1
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Wu Z, Zhou T. Structural Coarse-Graining via Multiobjective Optimization with Differentiable Simulation. J Chem Theory Comput 2024; 20:2605-2617. [PMID: 38483262 DOI: 10.1021/acs.jctc.3c01348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
In the realm of multiscale molecular simulations, structure-based coarse-graining is a prominent approach for creating efficient coarse-grained (CG) representations of soft matter systems, such as polymers. This involves optimizing CG interactions by matching static correlation functions of the corresponding degrees of freedom in all-atom (AA) models. Here, we present a versatile method, namely, differentiable coarse-graining (DiffCG), which combines multiobjective optimization and differentiable simulation. The DiffCG approach is capable of constructing robust CG models by iteratively optimizing the effective potentials to simultaneously match multiple target properties. We demonstrate our approach by concurrently optimizing bonded and nonbonded potentials of a CG model of polystyrene (PS) melts. The resulting CG-PS model effectively reproduces both the structural characteristics, such as the equilibrium probability distribution of microscopic degrees of freedom and the thermodynamic pressure of the AA counterpart. More importantly, leveraging the multiobjective optimization capability, we develop a precise and efficient CG model for PS melts that is transferable across a wide range of temperatures, i.e., from 400 to 600 K. It is achieved via optimizing a pairwise potential with nonlinear temperature dependence in the CG model to simultaneously match target data from AA-MD simulations at multiple thermodynamic states. The temperature transferable CG-PS model demonstrates its ability to accurately predict the radial distribution functions and density at different temperatures, including those that are not included in the target thermodynamic states. Our work opens up a promising route for developing accurate and transferable CG models of complex soft-matter systems through multiobjective optimization with differentiable simulation.
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Affiliation(s)
- Zhenghao Wu
- Department of Chemistry, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, P. R. China
| | - Tianhang Zhou
- College of Carbon Neutrality Future Technology, State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Beijing), Beijing 102249, P. R. China
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2
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Hooten M, Banerjee A, Dutt M. Multiscale, Multiresolution Coarse-Grained Model via a Hybrid Approach: Solvation, Structure, and Self-Assembly of Aromatic Tripeptides. J Chem Theory Comput 2024; 20:1689-1703. [PMID: 37931005 DOI: 10.1021/acs.jctc.3c00458] [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: 11/08/2023]
Abstract
Short aromatic peptides have been observed to assemble into diverse nanostructures, including fibers, tubes, and vesicles, using computational techniques. However, the computational studies have employed top-down coarse-grained (CG) models, which are unable to capture the assembly along with the conformation, packing, and organization of the peptides within the aggregates in a manner that is consistent with the all atom (AA) representation of the molecules. In this study, a hybrid structure- and force-based approach is adapted to develop a bottom-up CG force field of triphenylalanine using reference data from AA trajectories. This approach follows a flexible methodology to approximate the chemical complexity of the underlying AA representation with the chosen CG representation. Two CG models are developed with distinct representations of the aromatic side chains. The first uses a simple single-bead representation, while the second uses a three-bead representation to more accurately represent the planarity of the ring. The one-bead model yields nanorods, while the three-bead model results in nanospheres. The role of different chemical groups in the assembly of nanostructures is identified, along with the importance of steric effects on the packing of the peptides within assemblies.
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Affiliation(s)
- Mason Hooten
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Akash Banerjee
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Meenakshi Dutt
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
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3
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Petix CL, Fakhraei M, Kieslich CA, Howard MP. Surrogate Modeling of the Relative Entropy for Inverse Design Using Smolyak Sparse Grids. J Chem Theory Comput 2024; 20:1538-1546. [PMID: 37703086 DOI: 10.1021/acs.jctc.3c00651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Relative entropy minimization, a statistical-mechanics approach for finding potential energy functions that produce target structural ensembles, has proven to be a powerful strategy for the inverse design of nanoparticle self-assembly. For a given target structure, the gradient of the relative entropy with respect to the adjustable parameters of the potential energy function is computed by performing a simulation, and then these parameters are updated using iterative gradient-based optimization. Small parameter updates per iteration and many iterations can be required for numerical stability, but this incurs considerable computational expense because a new simulation must be performed to reevaluate the gradient at each iteration. Here, we investigate the use of surrogate modeling to decouple the process of minimizing the relative entropy from the computationally demanding process of determining its gradient. We approximate the relative-entropy gradient using Chebyshev polynomial interpolation on Smolyak sparse grids. Our approach potentially increases the robustness and computational efficiency of using the relative entropy for inverse design, primarily for physically informed potential energy functions that have a small number of adjustable parameters.
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Affiliation(s)
- C Levi Petix
- Department of Chemical Engineering, Auburn University, Auburn, Alabama 36849, United States
| | - Mohammadreza Fakhraei
- Department of Chemical Engineering, Auburn University, Auburn, Alabama 36849, United States
| | - Chris A Kieslich
- Department of Chemical Engineering, Auburn University, Auburn, Alabama 36849, United States
| | - Michael P Howard
- Department of Chemical Engineering, Auburn University, Auburn, Alabama 36849, United States
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4
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Pretti E, Shell MS. Mapping the configurational landscape and aggregation phase behavior of the tau protein fragment PHF6. Proc Natl Acad Sci U S A 2023; 120:e2309995120. [PMID: 37983502 PMCID: PMC10691331 DOI: 10.1073/pnas.2309995120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 10/17/2023] [Indexed: 11/22/2023] Open
Abstract
The PHF6 (Val-Gln-Ile-Val-Tyr-Lys) motif, found in all isoforms of the microtubule-associated protein tau, forms an integral part of ordered cores of amyloid fibrils formed in tauopathies and is thought to play a fundamental role in tau aggregation. Because PHF6 as an isolated hexapeptide assembles into ordered fibrils on its own, it is investigated as a minimal model for insight into the initial stages of aggregation of larger tau fragments. Even for this small peptide, however, the large length and time scales associated with fibrillization pose challenges for simulation studies of its dynamic assembly, equilibrium configurational landscape, and phase behavior. Here, we develop an accurate, bottom-up coarse-grained model of PHF6 for large-scale simulations of its aggregation, which we use to uncover molecular interactions and thermodynamic driving forces governing its assembly. The model, not trained on any explicit information about fibrillar structure, predicts coexistence of formed fibrils with monomers in solution, and we calculate a putative equilibrium phase diagram in concentration-temperature space. We also characterize the configurational and free energetic landscape of PHF6 oligomers. Importantly, we demonstrate with a model of heparin that this widely studied cofactor enhances the aggregation propensity of PHF6 by ordering monomers during nucleation and remaining associated with growing fibrils, consistent with experimentally characterized heparin-tau interactions. Overall, this effort provides detailed molecular insight into PHF6 aggregation thermodynamics and pathways and, furthermore, demonstrates the potential of modern multiscale modeling techniques to produce predictive models of amyloidogenic peptides simultaneously capturing sequence-specific effects and emergent aggregate structures.
