadrupole moments; various AIM polarizabilities; quantum Drude oscillator parameters; AIM electron cloud parameters; etc. Electrostatic parameters were validated through comparisons to the DFT-computed electrostatic potential. These forcefield precursors should find widespread applications to developing MOF force fields.
Collapse
Affiliation(s)
- Taoyi Chen
- Department of Chemical & Materials Engineering, New Mexico State University Las Cruces New Mexico 88003-8001 USA
| | - Thomas A Manz
- Department of Chemical & Materials Engineering, New Mexico State University Las Cruces New Mexico 88003-8001 USA
| |
Collapse
|
22
|
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.
Collapse
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
| |
Collapse
|
23
|
|
24
|
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] [Grants] [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
Polarizabilities and London dispersion forces are important to many chemical processes. Force fields for classical atomistic simulations can be constructed using atom-in-material polarizabilities and C n (n = 6, 8, 9, 10…) dispersion coefficients. This article addresses the key question of how to efficiently assign these parameters to constituent atoms in a material so that properties of the whole material are better reproduced. We develop a new set of scaling laws and computational algorithms (called MCLF) to do this in an accurate and computationally efficient manner across diverse material types. We introduce a conduction limit upper bound and m-scaling to describe the different behaviors of surface and buried atoms. We validate MCLF by comparing results to high-level benchmarks for isolated neutral and charged atoms, diverse diatomic molecules, various polyatomic molecules (e.g., polyacenes, fullerenes, and small organic and inorganic molecules), and dense solids (including metallic, covalent, and ionic). We also present results for the HIV reverse transcriptase enzyme complexed with an inhibitor molecule. MCLF provides the non-directionally screened polarizabilities required to construct force fields, the directionally-screened static polarizability tensor components and eigenvalues, and environmentally screened C6 coefficients. Overall, MCLF has improved accuracy compared to the TS-SCS method. For TS-SCS, we compared charge partitioning methods and show DDEC6 partitioning yields more accurate results than Hirshfeld partitioning. MCLF also gives approximations for C8, C9, and C10 dispersion coefficients and quantum Drude oscillator parameters. This method should find widespread applications to parameterize classical force fields and density functional theory (DFT) + dispersion methods.
Collapse
Affiliation(s)
- Thomas A Manz
- Department of Chemical & Materials Engineering, New Mexico State University Las Cruces New Mexico 88003-8001 USA
| | - Taoyi Chen
- Department of Chemical & Materials Engineering, New Mexico State University Las Cruces New Mexico 88003-8001 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 New Mexico 88003-8001 USA
| | - Benjamin Fiszbein
- Department of Chemical & Materials Engineering, New Mexico State University Las Cruces New Mexico 88003-8001 USA
| |
Collapse
|
25
|
Krämer A, Pickard FC, Huang J, Venable RM, Simmonett AC, Reith D, Kirschner KN, Pastor RW, Brooks BR. Interactions of Water and Alkanes: Modifying Additive Force Fields to Account for Polarization Effects. J Chem Theory Comput 2019; 15:3854-3867. [PMID: 31002505 DOI: 10.1021/acs.jctc.9b00016] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Atomistic biomolecular simulations predominantly utilize additive force fields (FF), where the electrostatic potential is modeled by fixed point charges. Among other consequences, the lack of polarizability in these models undermines the balance of hydrophilic/hydrophobic nonbonded interactions. Simulations of water/alkane systems using the TIP3P water model and CHARMM36 parameters reveal a 1 kcal/mol overestimate of the experimental transfer free energy of water to hexadecane; more recent optimized water models (SPC/E, TIP4P/2005, TIP4P-Ew, TIP3P-FB, TIP4P-FB, OPC, TIP4P-D) overestimate this transfer free energy by approximately 2 kcal/mol. In contrast, the polarizable SWM4-NDP and SWM6 water models reproduce experimental values to within statistical error. As an alternative to explicitly modeling polarizability, this paper develops an efficient automated workflow to optimize pair-specific Lennard-Jones parameters within an additive FF. Water/hexadecane is used as a prototype and the free energy of water transfer to hexadecane as a target. The optimized model yields quantitative agreement with the experimental transfer free energy and improves the water/hexadecane interfacial tension by 6%. Simulations of five different lipid bilayers show a strong increase of water permeabilities compared to the unmodified CHARMM36 lipid FF which consistently improves match with experiment: the order-of-magnitude underestimate for monounsaturated bilayers is rectified and the factor of 2.8-4 underestimate for saturated bilayers is turned into a factor of 1.5-3 overestimate. While agreement with experiment is decreased for the diffusion constant of water in hexadecane, alkane transfer free energies, and the bilayers' area per lipid, the method provides a permeant-specific route to achieve a wide range of heterogeneous observables via rapidly optimized pairwise parameters.
Collapse
Affiliation(s)
- Andreas Krämer
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States.,Institute of Technology, Resource and Energy-Efficient Engineering , Bonn-Rhein-Sieg University of Applied Sciences , Grantham-Allee 20 , 53757 Sankt Augustin , Germany
| | - Frank C Pickard
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Jing Huang
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States.,School of Life Sciences , Westlake University , 18 Shilongshan Road , Hangzhou 310024 , Zhejiang China.,Department of Pharmaceutical Science, School of Pharmacy , University of Maryland , 20 Penn Street , Baltimore , Maryland 21201 , United States
| | - Richard M Venable
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Andrew C Simmonett
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Dirk Reith
- Institute of Technology, Resource and Energy-Efficient Engineering , Bonn-Rhein-Sieg University of Applied Sciences , Grantham-Allee 20 , 53757 Sankt Augustin , Germany
| | - Karl N Kirschner
- Institute of Technology, Resource and Energy-Efficient Engineering , Bonn-Rhein-Sieg University of Applied Sciences , Grantham-Allee 20 , 53757 Sankt Augustin , Germany
| | - Richard W Pastor
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute , National Institutes of Health , Bethesda , Maryland 20892 , United States
| |
Collapse
|