Bottom-Up Coarse-Grained Modeling of DNA

Sun, Tiedong; Minhas, Vishal; Korolev, Nikolay; Mirzoev, Alexander; Lyubartsev, Alexander P.; Nordenskiöld, Lars

doi:10.3389/fmolb.2021.645527

Cited by 23 publications

(28 citation statements)

References 83 publications

(86 reference statements)

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“…A number of methods combine the bottom-up approach in the derivation of coarse-grained energy terms and the top-down approach to put them together into a working force field. In this section, we summarize some of the coarse-grained force fields and methods of their derivation, referring the reader to dedicated review articles [ 23 , 61 , 62 , 63 , 64 ] and books [ 12 , 13 , 14 ] for details.…”

Section: Theory and Methodologymentioning

confidence: 99%

Theory and Practice of Coarse-Grained Molecular Dynamics of Biologically Important Systems

et al. 2021

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Molecular dynamics with coarse-grained models is nowadays extensively used to simulate biomolecular systems at large time and size scales, compared to those accessible to all-atom molecular dynamics. In this review article, we describe the physical basis of coarse-grained molecular dynamics, the coarse-grained force fields, the equations of motion and the respective numerical integration algorithms, and selected practical applications of coarse-grained molecular dynamics. We demonstrate that the motion of coarse-grained sites is governed by the potential of mean force and the friction and stochastic forces, resulting from integrating out the secondary degrees of freedom. Consequently, Langevin dynamics is a natural means of describing the motion of a system at the coarse-grained level and the potential of mean force is the physical basis of the coarse-grained force fields. Moreover, the choice of coarse-grained variables and the fact that coarse-grained sites often do not have spherical symmetry implies a non-diagonal inertia tensor. We describe selected coarse-grained models used in molecular dynamics simulations, including the most popular MARTINI model developed by Marrink’s group and the UNICORN model of biological macromolecules developed in our laboratory. We conclude by discussing examples of the application of coarse-grained molecular dynamics to study biologically important processes.

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Section: Theory and Methodologymentioning

confidence: 99%

Theory and Practice of Coarse-Grained Molecular Dynamics of Biologically Important Systems

et al. 2021

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“…An advantage of the IMC scheme is that the parameter updates are rigorously derived from statistical mechanical manipulation and that the updates are supplied simultaneously, which may lead to faster convergence; however, the latter advantage also necessitates the acquisition of cross-correlated averages (i.e., hS α S γ i). Although not as widely used today as IBI, IMC (and variants) have been employed for CG polymer simulation, such as modeling biopolymers 204,205 and polyisoprene melts. 206…”

Section: Key Equation(s) Key Inputs Advantages Disadvantagesmentioning

confidence: 99%

Chemically specific coarse‐graining of polymers: Methods and prospects

Dhamankar

Webb

2021

Journal of Polymer Science

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Coarse-grained (CG) modeling is an invaluable tool for the study of polymers and other soft matter systems due to the span of spatiotemporal scales that typify their physics and behavior. Given continuing advancements in experimental synthesis and characterization of such systems, there is ever greater need to leverage and expand CG capabilities to simulate diverse soft matter systems with chemical specificity. In this review, we discuss essential modeling techniques, bottom-up coarse-graining methodologies, and outstanding challenges for the chemically specific CG modeling of polymer-based systems. This methodologically oriented discussion is complemented by representative literature examples for polymer simulation; we also offer some advisory practical considerations that should be useful for new researchers. Given its growing importance in the modeling and polymer science community, we further highlight some recent applications of machine learning that enhance CG modeling strategies. Overall, this review provides comprehensive discussion of methods and prospects for the chemically specific coarse-graining of polymers.

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“…Collective variable-based potentials, for example those dependent on density, are sometimes used ( Louis, 2002 ; Wagner et al., 2017 ). Pairwise force-matching optimization strategies include the simplex algorithm ( Meyer et al., 2000 ), iterative Boltzmann inversion ( Reith et al., 2003 ), inverse Monte Carlo ( Lyubartsev and Laaksonen, 1995 ), and machine learning ( Gkeka et al., 2020 ), among others ( Sun et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%