1
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Lesniewski MC, Noid WG. Insight into the Density-Dependence of Pair Potentials for Predictive Coarse-Grained Models. J Phys Chem B 2024; 128:1298-1316. [PMID: 38271676 DOI: 10.1021/acs.jpcb.3c06890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
We investigate the temperature- and density-dependence of effective pair potentials for 1-site coarse-grained (CG) models of two industrial solvents, 1,4-dioxane and tetrahydrofuran. We observe that the calculated pair potentials are much more sensitive to density than to temperature. The generalized-Yvon-Born-Green framework reveals that this striking density-dependence reflects corresponding variations in the many-body correlations that determine the environment-mediated indirect contribution to the pair mean force. Moreover, we demonstrate, perhaps surprisingly, that this density-dependence is not important for accurately modeling the intermolecular structure. Accordingly, we adopt a density-independent interaction potential and transfer the density-dependence of the calculated pair potentials into a configuration-independent volume potential. Furthermore, we develop a single global potential that accurately models the intermolecular structure and pressure-volume equation of state across a very wide range of liquid state points. Consequently, this work provides fundamental insight into the density-dependence of effective pair potentials and also provides a significant step toward developing predictive CG models for efficiently modeling industrial solvents.
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Affiliation(s)
- Maria C Lesniewski
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - W G Noid
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
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2
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Chen Y, Krämer A, Charron NE, Husic BE, Clementi C, Noé F. Machine learning implicit solvation for molecular dynamics. J Chem Phys 2021; 155:084101. [PMID: 34470360 DOI: 10.1063/5.0059915] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scales for large system sizes is to incorporate the effect of the solvent in a mean-field fashion with implicit solvent models. However, a challenge with existing implicit solvent models is that they often lack accuracy or certain physical properties compared to explicit solvent models as the many-body effects of the neglected solvent molecules are difficult to model as a mean field. Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data. Following the previous ML-CG models CGnet and CGSchnet, we introduce ISSNet, a graph neural network, to model the implicit solvent potential of mean force. ISSNet can learn from explicit solvent simulation data and be readily applied to molecular dynamics simulations. We compare the solute conformational distributions under different solvation treatments for two peptide systems. The results indicate that ISSNet models can outperform widely used generalized Born and surface area models in reproducing the thermodynamics of small protein systems with respect to explicit solvent. The success of this novel method demonstrates the potential benefit of applying machine learning methods in accurate modeling of solvent effects for in silico research and biomedical applications.
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Affiliation(s)
- Yaoyi Chen
- Department of Mathematics and Computer Science, Freie Universität, Berlin, Germany
| | - Andreas Krämer
- Department of Mathematics and Computer Science, Freie Universität, Berlin, Germany
| | | | - Brooke E Husic
- Department of Mathematics and Computer Science, Freie Universität, Berlin, Germany
| | - Cecilia Clementi
- Department of Physics, Rice University, Houston, Texas 77005, USA
| | - Frank Noé
- Department of Mathematics and Computer Science, Freie Universität, Berlin, Germany
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3
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Lebold KM, Noid WG. Dual-potential approach for coarse-grained implicit solvent models with accurate, internally consistent energetics and predictive transferability. J Chem Phys 2019; 151:164113. [PMID: 31675902 DOI: 10.1063/1.5125246] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
The dual-potential approach promises coarse-grained (CG) models that accurately reproduce both structural and energetic properties, while simultaneously providing predictive estimates for the temperature-dependence of the effective CG potentials. In this work, we examine the dual-potential approach for implicit solvent CG models that reflect large entropic effects from the eliminated solvent. Specifically, we construct implicit solvent models at various resolutions, R, by retaining a fraction 0.10 ≤ R ≤ 0.95 of the molecules from a simple fluid of Lennard-Jones spheres. We consider the dual-potential approach in both the constant volume and constant pressure ensembles across a relatively wide range of temperatures. We approximate the many-body potential of mean force for the remaining solutes with pair and volume potentials, which we determine via multiscale coarse-graining and self-consistent pressure-matching, respectively. Interestingly, with increasing temperature, the pair potentials appear increasingly attractive, while the volume potentials become increasingly repulsive. The dual-potential approach not only reproduces the atomic energetics but also quite accurately predicts this temperature-dependence. We also derive an exact relationship between the thermodynamic specific heat of an atomic model and the energetic fluctuations that are observable at the CG resolution. With this generalized fluctuation relationship, the approximate CG models quite accurately reproduce the thermodynamic specific heat of the underlying atomic model.
