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Oliveira MP, Hünenberger PH. Influence of the Lennard-Jones Combination Rules on the Simulated Properties of Organic Liquids at Optimal Force-Field Parametrization. J Chem Theory Comput 2023; 19:2048-2063. [PMID: 36920838 PMCID: PMC10100539 DOI: 10.1021/acs.jctc.2c01170] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
We recently introduced the CombiFF scheme [Oliveira et al., J. Chem. Theory Comput. 2020, 16, 7525], an approach for the automated refinement of force-field parameters against experimental condensed-phase data for large compound families. Using this scheme, once the time-consuming task of target-data selection and curation has been performed, the force-field optimization itself is both straightforward and fast. As a result, CombiFF provides an ideal framework for evaluating the influence of functional-form decisions on the accuracy of a force field at an optimal level of parametrization. We already used this approach to assess the effect of using an all-atom representation compared to united-atom representations in the force field [Oliveira et al., J. Chem. Theory Comput. 2022, 18, 6757]. Here, CombiFF is applied to assess the effect of three Lennard-Jones combination rules, geometric-mean (GM), Lorentz-Berthelot (LB), or Waldman-Hagler (WH), on the simulated properties of organic liquids. The comparison is performed in terms of the experimental liquid density ρliq, vaporization enthalpy ΔHvap, surface-tension coefficient γ, static relative dielectric permittivity ϵ, and self-diffusion coefficient D. The calibrations of the three force-field variants are carried out independently against 2044 experimental values for ρliq, and ΔHvap concerning 1516 compounds. The resulting root-mean-square deviations from experiment are 30.0, 26.9, and 36.7 kg m-3 for ρliq and 2.8, 2.8, and 2.9 kJ mol-1 for ΔHvap, when applying the GM, LB, and WH combination rules, respectively. In terms of these (and the other) properties, the three combination rules perform comparatively well, with the GM and LB results being more similar to each other and slightly more accurate compared to experiment. In contrast, the use of distinct combination rules for the parameter calibration and property calculation leads to much larger errors.
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Affiliation(s)
- Marina P Oliveira
- Laboratorium für Physikalische Chemie, ETH Zürich, CH-8093 Zürich, Switzerland
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Computational Studies of Aflatoxin B 1 (AFB 1): A Review. Toxins (Basel) 2023; 15:toxins15020135. [PMID: 36828449 PMCID: PMC9967988 DOI: 10.3390/toxins15020135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/25/2023] [Accepted: 02/03/2023] [Indexed: 02/11/2023] Open
Abstract
Aflatoxin B1 (AFB1) exhibits the most potent mutagenic and carcinogenic activity among aflatoxins. For this reason, AFB1 is recognized as a human group 1 carcinogen by the International Agency of Research on Cancer. Consequently, it is essential to determine its properties and behavior in different chemical systems. The chemical properties of AFB1 can be explored using computational chemistry, which has been employed complementarily to experimental investigations. The present review includes in silico studies (semiempirical, Hartree-Fock, DFT, molecular docking, and molecular dynamics) conducted from the first computational study in 1974 to the present (2022). This work was performed, considering the following groups: (a) molecular properties of AFB1 (structural, energy, solvent effects, ground and the excited state, atomic charges, among others); (b) theoretical investigations of AFB1 (degradation, quantification, reactivity, among others); (c) molecular interactions with inorganic compounds (Ag+, Zn2+, and Mg2+); (d) molecular interactions with environmentally compounds (clays); and (e) molecular interactions with biological compounds (DNA, enzymes, cyclodextrins, glucans, among others). Accordingly, in this work, we provide to the stakeholder the knowledge of toxicity of types of AFB1-derivatives, the structure-activity relationships manifested by the bonds between AFB1 and DNA or proteins, and the types of strategies that have been employed to quantify, detect, and eliminate the AFB1 molecule.