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Affiliation(s)
- Evan Pretti
- Department of Chemical Engineering, University of California, Santa Barbara, CA93106-5080
| | - M. Scott Shell
- Department of Chemical Engineering, University of California, Santa Barbara, CA93106-5080
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5
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Stroh KS, Souza PCT, Monticelli L, Risselada HJ. CGCompiler: Automated Coarse-Grained Molecule Parametrization via Noise-Resistant Mixed-Variable Optimization. J Chem Theory Comput 2023; 19:8384-8400. [PMID: 37971301 PMCID: PMC10688431 DOI: 10.1021/acs.jctc.3c00637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/19/2023]
Abstract
Coarse-grained force fields (CG FFs) such as the Martini model entail a predefined, fixed set of Lennard-Jones parameters (building blocks) to model virtually all possible nonbonded interactions between chemically relevant molecules. Owing to its universality and transferability, the building-block coarse-grained approach has gained tremendous popularity over the past decade. The parametrization of molecules can be highly complex and often involves the selection and fine-tuning of a large number of parameters (e.g., bead types and bond lengths) to optimally match multiple relevant targets simultaneously. The parametrization of a molecule within the building-block CG approach is a mixed-variable optimization problem: the nonbonded interactions are discrete variables, whereas the bonded interactions are continuous variables. Here, we pioneer the utility of mixed-variable particle swarm optimization in automatically parametrizing molecules within the Martini 3 coarse-grained force field by matching both structural (e.g., RDFs) as well as thermodynamic data (phase-transition temperatures). For the sake of demonstration, we parametrize the linker of the lipid sphingomyelin. The important advantage of our approach is that both bonded and nonbonded interactions are simultaneously optimized while conserving the search efficiency of vector guided particle swarm optimization (PSO) methods over other metaheuristic search methods such as genetic algorithms. In addition, we explore noise-mitigation strategies in matching the phase-transition temperatures of lipid membranes, where nucleation and concomitant hysteresis introduce a dominant noise term within the objective function. We propose that noise-resistant mixed-variable PSO methods can both improve and automate parametrization of molecules within building-block CG FFs, such as Martini.
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Affiliation(s)
- Kai Steffen Stroh
- Department
of Physics, Technische Universität
Dortmund, 44227 Dortmund, Germany
- Institute
for Theoretical Physics, Georg-August University
Göttingen, 37077 Göttingen, Germany
| | - Paulo C. T. Souza
- Molecular
Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS and University of Lyon, 69367 Lyon, France
| | - Luca Monticelli
- Molecular
Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS and University of Lyon, 69367 Lyon, France
| | - Herre Jelger Risselada
- Department
of Physics, Technische Universität
Dortmund, 44227 Dortmund, Germany
- Institute
for Theoretical Physics, Georg-August University
Göttingen, 37077 Göttingen, Germany
- Leiden
Institute of Chemistry, Leiden University, 2333 CC Leiden, The Netherlands
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6
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Nadkarni I, Wu H, Aluru NR. Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement. J Chem Theory Comput 2023; 19:7358-7370. [PMID: 37791529 DOI: 10.1021/acs.jctc.3c00633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
We propose a data-driven framework for identifying coarse-grained (CG) Lennard-Jones (LJ) potential parameters in confined systems for simple liquids. Our approach involves the use of a Deep Neural Network (DNN) that is trained to approximate the solution of the Inverse Liquid State (ILST) problem for confined systems. The DNN model inherently incorporates essential physical characteristics specific to confined fluids, enabling an accurate prediction of inhomogeneity effects. By utilizing transfer learning, we predict single-site LJ potentials of simple multiatomic liquids confined in a slit-like channel, which effectively replicate both the fluid structure and molecular force of the target All-Atom (AA) system when the electrostatic interactions are not dominant. In addition, we showcase the synergy between the data-driven approach and the well-known Bottom-Up coarse-graining method utilizing Relative-Entropy (RE) Minimization. Through the sequential utilization of these two methods, the robustness of the iterative RE method is significantly augmented, leading to a remarkable enhancement in convergence.
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Affiliation(s)
- Ishan Nadkarni
- Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Haiyi Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Narayana R Aluru
- Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, United States
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7
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Durumeric AEP, Charron NE, Templeton C, Musil F, Bonneau K, Pasos-Trejo AS, Chen Y, Kelkar A, Noé F, Clementi C. Machine learned coarse-grained protein force-fields: Are we there yet? Curr Opin Struct Biol 2023; 79:102533. [PMID: 36731338 PMCID: PMC10023382 DOI: 10.1016/j.sbi.2023.102533] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/14/2022] [Accepted: 12/18/2022] [Indexed: 02/04/2023]
Abstract
The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning of force-fields at coarser resolutions is rapidly gaining relevance as an efficient way to represent the higher-body interactions needed in coarse-grained force-fields to compensate for the omitted degrees of freedom. Coarse-grained models are important for the study of systems at time and length scales exceeding those of atomistic simulations. However, the development of transferable coarse-grained models via machine learning still presents significant challenges. Here, we discuss recent developments in this field and current efforts to address the remaining challenges.
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Affiliation(s)
- Aleksander E P Durumeric
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany
| | - Nicholas E Charron
- Department of Physics and Astronomy, Rice University, 6100 Main Street, Houston, 77005, Texas, USA; Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany; Center for Theoretical Biological Physics, Rice University, 6100 Main Street, Houston, 77005, Texas, USA
| | - Clark Templeton
- Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany. https://twitter.com/pbrun03
| | - Félix Musil
- Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany. https://twitter.com/FelixMusil
| | - Klara Bonneau
- Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany
| | - Aldo S Pasos-Trejo
- Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany. https://twitter.com/sayeg84
| | - Yaoyi Chen
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany. https://twitter.com/hello_yaoyi
| | - Atharva Kelkar
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany
| | - Frank Noé
- Microsoft Research AI4Science, Karl-Liebknecht Str. 32, Berlin, 10178, Berlin, Germany; Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany; Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany; Department of Chemistry, Rice University, 6100 Main Street, Houston, 77005, Texas, USA. https://twitter.com/FrankNoeBerlin
| | - Cecilia Clementi
- Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany; Center for Theoretical Biological Physics, Rice University, 6100 Main Street, Houston, 77005, Texas, USA; Department of Chemistry, Rice University, 6100 Main Street, Houston, 77005, Texas, USA; Department of Physics and Astronomy, Rice University, 6100 Main Street, Houston, 77005, Texas, USA.
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8
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Thaler S, Stupp M, Zavadlav J. Deep coarse-grained potentials via relative entropy minimization. J Chem Phys 2022; 157:244103. [PMID: 36586977 DOI: 10.1063/5.0124538] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Neural network (NN) potentials are a natural choice for coarse-grained (CG) models. Their many-body capacity allows highly accurate approximations of the potential of mean force, promising CG simulations of unprecedented accuracy. CG NN potentials trained bottom-up via force matching (FM), however, suffer from finite data effects: They rely on prior potentials for physically sound predictions outside the training data domain, and the corresponding free energy surface is sensitive to errors in the transition regions. The standard alternative to FM for classical potentials is relative entropy (RE) minimization, which has not yet been applied to NN potentials. In this work, we demonstrate, for benchmark problems of liquid water and alanine dipeptide, that RE training is more data efficient, due to accessing the CG distribution during training, resulting in improved free energy surfaces and reduced sensitivity to prior potentials. In addition, RE learns to correct time integration errors, allowing larger time steps in CG molecular dynamics simulation, while maintaining accuracy. Thus, our findings support the use of training objectives beyond FM, as a promising direction for improving CG NN potential's accuracy and reliability.
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Affiliation(s)
- Stephan Thaler
- Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany
| | - Maximilian Stupp
- Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany
| | - Julija Zavadlav
- Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany
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9
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Cai X, Han W. Development of a Hybrid-Resolution Force Field for Peptide Self-Assembly Simulations: Optimizing Peptide-Peptide and Peptide-Solvent Interactions. J Chem Inf Model 2022; 62:2744-2760. [PMID: 35561002 DOI: 10.1021/acs.jcim.2c00066] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Atomic descriptions of peptide self-assembly are crucial to an understanding of disease-related peptide aggregation and the design of peptide-assembled materials. Obtaining these descriptions through computer simulation is challenging because current force fields, which were not designed for this process and are often unable to describe correctly peptide self-assembly behavior and the sequence dependence. Here, we developed a framework using dipeptide aggregation as a model system to improve force fields for simulations of self-assembly. Aggregation-related structural properties were designed and used to guide the optimization of peptide-peptide and peptide-solvent interactions. With this framework, we developed a self-assembly force field, termed PACE-ASM, by reoptimizing a hybrid-resolution force field that was originally developed for folding simulation. With its applicability in folding simulations, the new PACE was used to simulate the self-assembly of two disease-related short peptides, Aβ16-21 and PHF6, into β-sheet-rich cross-β amyloids. These simulations reproduced the crystal structures of Aβ16-21 and PHF6 amyloids at near-atomic resolution and captured the difference in packing orientations between the two sequences, a task which is challenging even with all-atom force fields. Apart from cross-β amyloids, the self-assembly of emerging helix-rich cross-α amyloids by another peptide PSMα3 can also be correctly described with the new PACE, manifesting the versatility of the force field. We demonstrated that the ability of the PACE-ASM to model peptide self-assembly is based largely on its improved description of peptide-peptide and peptide-solvent interactions. This was achieved with our optimization framework that can readily identify and address the deficiency in describing these interactions.