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Affiliation(s)
- Kathryn M Lebold
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
| | - W G Noid
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
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4
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Durumeric AEP, Voth GA. Adversarial-residual-coarse-graining: Applying machine learning theory to systematic molecular coarse-graining. J Chem Phys 2019; 151:124110. [PMID: 31575201 DOI: 10.1063/1.5097559] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
We utilize connections between molecular coarse-graining (CG) approaches and implicit generative models in machine learning to describe a new framework for systematic molecular CG. Focus is placed on the formalism encompassing generative adversarial networks. The resulting method enables a variety of model parameterization strategies, some of which show similarity to previous CG methods. We demonstrate that the resulting framework can rigorously parameterize CG models containing CG sites with no prescribed connection to the reference atomistic system (termed virtual sites); however, this advantage is offset by the lack of a closed-form expression for the CG Hamiltonian at the resolution obtained after integration over the virtual CG sites. Computational examples are provided for cases in which these methods ideally return identical parameters as relative entropy minimization CG but where traditional relative entropy minimization CG optimization equations are not applicable.
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Affiliation(s)
- Aleksander E P Durumeric
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, and Computation Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - Gregory A Voth
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, and Computation Institute, The University of Chicago, Chicago, Illinois 60637, USA
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5
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Theory and simulations for RNA folding in mixtures of monovalent and divalent cations. Proc Natl Acad Sci U S A 2019; 116:21022-21030. [PMID: 31570624 DOI: 10.1073/pnas.1911632116] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
RNA molecules cannot fold in the absence of counterions. Experiments are typically performed in the presence of monovalent and divalent cations. How to treat the impact of a solution containing a mixture of both ion types on RNA folding has remained a challenging problem for decades. By exploiting the large concentration difference between divalent and monovalent ions used in experiments, we develop a theory based on the reference interaction site model (RISM), which allows us to treat divalent cations explicitly while keeping the implicit screening effect due to monovalent ions. Our theory captures both the inner shell and outer shell coordination of divalent cations to phosphate groups, which we demonstrate is crucial for an accurate calculation of RNA folding thermodynamics. The RISM theory for ion-phosphate interactions when combined with simulations based on a transferable coarse-grained model allows us to predict accurately the folding of several RNA molecules in a mixture containing monovalent and divalent ions. The calculated folding free energies and ion-preferential coefficients for RNA molecules (pseudoknots, a fragment of the rRNA, and the aptamer domain of the adenine riboswitch) are in excellent agreement with experiments over a wide range of monovalent and divalent ion concentrations. Because the theory is general, it can be readily used to investigate ion and sequence effects on DNA properties.
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6
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Recent Progress towards Chemically-Specific Coarse-Grained Simulation Models with Consistent Dynamical Properties. COMPUTATION 2019. [DOI: 10.3390/computation7030042] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Coarse-grained (CG) models can provide computationally efficient and conceptually simple characterizations of soft matter systems. While generic models probe the underlying physics governing an entire family of free-energy landscapes, bottom-up CG models are systematically constructed from a higher-resolution model to retain a high level of chemical specificity. The removal of degrees of freedom from the system modifies the relationship between the relative time scales of distinct dynamical processes through both a loss of friction and a “smoothing” of the free-energy landscape. While these effects typically result in faster dynamics, decreasing the computational expense of the model, they also obscure the connection to the true dynamics of the system. The lack of consistent dynamics is a serious limitation for CG models, which not only prevents quantitatively accurate predictions of dynamical observables but can also lead to qualitatively incorrect descriptions of the characteristic dynamical processes. With many methods available for optimizing the structural and thermodynamic properties of chemically-specific CG models, recent years have seen a stark increase in investigations addressing the accurate description of dynamical properties generated from CG simulations. In this review, we present an overview of these efforts, ranging from bottom-up parameterizations of generalized Langevin equations to refinements of the CG force field based on a Markov state modeling framework. We aim to make connections between seemingly disparate approaches, while laying out some of the major challenges as well as potential directions for future efforts.