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George A, Mondal S, Purnaprajna M, Athri P. Review of Electrostatic Force Calculation Methods and Their Acceleration in Molecular Dynamics Packages Using Graphics Processors. ACS OMEGA 2022; 7:32877-32896. [PMID: 36157750 PMCID: PMC9494432 DOI: 10.1021/acsomega.2c03189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
Molecular dynamics (MD) simulations probe the conformational repertoire of macromolecular systems using Newtonian dynamic equations. The time scales of MD simulations allow the exploration of biologically relevant phenomena and can elucidate spatial and temporal properties of the building blocks of life, such as deoxyribonucleic acid (DNA) and protein, across microsecond (μs) time scales using femtosecond (fs) time steps. A principal bottleneck toward extending MD calculations to larger time scales is the long-range electrostatic force measuring component of the naive nonbonded force computation algorithm, which scales with a complexity of (N, number of atoms). In this review, we present various methods to determine electrostatic interactions in often-used open-source MD packages as well as the implementation details that facilitate acceleration of the electrostatic interaction calculation.
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Affiliation(s)
- Anu George
- Department
of Computer Science and Engineering, Amrita
School of Engineering, Bengaluru 560035, Amrita Vishwa Vidyapeetham, India
| | | | - Madhura Purnaprajna
- Department
of Computer Science and Engineering, PES
University, Bengaluru 560085, India
| | - Prashanth Athri
- Department
of Computer Science and Engineering, Amrita
School of Engineering, Bengaluru 560035, Amrita Vishwa Vidyapeetham, India
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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
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Guzman HV, Junghans C, Kremer K, Stuehn T. Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes. Phys Rev E 2017; 96:053311. [PMID: 29347684 DOI: 10.1103/physreve.96.053311] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Indexed: 11/07/2022]
Abstract
Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach, the theoretical modeling and scaling laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. These two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.
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Affiliation(s)
- Horacio V Guzman
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - Christoph Junghans
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Kurt Kremer
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - Torsten Stuehn
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
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Chiricotto M, Sterpone F, Derreumaux P, Melchionna S. Multiscale simulation of molecular processes in cellular environments. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2016; 374:20160225. [PMID: 27698046 PMCID: PMC5052736 DOI: 10.1098/rsta.2016.0225] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/03/2016] [Indexed: 05/27/2023]
Abstract
We describe the recent advances in studying biological systems via multiscale simulations. Our scheme is based on a coarse-grained representation of the macromolecules and a mesoscopic description of the solvent. The dual technique handles particles, the aqueous solvent and their mutual exchange of forces resulting in a stable and accurate methodology allowing biosystems of unprecedented size to be simulated.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.
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Affiliation(s)
- Mara Chiricotto
- Laboratoire de Biochimie Théorique, UPR 9080 CNRS, Université Paris Diderot, Sorbonne Paris Cité, IBPC, 13 Rue Pierre et Marie Curie, 75005 Paris, France
| | - Fabio Sterpone
- Laboratoire de Biochimie Théorique, UPR 9080 CNRS, Université Paris Diderot, Sorbonne Paris Cité, IBPC, 13 Rue Pierre et Marie Curie, 75005 Paris, France
| | - Philippe Derreumaux
- Laboratoire de Biochimie Théorique, UPR 9080 CNRS, Université Paris Diderot, Sorbonne Paris Cité, IBPC, 13 Rue Pierre et Marie Curie, 75005 Paris, France
| | - Simone Melchionna
- Istituto Sistemi Complessi-ISC, Consiglio Nazionale delle Ricerche, P.za A. Moro 2, 00185 Rome, Italy
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Papaleo E. Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity. Front Mol Biosci 2015; 2:28. [PMID: 26075210 PMCID: PMC4445042 DOI: 10.3389/fmolb.2015.00028] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 05/08/2015] [Indexed: 12/11/2022] Open
Abstract
In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.
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Affiliation(s)
- Elena Papaleo
- Structural Biology and Nuclear Magnetic Resonance Laboratory, Department of Biology, University of Copenhagen Copenhagen, Denmark
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