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Affiliation(s)
- Xiang Cai
- State Key Laboratory of Chemical Oncogenomics, Guangdong Provincial Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Wei Han
- State Key Laboratory of Chemical Oncogenomics, Guangdong Provincial Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen 518055, China.,Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
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10
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Blanco MA. Computational models for studying physical instabilities in high concentration biotherapeutic formulations. MAbs 2022; 14:2044744. [PMID: 35282775 PMCID: PMC8928847 DOI: 10.1080/19420862.2022.2044744] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.
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Affiliation(s)
- Marco A. Blanco
- Materials and Biophysical Characterization, Analytical R & D, Merck & Co., Inc, Kenilworth, NJ USA
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11
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Banerjee A, Lu CY, Dutt M. A hybrid coarse-grained model for structure, solvation and assembly of lipid-like peptides. Phys Chem Chem Phys 2021; 24:1553-1568. [PMID: 34940778 DOI: 10.1039/d1cp04205j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Reconstituted photosynthetic proteins which are activated upon exposure to solar energy hold enormous potential for powering future solid state devices and solar cells. The functionality and integration of these proteins into such devices has been successfully enabled by lipid-like peptides. Yet, a fundamental understanding of the organization of these peptides with respect to the photosynthetic proteins and themselves remains unknown and is critical for guiding the design of such light-activated devices. This study investigates the relative organization of one such peptide sequence V6K2 (V: valine and K: lysine) within assemblies. Given the expansive spatiotemporal scales associated with this study, a hybrid coarse-grained (CG) model which captures the structure, conformation and aggregation of the peptide is adopted. The CG model uses a combination of iterative Boltzmann inversion and force matching to provide insight into the relative organization of V6K2 in assemblies. The CG model reproduces the structure of a V6K2 peptide sequence along with its all atom (AA) solvation structure. The relative organization of multiple peptides in an assembly, as captured by CG simulations, is in agreement with corresponding results from AA simulations. Also, a backmapping procedure reintroduces the AA details of the peptides within the aggregates captured by the CG model to demonstrate the relative organization of the peptides. Furthermore, a large number of peptides self-assemble into an elongated micelle in the CG simulation, which is consistent with experimental findings. The coarse-graining procedure is tested for transferability to longer peptide sequences, and hence can be extended to other amphiphilic peptide sequences.
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Affiliation(s)
- Akash Banerjee
- Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA.
| | - Chien Yu Lu
- Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA.
| | - Meenakshi Dutt
- Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA.
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12
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Thaler S, Zavadlav J. Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting. Nat Commun 2021; 12:6884. [PMID: 34824254 PMCID: PMC8617111 DOI: 10.1038/s41467-021-27241-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 11/09/2021] [Indexed: 11/09/2022] Open
Abstract
In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently. Top-down approaches that learn NN potentials directly from experimental data have received less attention, typically facing numerical and computational challenges when backpropagating through MD simulations. We present the Differentiable Trajectory Reweighting (DiffTRe) method, which bypasses differentiation through the MD simulation for time-independent observables. Leveraging thermodynamic perturbation theory, we avoid exploding gradients and achieve around 2 orders of magnitude speed-up in gradient computation for top-down learning. We show effectiveness of DiffTRe in learning NN potentials for an atomistic model of diamond and a coarse-grained model of water based on diverse experimental observables including thermodynamic, structural and mechanical properties. Importantly, DiffTRe also generalizes bottom-up structural coarse-graining methods such as iterative Boltzmann inversion to arbitrary potentials. The presented method constitutes an important milestone towards enriching NN potentials with experimental data, particularly when accurate bottom-up data is unavailable.
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Affiliation(s)
- Stephan Thaler
- Professorship of Multiscale Modeling of Fluid Materials, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany.
| | - Julija Zavadlav
- Professorship of Multiscale Modeling of Fluid Materials, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany.
- Munich Data Science Institute, Technical University of Munich, Munich, Germany.
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13
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Potter T, Barrett EL, Miller MA. Automated Coarse-Grained Mapping Algorithm for the Martini Force Field and Benchmarks for Membrane-Water Partitioning. J Chem Theory Comput 2021; 17:5777-5791. [PMID: 34472843 PMCID: PMC8444346 DOI: 10.1021/acs.jctc.1c00322] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Indexed: 01/08/2023]
Abstract
With a view to high-throughput simulations, we present an automated system for mapping and parameterizing organic molecules for use with the coarse-grained Martini force field. The method scales to larger molecules and a broader chemical space than existing schemes. The core of the mapping process is a graph-based analysis of the molecule's bonding network, which has the advantages of being fast, general, and preserving symmetry. The parameterization process pays special attention to coarse-grained beads in aromatic rings. It also includes a method for building efficient and stable frameworks of constraints for molecules with structural rigidity. The performance of the method is tested on a diverse set of 87 neutral organic molecules and the ability of the resulting models to capture octanol-water and membrane-water partition coefficients. In the latter case, we introduce an adaptive method for extracting partition coefficients from free-energy profiles to take into account the interfacial region of the membrane. We also use the models to probe the response of membrane-water partitioning to the cholesterol content of the membrane.
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Affiliation(s)
- Thomas
D. Potter
- Department
of Chemistry, Durham University, South Road, Durham DH1 3LE, United
Kingdom
| | - Elin L. Barrett
- Unilever
Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Mark A. Miller
- Department
of Chemistry, Durham University, South Road, Durham DH1 3LE, United
Kingdom
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14
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Pretti E, Shell MS. A microcanonical approach to temperature-transferable coarse-grained models using the relative entropy. J Chem Phys 2021; 155:094102. [PMID: 34496595 DOI: 10.1063/5.0057104] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Bottom-up coarse-graining methods provide systematic tools for creating simplified models of molecular systems. However, coarse-grained (CG) models produced with such methods frequently fail to accurately reproduce all thermodynamic properties of the reference atomistic systems they seek to model and, moreover, can fail in even more significant ways when used at thermodynamic state points different from the reference conditions. These related problems of representability and transferability limit the usefulness of CG models, especially those of strongly state-dependent systems. In this work, we present a new strategy for creating temperature-transferable CG models using a single reference system and temperature. The approach is based on two complementary concepts. First, we switch to a microcanonical basis for formulating CG models, focusing on effective entropy functions rather than energy functions. This allows CG models to naturally represent information about underlying atomistic energy fluctuations, which would otherwise be lost. Such information not only reproduces energy distributions of the reference model but also successfully predicts the correct temperature dependence of the CG interactions, enabling temperature transferability. Second, we show that relative entropy minimization provides a direct and systematic approach to parameterize such classes of temperature-transferable CG models. We calibrate the approach initially using idealized model systems and then demonstrate its ability to create temperature-transferable CG models for several complex molecular liquids.