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7
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Jin J, Pak AJ, Voth GA. Understanding Missing Entropy in Coarse-Grained Systems: Addressing Issues of Representability and Transferability. J Phys Chem Lett 2019; 10:4549-4557. [PMID: 31319036 PMCID: PMC6782054 DOI: 10.1021/acs.jpclett.9b01228] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Coarse-grained (CG) models facilitate efficient simulation of complex systems by integrating out the atomic, or fine-grained (FG), degrees of freedom. Systematically derived CG models from FG simulations often attempt to approximate the CG potential of mean force (PMF), an inherently multidimensional and many-body quantity, using additive pairwise contributions. However, they currently lack fundamental principles that enable their extensible use across different thermodynamic state points, i.e., transferability. In this work, we investigate the explicit energy-entropy decomposition of the CG PMF as a means to construct transferable CG models. In particular, despite its high-dimensional nature, we find for liquid systems that the entropic component to the CG PMF can similarly be represented using additive pairwise contributions, which we show is highly coupled to the CG configurational entropy. This approach formally connects the missing entropy that is lost due to the CG representation, i.e., translational, rotational, and vibrational modes associated with the missing degrees of freedom, to the CG entropy. By design, the present framework imparts transferable CG interactions across different temperatures due to the explicit definition of an additive entropic contribution. Furthermore, we demonstrate that transferability across composition state points, such as between bulk liquids and their mixtures, is also achieved by designing combining rules to approximate cross-interactions from bulk CG PMFs. Using the predicted CG model for liquid mixtures, structural correlations of the fitted CG model were found to corroborate a high-fidelity combining rule. Our findings elucidate the physical nature and compact representation of CG entropy and suggest a new approach for overcoming the transferability problem. We expect that this approach will further extend the current view of CG modeling into predictive multiscale modeling.
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8
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Lebold KM, Noid WG. Systematic study of temperature and density variations in effective potentials for coarse-grained models of molecular liquids. J Chem Phys 2019; 150:014104. [DOI: 10.1063/1.5050509] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Affiliation(s)
- Kathryn M. Lebold
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
| | - W. G. Noid
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
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9
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Rudzinski JF, Lu K, Milner ST, Maranas JK, Noid WG. Extended Ensemble Approach to Transferable Potentials for Low-Resolution Coarse-Grained Models of Ionomers. J Chem Theory Comput 2017; 13:2185-2201. [DOI: 10.1021/acs.jctc.6b01160] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Joseph F. Rudzinski
- Department
of Chemistry and ‡Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Keran Lu
- Department
of Chemistry and ‡Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Scott T. Milner
- Department
of Chemistry and ‡Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Janna K. Maranas
- Department
of Chemistry and ‡Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - William G. Noid
- Department
of Chemistry and ‡Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
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10
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Cao F, Deetz JD, Sun H. Free Energy-Based Coarse-Grained Force Field for Binary Mixtures of Hydrocarbons, Nitrogen, Oxygen, and Carbon Dioxide. J Chem Inf Model 2017; 57:50-59. [DOI: 10.1021/acs.jcim.6b00685] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Fenglei Cao
- School
of Chemistry and Chemical Engineering and Key Laboratory of Scientific
and Engineering Computing of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Joshua D. Deetz
- School
of Chemistry and Chemical Engineering and Key Laboratory of Scientific
and Engineering Computing of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Huai Sun
- School
of Chemistry and Chemical Engineering and Key Laboratory of Scientific
and Engineering Computing of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
- State Key Laboratory of Inorganic Synthesis & Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130012, China
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11
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Sauter J, Grafmüller A. Procedure for Transferable Coarse-Grained Models of Aqueous Polysaccharides. J Chem Theory Comput 2016; 13:223-236. [DOI: 10.1021/acs.jctc.6b00613] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Jörg Sauter
- Theory and Bio-Systems, Max Planck Institute of Colloids and Interfaces, Potsdam 14424, Germany
| | - Andrea Grafmüller
- Theory and Bio-Systems, Max Planck Institute of Colloids and Interfaces, Potsdam 14424, Germany
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12
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Ions interacting in solution: Moving from intrinsic to collective properties. Curr Opin Colloid Interface Sci 2016. [DOI: 10.1016/j.cocis.2016.05.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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13
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Cao Z, Voth GA. The multiscale coarse-graining method. XI. Accurate interactions based on the centers of charge of coarse-grained sites. J Chem Phys 2016; 143:243116. [PMID: 26723601 DOI: 10.1063/1.4933249] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
It is essential to be able to systematically construct coarse-grained (CG) models that can efficiently and accurately reproduce key properties of higher-resolution models such as all-atom. To fulfill this goal, a mapping operator is needed to transform the higher-resolution configuration to a CG configuration. Certain mapping operators, however, may lose information related to the underlying electrostatic properties. In this paper, a new mapping operator based on the centers of charge of CG sites is proposed to address this issue. Four example systems are chosen to demonstrate this concept. Within the multiscale coarse-graining framework, CG models that use this mapping operator are found to better reproduce the structural correlations of atomistic models. The present work also demonstrates the flexibility of the mapping operator and the robustness of the force matching method. For instance, important functional groups can be isolated and emphasized in the CG model.