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Affiliation(s)
- Evan Pretti
- Department of Chemical Engineering, Engineering II Building, University of California, Santa Barbara, Santa Barbara, California 93106-5080, USA
| | - M Scott Shell
- Department of Chemical Engineering, Engineering II Building, University of California, Santa Barbara, Santa Barbara, California 93106-5080, USA
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15
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Dhabal D, Jiang Z, Pallath A, Patel AJ. Characterizing the Interplay between Polymer Solvation and Conformation. J Phys Chem B 2021; 125:5434-5442. [PMID: 33978411 DOI: 10.1021/acs.jpcb.1c02191] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Conformational transitions of flexible molecules, especially those driven by hydrophobic effects, tend to be hindered by desolvation barriers. For such transitions, it is thus important to characterize and understand the interplay between solvation and conformation. Using specialized molecular simulations, here we perform such a characterization for a hydrophobic polymer solvated in water. We find that an external potential, which unfavorably perturbs the polymer hydration waters, can trigger a coil-to-globule or collapse transition, and that the relative stabilities of the collapsed and extended states can be quantified by the strength of the requisite potential. Our results also provide mechanistic insights into the collapse transition, highlighting that the bottleneck to polymer collapse is the formation of a sufficiently large cluster, and the collective dewetting of such a cluster. We also study the collapse of the hydrophobic polymer in octane, a nonpolar solvent, and interestingly, we find that the mechanistic details of the transition are qualitatively similar to that in water.
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Affiliation(s)
- Debdas Dhabal
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Zhitong Jiang
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Akash Pallath
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Amish J Patel
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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16
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Abstract
Every protein has a story-how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein's shape (i.e., its structure). But increasingly, stories are told using computational molecular physics (CMP). CMP is rooted in the principled physics of driving forces and reveals granular detail of conformational populations in space and time. Recent advances are accessing longer time scales, larger actions, and blind testing, enabling more of biology's stories to be told in the language of atomistic physics.
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Affiliation(s)
- Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Carlos Simmerling
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ken Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA. .,Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New NY 11794, USA
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17
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Choudhury CK, Kuksenok O. Native-Based Dissipative Particle Dynamics Approach for α-Helical Folding. J Phys Chem B 2020; 124:11379-11386. [PMID: 33270459 DOI: 10.1021/acs.jpcb.0c08603] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
We developed a dissipative particle dynamics (DPD) approach that captures polyalanine folding into a stable helical conformation. Within the proposed native-based approach, the DPD parameters are derived based on the contact map constructed from the molecular dynamics (MD) simulations. We show that the proposed approach reproduces the folding of polypeptides of various lengths, including bundle formation for sufficiently long polypeptides. The proposed approach also allows one to capture the folding of the helical segments of the lysozyme. With further development of computationally efficient native-based DPD approaches for folding, modeling of a range of biomaterials incorporating α-helical segments could be extended to time and length scales far beyond those accessible in molecular dynamics simulations.
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Affiliation(s)
- Chandan Kumar Choudhury
- Department of Materials Science and Engineering, Clemson University, Clemson, South Carolina 29634, United States
| | - Olga Kuksenok
- Department of Materials Science and Engineering, Clemson University, Clemson, South Carolina 29634, United States
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18
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Shen K, Sherck N, Nguyen M, Yoo B, Köhler S, Speros J, Delaney KT, Fredrickson GH, Shell MS. Learning composition-transferable coarse-grained models: Designing external potential ensembles to maximize thermodynamic information. J Chem Phys 2020; 153:154116. [DOI: 10.1063/5.0022808] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Kevin Shen
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
- Materials Research Laboratory, University of California, Santa Barbara, California 93106, USA
| | - Nicholas Sherck
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
| | - My Nguyen
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
| | - Brian Yoo
- BASF Corporation, Tarrytown, New York 10591, USA
| | | | - Joshua Speros
- California Research Alliance (CARA) by BASF, Berkeley, California 94720, USA
| | - Kris T. Delaney
- Materials Research Laboratory, University of California, Santa Barbara, California 93106, USA
| | - Glenn H. Fredrickson
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
- Materials Research Laboratory, University of California, Santa Barbara, California 93106, USA
- Department of Materials Engineering, University of California, Santa Barbara, California 93106, USA
| | - M. Scott Shell
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
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19
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Role of Entropy in Colloidal Self-Assembly. ENTROPY 2020; 22:e22080877. [PMID: 33286648 PMCID: PMC7517482 DOI: 10.3390/e22080877] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 08/03/2020] [Accepted: 08/05/2020] [Indexed: 12/13/2022]
Abstract
Entropy plays a key role in the self-assembly of colloidal particles. Specifically, in the case of hard particles, which do not interact or overlap with each other during the process of self-assembly, the free energy is minimized due to an increase in the entropy of the system. Understanding the contribution of entropy and engineering it is increasingly becoming central to modern colloidal self-assembly research, because the entropy serves as a guide to design a wide variety of self-assembled structures for many technological and biomedical applications. In this work, we highlight the importance of entropy in different theoretical and experimental self-assembly studies. We discuss the role of shape entropy and depletion interactions in colloidal self-assembly. We also highlight the effect of entropy in the formation of open and closed crystalline structures, as well as describe recent advances in engineering entropy to achieve targeted self-assembled structures.
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20
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Abstract
Low resolution coarse-grained (CG) models are widely adopted for investigating phenomena that cannot be effectively simulated with all-atom (AA) models. Since the development of the many-body dissipative particle dynamics method, CG models have increasingly supplemented conventional pair potentials with one-body potentials of the local density (LD) around each site. These LD potentials appear to significantly extend the transferability of CG models, while also enabling more accurate descriptions of thermodynamic properties, interfacial phenomena, and many-body correlations. In this work, we systematically examine the properties of LD potentials. We first derive and numerically demonstrate a nontrivial transformation of pair and LD potentials that leaves the total forces and equilibrium distribution invariant. Consequently, the pair and LD potentials determined via bottom-up methods are not unique. We then investigate the sensitivity of CG models for glycerol to the weighting function employed for defining the local density. We employ the multiscale coarse-graining (MS-CG) method to simultaneously parameterize both pair and LD potentials. When employing a short-ranged Lucy function that defines the local density from the first solvation shell, the MS-CG model accurately reproduces the pair structure, pressure-density equation of state, and liquid-vapor interfacial profile of the AA model. The accuracy of the model generally decreases as the range of the Lucy function increases further. The MS-CG model provides similar accuracy when a smoothed Heaviside function is employed to define the local density from the first solvation shell. However, the model performs less well when this function acts on either longer or shorter length scales.
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Affiliation(s)
- Michael R DeLyser
- 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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21
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Manandhar A, Chakraborty K, Tang PK, Kang M, Zhang P, Cui H, Loverde SM. Rational Coarse-Grained Molecular Dynamics Simulations of Supramolecular Anticancer Nanotubes. J Phys Chem B 2019; 123:10582-10593. [DOI: 10.1021/acs.jpcb.9b07417] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Anjela Manandhar
- Department of Chemistry, College of Staten Island, City University of New York, New York 10314, United States
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York 10016, United States
| | - Kaushik Chakraborty
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York 10016, United States
| | - Phu K. Tang
- Department of Chemistry, College of Staten Island, City University of New York, New York 10314, United States
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York 10016, United States
| | - Myungshim Kang
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York 10016, United States
| | - Pengcheng Zhang
- Center of Pharmaceutics, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Honggang Cui
- Department of Chemical and Biomolecular Engineering and Institute for NanoBioTechnology, The Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Sharon M. Loverde
- Department of Chemistry, College of Staten Island, City University of New York, New York 10314, United States
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York 10016, United States
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22
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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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23
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Pervaje AK, Walker CC, Santiso EE. Molecular simulation of polymers with a SAFT-γ Mie approach. MOLECULAR SIMULATION 2019. [DOI: 10.1080/08927022.2019.1645331] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Amulya K. Pervaje
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA
| | - Christopher C. Walker
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA
| | - Erik E. Santiso
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA
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24
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Sanyal T, Mittal J, Shell MS. A hybrid, bottom-up, structurally accurate, Go¯-like coarse-grained protein model. J Chem Phys 2019; 151:044111. [PMID: 31370551 PMCID: PMC6663515 DOI: 10.1063/1.5108761] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 06/24/2019] [Indexed: 12/21/2022] Open
Abstract
Coarse-grained (CG) protein models in the structural biology literature have improved over the years from being simple tools to understand general folding and aggregation driving forces to capturing detailed structures achieved by actual folding sequences. Here, we ask whether such models can be developed systematically from recent advances in bottom-up coarse-graining methods without relying on bioinformatic data (e.g., protein data bank statistics). We use relative entropy coarse-graining to develop a hybrid CG but Go¯-like CG peptide model, hypothesizing that the landscape of proteinlike folds is encoded by the backbone interactions, while the sidechain interactions define which of these structures globally minimizes the free energy in a unique native fold. To construct a model capable of capturing varied secondary structures, we use a new extended ensemble relative entropy method to coarse-grain based on multiple reference atomistic simulations of short polypeptides with varied α and β character. Subsequently, we assess the CG model as a putative protein backbone forcefield by combining it with sidechain interactions based on native contacts but not incorporating native distances explicitly, unlike standard Go¯ models. We test the model's ability to fold a range of proteins and find that it achieves high accuracy (∼2 Å root mean square deviation resolution for both short sequences and large globular proteins), suggesting the strong role that backbone conformational preferences play in defining the fold landscape. This model can be systematically extended to non-natural amino acids and nonprotein polymers and sets the stage for extensions to non-Go¯ models with sequence-specific sidechain interactions.
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Affiliation(s)
- Tanmoy Sanyal
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
| | - Jeetain Mittal
- Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, USA
| | - M. Scott Shell
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
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25
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Szała B, Molski A. Aggregation kinetics of short peptides: All-atom and coarse-grained molecular dynamics study. Biophys Chem 2019; 253:106219. [PMID: 31301554 DOI: 10.1016/j.bpc.2019.106219] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 06/14/2019] [Accepted: 07/03/2019] [Indexed: 11/30/2022]
Abstract
Peptides can aggregate into ordered structures with different morphologies. The aggregation mechanism and evolving structures are the subject of intense research. In this paper we have used molecular dynamics to examine the sequence-dependence of aggregation kinetics for three short peptides: octaalanine (Ala8), octaasparagine (Asn8), and the heptapeptide GNNQQNY (abbreviated as GNN). First, we compared the aggregation of 20 randomly distributed peptides using the coarse-grained MARTINI force field and the atomistic OPLS-AA force field. We found that the MARTINI and OPLS-AA aggregation kinetics are similar for Ala8, Asn8, and GNN. Second, we used the MARTINI force field to study the early stages of aggregation kinetics for a larger system with 72 peptides. In the initial stage of aggregation small clusters grow by monomer addition. In the second stage, when the free monomers are depleted, the dominant cluster growth path is cluster-cluster coalescence. We quantified the aggregation kinetics in terms of rate equations. Our study shows that the initial aggregation kinetics are similar for Ala8, Asn8, and GNN but the molecular details can be different, especially for MARTINI Ala8. We hypothesize that peptide aggregation proceed in two steps. In the first step amorphous aggregates are formed, and then, in the second step, they reorganize into ordered structures. We conclude that sequence-specific differences show up in the second step of aggregation.
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Affiliation(s)
- Beata Szała
- Adam Mickiewicz University in Poznań, Faculty of Chemistry, Umultowska 89b, 61-614 Poznań, Poland.
| | - Andrzej Molski
- Adam Mickiewicz University in Poznań, Faculty of Chemistry, Umultowska 89b, 61-614 Poznań, Poland.
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26
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Rosenberger D, Sanyal T, Shell MS, van der Vegt NFA. Transferability of Local Density-Assisted Implicit Solvation Models for Homogeneous Fluid Mixtures. J Chem Theory Comput 2019; 15:2881-2895. [PMID: 30995034 DOI: 10.1021/acs.jctc.8b01170] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The application of bottom-up coarse grained (CG) models to study the equilibrium mixing behavior of liquids is rather challenging, since these models can be significantly influenced by the density or the concentration of the state chosen during parametrization. This dependency leads to low transferability in density/concentration space and has been one of the major limitations in bottom-up coarse graining. Recent approaches proposed to tackle this shortcoming range from the addition of thermodynamic constraints, to an extended ensemble parametrization, to the addition of supplementary terms to the system's Hamiltonian. To study fluid phase equilibria with bottom-up CG models, the application of local density (LD) potentials appears to be a promising approach, as shown in previous work by Sanyal and Shell [T. Sanyal, M. S. Shell, J. Phys. Chem. B, 2018, 122, 5678]. Here, we want to further explore this method and test its ability to model a system which contains structural inhomogeneities only on the molecular scale, namely, solutions of methanol and water. We find that a water-water LD potential improves the transferability of an implicit-methanol CG model toward high water concentration. Conversely, a methanol-methanol LD potential does not significantly improve the transferability of an implicit-water CG model toward high methanol concentration. These differences appear due to the presence of cooperative interactions in water at high concentrations that the LD potentials can capture. In addition, we compare two different approaches to derive our CG models, namely, relative entropy optimization and the Inverse Monte Carlo method, and formally demonstrate under which analytical and numerical assumptions these two methods yield equivalent results.
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Affiliation(s)
- David Rosenberger
- Eduard Zintl Institut für Anorganische und Physikalische Chemie , Technische Universität Darmstadt , Darmstadt , Germany
| | - Tanmoy Sanyal
- Department of Chemical Engineering , University of California Santa Barbara , Santa Barbara , California 93106 , United States
| | - M Scott Shell
- Department of Chemical Engineering , University of California Santa Barbara , Santa Barbara , California 93106 , United States
| | - Nico F A van der Vegt
- Eduard Zintl Institut für Anorganische und Physikalische Chemie , Technische Universität Darmstadt , Darmstadt , Germany
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27
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Sharp ME, Vázquez FX, Wagner JW, Dannenhoffer-Lafage T, Voth GA. Multiconfigurational Coarse-Grained Molecular Dynamics. J Chem Theory Comput 2019; 15:3306-3315. [PMID: 30897328 PMCID: PMC6660024 DOI: 10.1021/acs.jctc.8b01133] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
![]()
Standard low resolution
coarse-grained modeling techniques have difficulty capturing multiple
configurations of protein systems. Here, we present a method for creating
accurate coarse-grained (CG) models with multiple configurations using
a linear combination of functions or “states”. Individual
CG models are created to capture the individual states, and the approximate
coupling between the two states is determined from an all-atom potential
of mean force. We show that the resulting multiconfiguration coarse-graining
(MCCG) method accurately captures the transition state as well as
the free energy between the two states. We have tested this method
on the folding of dodecaalanine, as well as the amphipathic helix
of endophilin.
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Affiliation(s)
- Morris E Sharp
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics , The University of Chicago , Chicago , Illinois 60637 , United States
| | - Francisco X Vázquez
- Department of Chemistry , St. John's University , Queens , New York 11439 , United States
| | - Jacob W Wagner
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics , The University of Chicago , Chicago , Illinois 60637 , United States
| | - Thomas Dannenhoffer-Lafage
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics , The University of Chicago , Chicago , Illinois 60637 , United States
| | - Gregory A Voth
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics , The University of Chicago , Chicago , Illinois 60637 , United States
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28
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Dignon GL, Zheng W, Mittal J. Simulation methods for liquid-liquid phase separation of disordered proteins. Curr Opin Chem Eng 2019; 23:92-98. [PMID: 32802734 PMCID: PMC7426017 DOI: 10.1016/j.coche.2019.03.004] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Liquid-liquid phase separation of intrinsically disordered proteins (IDPs) and other biomolecules is a highly complex but robust process used by living systems. Drawing inspiration from biology, phase separating proteins have been successfully utilized for promising applications in fields of materials design and drug delivery. These protein-based materials are advantageous due to the ability to finely tune their stimulus-responsive phase behavior and material properties, and the ability to encode biologically active motifs directly into the sequence. The number of possible protein sequences is virtually endless, which makes sequence-based design a rather daunting task, but also attractive due to the amount of control coming from exploration of this variable space. The use of computational methods in this field of research have come to the aid in several aspects, including interpreting experimental results, identifying important structural features and molecular mechanisms capable of explaining the phase behavior, and ultimately providing predictive frameworks for rational design of protein sequences. Here we provide an overview of computational studies focused on phase separating biomolecules and the tools that are available to researchers interested in this topic.