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Affiliation(s)
- Zhen Cao
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, and Computation Institute, The University of Chicago, 5735 S Ellis Ave., Chicago, Illinois 60637, USA
| | - Gregory A Voth
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, and Computation Institute, The University of Chicago, 5735 S Ellis Ave., Chicago, Illinois 60637, USA
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14
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Vila Verde A, Santer M, Lipowsky R. Solvent-shared pairs of densely charged ions induce intense but short-range supra-additive slowdown of water rotation. Phys Chem Chem Phys 2016; 18:1918-30. [DOI: 10.1039/c5cp05726d] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Magnesium and sulfate ions in solvent-shared (SIP) ion pair configuration supra-additively slowdown the rotation of water molecules between them; water molecules around solvent-separated (2SIP) ion pairs show only additive slowdown.
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Affiliation(s)
- Ana Vila Verde
- Max Planck Institute of Colloids and Interfaces
- Theory and Bio-Systems Department
- 14424 Potsdam
- Germany
| | - Mark Santer
- Max Planck Institute of Colloids and Interfaces
- Theory and Bio-Systems Department
- 14424 Potsdam
- Germany
| | - Reinhard Lipowsky
- Max Planck Institute of Colloids and Interfaces
- Theory and Bio-Systems Department
- 14424 Potsdam
- Germany
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15
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Cao F, Sun H. Transferability and Nonbond Functional Form of Coarse Grained Force Field – Tested on Linear Alkanes. J Chem Theory Comput 2015; 11:4760-9. [DOI: 10.1021/acs.jctc.5b00573] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Fenglei Cao
- School
of Chemistry and Chemical Engineering and Key Laboratory of Scientific
and Engineering Computing of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Huai Sun
- School
of Chemistry and Chemical Engineering and Key Laboratory of Scientific
and Engineering Computing of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
- State Key Laboratory of Inorganic Synthesis & Preparative Chemistry, College of Chemistry, Jilin University, Changchun, Jilin 130012, China
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16
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Wagner JW, Dama JF, Voth GA. Predicting the Sensitivity of Multiscale Coarse-Grained Models to their Underlying Fine-Grained Model Parameters. J Chem Theory Comput 2015; 11:3547-60. [DOI: 10.1021/acs.jctc.5b00180] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jacob W. Wagner
- Department of Chemistry,
James Franck Institute, Institute for Biophysical Dynamics, and Computation
Institute, University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States
| | - James F. Dama
- Department of Chemistry,
James Franck Institute, Institute for Biophysical Dynamics, and Computation
Institute, University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States
| | - Gregory A. Voth
- Department of Chemistry,
James Franck Institute, Institute for Biophysical Dynamics, and Computation
Institute, University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States
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17
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Kleinjung J, Fraternali F. Design and application of implicit solvent models in biomolecular simulations. Curr Opin Struct Biol 2014; 25:126-34. [PMID: 24841242 PMCID: PMC4045398 DOI: 10.1016/j.sbi.2014.04.003] [Citation(s) in RCA: 106] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Revised: 04/07/2014] [Accepted: 04/09/2014] [Indexed: 11/23/2022]
Abstract
Implicit solvent replaces explicit water by a potential of mean force. Popular models are SASA, VOL and Generalized Born. Implicit solvent is used in MD, protein modelling, folding, design, prediction and drug screening. Large-scale simulations allow for parametrisation via force matching. Application to nucleic acids and membranes is challenging.
We review implicit solvent models and their parametrisation by introducing the concepts and recent devlopments of the most popular models with a focus on parametrisation via force matching. An overview of recent applications of the solvation energy term in protein dynamics, modelling, design and prediction is given to illustrate the usability and versatility of implicit solvation in reproducing the physical behaviour of biomolecular systems. Limitations of implicit modes are discussed through the example of more challenging systems like nucleic acids and membranes.
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Affiliation(s)
- Jens Kleinjung
- Division of Mathematical Biology, MRC National Institute for Medical Research, The Ridgeway, London NW7 1AA, United Kingdom
| | - Franca Fraternali
- Randall Division of Cell and Molecular Biophysics, King's College London, New Hunt's House, London SE1 1UL, United Kingdom.