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Affiliation(s)
- Gregory L. Dignon
- Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA, 18015, United States
| | - Wenwei Zheng
- College of Integrative Sciences and Arts, Arizona State University, Mesa, AZ 85212
| | - Jeetain Mittal
- Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA, 18015, United States
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29
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Schöberl M, Zabaras N, Koutsourelakis PS. Predictive collective variable discovery with deep Bayesian models. J Chem Phys 2019; 150:024109. [DOI: 10.1063/1.5058063] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Markus Schöberl
- Center for Informatics and Computational Science, University of Notre Dame, 311 Cushing Hall, Notre Dame, Indiana 46556, USA
- Continuum Mechanics Group, Technical University of Munich, Boltzmannstraße 15, 85748 Garching, Germany
| | - Nicholas Zabaras
- Center for Informatics and Computational Science, University of Notre Dame, 311 Cushing Hall, Notre Dame, Indiana 46556, USA
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30
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Advances in coarse-grained modeling of macromolecular complexes. Curr Opin Struct Biol 2018; 52:119-126. [PMID: 30508766 DOI: 10.1016/j.sbi.2018.11.005] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 11/05/2018] [Accepted: 11/17/2018] [Indexed: 01/12/2023]
Abstract
Recent progress in coarse-grained (CG) molecular modeling and simulation has facilitated an influx of computational studies on biological macromolecules and their complexes. Given the large separation of length-scales and time-scales that dictate macromolecular biophysics, CG modeling and simulation are well-suited to bridge the microscopic and mesoscopic or macroscopic details observed from all-atom molecular simulations and experiments, respectively. In this review, we first summarize recent innovations in the development of CG models, which broadly include structure-based, knowledge-based, and dynamics-based approaches. We then discuss recent applications of different classes of CG models to explore various macromolecular complexes. Finally, we conclude with an outlook for the future in this ever-growing field of biomolecular modeling.
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31
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Conformational preferences and phase behavior of intrinsically disordered low complexity sequences: insights from multiscale simulations. Curr Opin Struct Biol 2018; 56:1-10. [PMID: 30439585 DOI: 10.1016/j.sbi.2018.10.003] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 10/17/2018] [Accepted: 10/19/2018] [Indexed: 11/22/2022]
Abstract
While many proteins and protein regions utilize a complex repertoire of amino acids to achieve their biological function, a subset of protein sequences are enriched in a reduced set of amino acids. These so-called low complexity (LC) sequences, specifically intrinsically disordered variants of LC sequences, have been the focus of recent investigations owing to their roles in a range of biological functions, specifically phase separation. Computational studies of LC sequences have provided rich insights into their behavior both as individual proteins in dilute solutions and as the drivers and modulators of higher-order assemblies. Here, we review how simulations performed across distinct resolutions have provided different types of insights into the biological role of LC sequences.
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32
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Howard MP, Reinhart WF, Sanyal T, Shell MS, Nikoubashman A, Panagiotopoulos AZ. Evaporation-induced assembly of colloidal crystals. J Chem Phys 2018; 149:094901. [DOI: 10.1063/1.5043401] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Affiliation(s)
- Michael P. Howard
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Wesley F. Reinhart
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Tanmoy Sanyal
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
| | - M. Scott Shell
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
| | - Arash Nikoubashman
- Institute of Physics, Johannes Gutenberg University Mainz, Staudingerweg 7, 55128 Mainz, Germany
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33
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Lampel A, Ulijn RV, Tuttle T. Guiding principles for peptide nanotechnology through directed discovery. Chem Soc Rev 2018; 47:3737-3758. [PMID: 29748676 DOI: 10.1039/c8cs00177d] [Citation(s) in RCA: 100] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Life's diverse molecular functions are largely based on only a small number of highly conserved building blocks - the twenty canonical amino acids. These building blocks are chemically simple, but when they are organized in three-dimensional structures of tremendous complexity, new properties emerge. This review explores recent efforts in the directed discovery of functional nanoscale systems and materials based on these same amino acids, but that are not guided by copying or editing biological systems. The review summarises insights obtained using three complementary approaches of searching the sequence space to explore sequence-structure relationships for assembly, reactivity and complexation, namely: (i) strategic editing of short peptide sequences; (ii) computational approaches to predicting and comparing assembly behaviours; (iii) dynamic peptide libraries that explore the free energy landscape. These approaches give rise to guiding principles on controlling order/disorder, complexation and reactivity by peptide sequence design.
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Affiliation(s)
- A Lampel
- Advanced Science Research Center (ASRC) at the Graduate Center, City University of New York (CUNY), New York, NY, USA.
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34
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Frederix PWJM, Patmanidis I, Marrink SJ. Molecular simulations of self-assembling bio-inspired supramolecular systems and their connection to experiments. Chem Soc Rev 2018; 47:3470-3489. [PMID: 29688238 PMCID: PMC5961611 DOI: 10.1039/c8cs00040a] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Indexed: 01/01/2023]
Abstract
In bionanotechnology, the field of creating functional materials consisting of bio-inspired molecules, the function and shape of a nanostructure only appear through the assembly of many small molecules together. The large number of building blocks required to define a nanostructure combined with the many degrees of freedom in packing small molecules has long precluded molecular simulations, but recent advances in computational hardware as well as software have made classical simulations available to this strongly expanding field. Here, we review the state of the art in simulations of self-assembling bio-inspired supramolecular systems. We will first discuss progress in force fields, simulation protocols and enhanced sampling techniques using recent examples. Secondly, we will focus on efforts to enable the comparison of experimentally accessible observables and computational results. Experimental quantities that can be measured by microscopy, spectroscopy and scattering can be linked to simulation output either directly or indirectly, via quantum mechanical or semi-empirical techniques. Overall, we aim to provide an overview of the various computational approaches to understand not only the molecular architecture of nanostructures, but also the mechanism of their formation.
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Affiliation(s)
- Pim W. J. M. Frederix
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials , University of Groningen , Groningen , The Netherlands . ;
| | - Ilias Patmanidis
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials , University of Groningen , Groningen , The Netherlands . ;
| | - Siewert J. Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials , University of Groningen , Groningen , The Netherlands . ;
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35
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Sanyal T, Shell MS. Transferable Coarse-Grained Models of Liquid-Liquid Equilibrium Using Local Density Potentials Optimized with the Relative Entropy. J Phys Chem B 2018; 122:5678-5693. [PMID: 29466859 DOI: 10.1021/acs.jpcb.7b12446] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Bottom-up coarse-grained (CG) models are now regularly pursued to enable large length and time scale molecular simulations of complex, often macromolecular systems. However, predicting fluid phase equilibria using such models remains fundamentally challenging. A major problem stems from the typically low transferability of CG models beyond the densities and/or compositions at which they are parametrized, which is necessary if they are to describe distinct structural and thermodynamic properties unique to each phase. CG model transferability is compounded by the representation of the inherently multibody coarse interactions using pair potentials that neglect higher order effects. Here, we propose to construct transferable single site CG models of liquid mixtures by supplementing traditional CG pair interactions with local density potentials, which constitute a computationally inexpensive mean-field approach to describe many-body effects, in that site energies are modulated by the local solution environment. To illustrate the approach, we use intra- and interspecies local density potentials to develop CG models of benzene-water solutions that show impressive transferability in structural metrics (pair correlation functions, density profiles) throughout composition space, in contrast to pair-only CG representations. While further refinement may be necessary to represent more complex thermodynamic properties, like the liquid-liquid interfacial tension, the generality and improvement offered by the local density approach are highly encouraging for enabling complex phase equilibrium modeling using CG models.
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Affiliation(s)
- Tanmoy Sanyal
- Department of Chemical Engineering , University of California, Santa Barbara , Santa Barbara , California , United States
| | - M Scott Shell
- Department of Chemical Engineering , University of California, Santa Barbara , Santa Barbara , California , United States
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36
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Ferguson AL. Machine learning and data science in soft materials engineering. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2018; 30:043002. [PMID: 29111979 DOI: 10.1088/1361-648x/aa98bd] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by 'de-jargonizing' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.
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Affiliation(s)
- Andrew L Ferguson
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, 1304 West Green Street, Urbana, IL 61801, United States of America. Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, 600 South Mathews Avenue, Urbana, IL 61801, United States of America. Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, IL 61801, United States of America. Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States of America. Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States of America
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37
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Bore SL, Milano G, Cascella M. Hybrid Particle-Field Model for Conformational Dynamics of Peptide Chains. J Chem Theory Comput 2018; 14:1120-1130. [DOI: 10.1021/acs.jctc.7b01160] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Sigbjørn Løland Bore
- Department
of Chemistry and Hylleraas Centre for Quantum Molecular Sciences, University of Oslo, P.O.
Box 1033 Blindern, 0315 Oslo, Norway
| | - Giuseppe Milano
- Department
of Organic Materials Science, University of Yamagata, 4-3-16 Jonan
Yonezawa, Yamagata-ken 992-8510, Japan
| | - Michele Cascella
- Department
of Chemistry and Hylleraas Centre for Quantum Molecular Sciences, University of Oslo, P.O.
Box 1033 Blindern, 0315 Oslo, Norway
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38
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39
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Sinitskiy AV, Voth GA. Quantum mechanics/coarse-grained molecular mechanics (QM/CG-MM). J Chem Phys 2018; 148:014102. [DOI: 10.1063/1.5006810] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Anton V. Sinitskiy
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, USA
| | - Gregory A. Voth
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, USA
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40
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Zavadlav J, Bevc S, Praprotnik M. Adaptive resolution simulations of biomolecular systems. EUROPEAN BIOPHYSICS JOURNAL: EBJ 2017; 46:821-835. [PMID: 28905203 DOI: 10.1007/s00249-017-1248-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Revised: 06/12/2017] [Accepted: 08/15/2017] [Indexed: 10/18/2022]
Abstract
In this review article, we discuss and analyze some recently developed hybrid atomistic-mesoscopic solvent models for multiscale biomolecular simulations. We focus on the biomolecular applications of the adaptive resolution scheme (AdResS), which allows solvent molecules to change their resolution back and forth between atomistic and coarse-grained representations according to their positions in the system. First, we discuss coupling of atomistic and coarse-grained models of salt solution using a 1-to-1 molecular mapping-i.e., one coarse-grained bead represents one water molecule-for development of a multiscale salt solution model. In order to make use of coarse-grained molecular models that are compatible with the MARTINI force field, one has to resort to a supramolecular mapping, in particular to a 4-to-1 mapping, where four water molecules are represented with one coarse-grained bead. To this end, bundled atomistic water models are employed, i.e., the relative movement of water molecules that are mapped to the same coarse-grained bead is restricted by employing harmonic springs. Supramolecular coupling has recently also been extended to polarizable coarse-grained water models with explicit charges. Since these coarse-grained models consist of several interaction sites, orientational degrees of freedom of the atomistic and coarse-grained representations are coupled via a harmonic energy penalty term. The latter aligns the dipole moments of both representations. The reviewed multiscale solvent models are ready to be used in biomolecular simulations, as illustrated in a few examples.
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Affiliation(s)
- Julija Zavadlav
- Department of Molecular Modeling, National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia.,Department of Physics, Faculty of Mathematics and Physics, University of Ljubljana, Jadranska 19, 1000, Ljubljana, Slovenia.,Chair of Computational Science, ETH Zurich, Clausiusstrasse 33, 8092, Zurich, Switzerland
| | - Staš Bevc
- Department of Molecular Modeling, National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia
| | - Matej Praprotnik
- Department of Molecular Modeling, National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia. .,Department of Physics, Faculty of Mathematics and Physics, University of Ljubljana, Jadranska 19, 1000, Ljubljana, Slovenia.
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41
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Dama JF, Jin J, Voth GA. The Theory of Ultra-Coarse-Graining. 3. Coarse-Grained Sites with Rapid Local Equilibrium of Internal States. J Chem Theory Comput 2017; 13:1010-1022. [PMID: 28112956 DOI: 10.1021/acs.jctc.6b01081] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
When viewed through a coarse-grained lens, important molecular and biophysical systems can appear to undergo discrete, switch-like state changes in addition to more continuous configurational motions. One of our recent papers described a theory for bottom-up coarse-graining of the equilibrium statistics of models with such behavior, called ultra-coarse-grained (UCG) models, and a follow up paper described an implementation when the states of the coarse-grained sites or "beads" change rarely. However, not all systems with this discrete behavior fall under that special limit. This article develops the general UCG theory for the opposite limit, that is, where the internal states of the CG particles or beads adjust rapidly so as to always remain effectively at quasi-equilibrium no matter what the positions of the coarse-grained particles. This rapid local equilibrium allows ultra-coarse-graining to mix standard coarse-grained force fields by using local order parameters to control the degree of mixing, which adds an environmental dependence and many-body effects to the coarse-grained model while requiring minimal new coding. This article first presents the definition of such UCG force fields as well as their fitting procedures from atomistic-scale data, and then it presents three examples of UCG simulations with an approach that we call UCG with rapid local equilibrium (UCG-RLE). We then present an application of UCG-RLE using the full bottom-up methodology to coarse-grain and simulate cooperative hydrophobic association of neopentane in methanol solvent. UCG-RLE force matching does a superior job of matching solute-solute correlation functions and solute cluster size distributions compared to the more standard force-matched models not having coarse-grained sites with discrete internal states.
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Affiliation(s)
- James F Dama
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago , Chicago, Illinois 60637, United States
| | - Jaehyeok Jin
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago , Chicago, Illinois 60637, United States
| | - Gregory A Voth
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago , Chicago, Illinois 60637, United States
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42
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Sanyal T, Shell MS. Coarse-grained models using local-density potentials optimized with the relative entropy: Application to implicit solvation. J Chem Phys 2017; 145:034109. [PMID: 27448876 DOI: 10.1063/1.4958629] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Bottom-up multiscale techniques are frequently used to develop coarse-grained (CG) models for simulations at extended length and time scales but are often limited by a compromise between computational efficiency and accuracy. The conventional approach to CG nonbonded interactions uses pair potentials which, while computationally efficient, can neglect the inherently multibody contributions of the local environment of a site to its energy, due to degrees of freedom that were coarse-grained out. This effect often causes the CG potential to depend strongly on the overall system density, composition, or other properties, which limits its transferability to states other than the one at which it was parameterized. Here, we propose to incorporate multibody effects into CG potentials through additional nonbonded terms, beyond pair interactions, that depend in a mean-field manner on local densities of different atomic species. This approach is analogous to embedded atom and bond-order models that seek to capture multibody electronic effects in metallic systems. We show that the relative entropy coarse-graining framework offers a systematic route to parameterizing such local density potentials. We then characterize this approach in the development of implicit solvation strategies for interactions between model hydrophobes in an aqueous environment.
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Affiliation(s)
- Tanmoy Sanyal
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
| | - M Scott Shell
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
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43
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Wagner JW, Dama JF, Durumeric AEP, Voth GA. On the representability problem and the physical meaning of coarse-grained models. J Chem Phys 2017; 145:044108. [PMID: 27475349 DOI: 10.1063/1.4959168] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
In coarse-grained (CG) models where certain fine-grained (FG, i.e., atomistic resolution) observables are not directly represented, one can nonetheless identify indirect the CG observables that capture the FG observable's dependence on CG coordinates. Often, in these cases it appears that a CG observable can be defined by analogy to an all-atom or FG observable, but the similarity is misleading and significantly undermines the interpretation of both bottom-up and top-down CG models. Such problems emerge especially clearly in the framework of the systematic bottom-up CG modeling, where a direct and transparent correspondence between FG and CG variables establishes precise conditions for consistency between CG observables and underlying FG models. Here we present and investigate these representability challenges and illustrate them via the bottom-up conceptual framework for several simple analytically tractable polymer models. The examples provide special focus on the observables of configurational internal energy, entropy, and pressure, which have been at the root of controversy in the CG literature, as well as discuss observables that would seem to be entirely missing in the CG representation but can nonetheless be correlated with CG behavior. Though we investigate these problems in the framework of systematic coarse-graining, the lessons apply to top-down CG modeling also, with crucial implications for simulation at constant pressure and surface tension and for the interpretations of structural and thermodynamic correlations for comparison to experiment.