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18
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Ingólfsson HI, Lopez CA, Uusitalo JJ, de Jong DH, Gopal SM, Periole X, Marrink SJ. The power of coarse graining in biomolecular simulations. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2014; 4:225-248. [PMID: 25309628 PMCID: PMC4171755 DOI: 10.1002/wcms.1169] [Citation(s) in RCA: 325] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Computational modeling of biological systems is challenging because of the multitude of spatial and temporal scales involved. Replacing atomistic detail with lower resolution, coarse grained (CG), beads has opened the way to simulate large-scale biomolecular processes on time scales inaccessible to all-atom models. We provide an overview of some of the more popular CG models used in biomolecular applications to date, focusing on models that retain chemical specificity. A few state-of-the-art examples of protein folding, membrane protein gating and self-assembly, DNA hybridization, and modeling of carbohydrate fibers are used to illustrate the power and diversity of current CG modeling.
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Affiliation(s)
- Helgi I Ingólfsson
- Groningen Biomolecular Sciences and Biotechnology Institute & Zernike Institute for Advanced Materials, University of GroningenGroningen, The Netherlands
| | - Cesar A Lopez
- Groningen Biomolecular Sciences and Biotechnology Institute & Zernike Institute for Advanced Materials, University of GroningenGroningen, The Netherlands
| | - Jaakko J Uusitalo
- Groningen Biomolecular Sciences and Biotechnology Institute & Zernike Institute for Advanced Materials, University of GroningenGroningen, The Netherlands
| | - Djurre H de Jong
- Groningen Biomolecular Sciences and Biotechnology Institute & Zernike Institute for Advanced Materials, University of GroningenGroningen, The Netherlands
| | - Srinivasa M Gopal
- Groningen Biomolecular Sciences and Biotechnology Institute & Zernike Institute for Advanced Materials, University of GroningenGroningen, The Netherlands
| | - Xavier Periole
- Groningen Biomolecular Sciences and Biotechnology Institute & Zernike Institute for Advanced Materials, University of GroningenGroningen, The Netherlands
| | - Siewert J Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute & Zernike Institute for Advanced Materials, University of GroningenGroningen, The Netherlands
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19
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Bottaro S, Lindorff-Larsen K, Best RB. Variational Optimization of an All-Atom Implicit Solvent Force Field to Match Explicit Solvent Simulation Data. J Chem Theory Comput 2013; 9:5641-5652. [PMID: 24748852 DOI: 10.1021/ct400730n] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The development of accurate implicit solvation models with low computational cost is essential for addressing many large-scale biophysical problems. Here, we present an efficient solvation term based on a Gaussian solvent-exclusion model (EEF1) for simulations of proteins in aqueous environment, with the primary aim of having a good overlap with explicit solvent simulations, particularly for unfolded and disordered states - as would be needed for multiscale applications. In order to achieve this, we have used a recently proposed coarse-graining procedure based on minimization of an entropy-related objective function to train the model to reproduce the equilibrium distribution obtained from explicit water simulations. Via this methodology, we have optimized both a charge screening parameter and a backbone torsion term against explicit solvent simulations of an α-helical and a β-stranded peptide. The performance of the resulting effective energy function, termed EEF1-SB, is tested with respect to the properties of folded proteins, the folding of small peptides or fast-folding proteins, and NMR data for intrinsically disordered proteins. The results show that EEF1-SB provides a reasonable description of a wide range of systems, but its key advantage over other methods tested is that it captures very well the structure and dimension of disordered or weakly structured peptides. EEF1-SB is thus a computationally inexpensive (~ 10 times faster than Generalized-Born methods) and transferable approximation for treating solvent effects.
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Affiliation(s)
- Sandro Bottaro
- Department of Biology, University of Copenhagen, Copenhagen, Denmark ; Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, U.S.A. ; SISSA-Scuola Internazionale Superiore di Studi Avanzati,Trieste, Italy
| | - Kresten Lindorff-Larsen
- Department of Biology, University of Copenhagen, Copenhagen, Denmark ; Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, U.S.A
| | - Robert B Best
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom ; Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, U.S.A
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20
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Vila Verde A, Lipowsky R. Cooperative Slowdown of Water Rotation near Densely Charged Ions Is Intense but Short-Ranged. J Phys Chem B 2013; 117:10556-66. [DOI: 10.1021/jp4059802] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Ana Vila Verde
- Theory and Bio-Systems Department, Max Planck Institute of Colloids and Interfaces, Wissenschaftspark Golm, 14424 Potsdam, Germany
| | - Reinhard Lipowsky
- Theory and Bio-Systems Department, Max Planck Institute of Colloids and Interfaces, Wissenschaftspark Golm, 14424 Potsdam, Germany
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