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Affiliation(s)
- Jacob W Wagner
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, and Computation Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - James F Dama
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, and Computation Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - 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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44
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Yuan C, Li S, Zou Q, Ren Y, Yan X. Multiscale simulations for understanding the evolution and mechanism of hierarchical peptide self-assembly. Phys Chem Chem Phys 2017; 19:23614-23631. [DOI: 10.1039/c7cp01923h] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Multiscale molecular simulations that combine and systematically link several hierarchies can provide insights into the evolution and dynamics of hierarchical peptide self-assembly from the molecular level to the mesoscale.
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Affiliation(s)
- Chengqian Yuan
- State Key Laboratory of Biochemical Engineering
- Institute of Process Engineering
- Chinese Academy of Sciences
- Beijing 100190
- China
| | - Shukun Li
- State Key Laboratory of Biochemical Engineering
- Institute of Process Engineering
- Chinese Academy of Sciences
- Beijing 100190
- China
| | - Qianli Zou
- State Key Laboratory of Biochemical Engineering
- Institute of Process Engineering
- Chinese Academy of Sciences
- Beijing 100190
- China
| | - Ying Ren
- Center for Mesoscience
- Institute of Process Engineering
- Chinese Academy of Sciences
- Beijing 100190
- China
| | - Xuehai Yan
- State Key Laboratory of Biochemical Engineering
- Institute of Process Engineering
- Chinese Academy of Sciences
- Beijing 100190
- China
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45
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Musiani F, Giorgetti A. Protein Aggregation and Molecular Crowding: Perspectives From Multiscale Simulations. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2016; 329:49-77. [PMID: 28109331 DOI: 10.1016/bs.ircmb.2016.08.009] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Cells are extremely crowded environments, thus the use of diluted salted aqueous solutions containing a single protein is too simplistic to mimic the real situation. Macromolecular crowding might affect protein structure, folding, shape, conformational stability, binding of small molecules, enzymatic activity, interactions with cognate biomolecules, and pathological aggregation. The latter phenomenon typically leads to the formation of amyloid fibrils that are linked to several lethal neurodegenerative diseases, but that can also play a functional role in certain organisms. The majority of molecular simulations performed before the last few years were conducted in diluted solutions and were restricted both in the timescales and in the system dimensions by the available computational resources. In recent years, several computational solutions were developed to get close to physiological conditions. In this review we summarize the main computational techniques used to tackle the issue of protein aggregation both in a diluted and in a crowded environment.
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Affiliation(s)
- F Musiani
- Laboratory of Bioinorganic Chemistry, University of Bologna, Bologna, Italy.
| | - A Giorgetti
- Applied Bioinformatics Group, University of Verona, Verona, Italy.
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46
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Ozgur B, Sayar M. Assembly of Triblock Amphiphilic Peptides into One-Dimensional Aggregates and Network Formation. J Phys Chem B 2016; 120:10243-10257. [PMID: 27635660 DOI: 10.1021/acs.jpcb.6b07545] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Peptide assembly plays a key role in both neurological diseases and development of novel biomaterials with well-defined nanostructures. Synthetic model peptides provide a unique platform to explore the role of intermolecular interactions in the assembly process. A triblock peptide architecture designed by the Hartgerink group is a versatile system which relies on Coulomb interactions, hydrogen bonding, and hydrophobicity to guide these peptides' assembly at three different length scales: β-sheets, double-wall ribbon-like aggregates, and finally a highly porous network structure which can support gels with ≤1% by weight peptide concentration. In this study, by using molecular dynamics simulations of a structure based implicit solvent coarse grained model, we analyzed this hierarchical assembly process. Parametrization of our CG model is based on multiple-state points from atomistic simulations, which enables this model to represent the conformational adaptability of the triblock peptide molecule based on the surrounding medium. Our results indicate that emergence of the double-wall β-sheet packing mechanism, proposed in light of the experimental evidence, strongly depends on the subtle balance of the intermolecular forces. We demonstrate that, even though backbone hydrogen bonding dominates the early nucleation stages, depending on the strength of the hydrophobic and Coulomb forces, alternative structures such as zero-dimensional aggregates with two β-sheets oriented orthogonally (which we refer to as a cross-packed structure) and β-sheets with misoriented hydrophobic side chains are also feasible. We discuss the implications of these competing structures for the three different length scales of assembly by systematically investigating the influence of density, counterion valency, and hydrophobicity.
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Affiliation(s)
| | - Mehmet Sayar
- College of Engineering, Koc University , Istanbul, Turkey.,Chemical & Biological Engineering and Mechanical Engineering Departments, Koc University , Istanbul, Turkey
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47
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Affiliation(s)
- M. Scott Shell
- Department of Chemical Engineering; University of California Santa Barbara; Santa Barbara CA USA
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48
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Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A. Coarse-Grained Protein Models and Their Applications. Chem Rev 2016; 116:7898-936. [DOI: 10.1021/acs.chemrev.6b00163] [Citation(s) in RCA: 555] [Impact Index Per Article: 69.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sebastian Kmiecik
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Dominik Gront
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Michal Kolinski
- Bioinformatics
Laboratory, Mossakowski Medical Research Center of the Polish Academy of Sciences, Pawinskiego 5, 02-106 Warsaw, Poland
| | - Lukasz Wieteska
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
- Department
of Medical Biochemistry, Medical University of Lodz, Mazowiecka 6/8, 92-215 Lodz, Poland
| | | | - Andrzej Kolinski
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
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49
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Lu J, Chakravarty C, Molinero V. Relationship between the line of density anomaly and the lines of melting, crystallization, cavitation, and liquid spinodal in coarse-grained water models. J Chem Phys 2016; 144:234507. [DOI: 10.1063/1.4953854] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Jibao Lu
- Department of Chemistry, The University of Utah, Salt Lake City, Utah 84112-0850, USA
| | | | - Valeria Molinero
- Department of Chemistry, The University of Utah, Salt Lake City, Utah 84112-0850, USA
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50
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Irbäck A, Wessén J. Thermodynamics of amyloid formation and the role of intersheet interactions. J Chem Phys 2016; 143:105104. [PMID: 26374063 DOI: 10.1063/1.4930280] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
The self-assembly of proteins into β-sheet-rich amyloid fibrils has been observed to occur with sigmoidal kinetics, indicating that the system initially is trapped in a metastable state. Here, we use a minimal lattice-based model to explore the thermodynamic forces driving amyloid formation in a finite canonical (NVT) system. By means of generalized-ensemble Monte Carlo techniques and a semi-analytical method, the thermodynamic properties of this model are investigated for different sets of intersheet interaction parameters. When the interactions support lateral growth into multi-layered fibrillar structures, an evaporation/condensation transition is observed, between a supersaturated solution state and a thermodynamically distinct state where small and large fibril-like species exist in equilibrium. Intermediate-size aggregates are statistically suppressed. These properties do not hold if aggregate growth is one-dimensional.
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Affiliation(s)
- Anders Irbäck
- Department of Astronomy and Theoretical Physics, Lund University, Sölvegatan 14A, SE-223 62 Lund, Sweden
| | - Jonas Wessén
- Department of Astronomy and Theoretical Physics, Lund University, Sölvegatan 14A, SE-223 62 Lund, Sweden
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