Coarse-Grained Simulation of Mechanical Properties of Single Microtubules With Micrometer Length

Zha, Jinyin; Zhang, Yuwei; Xia, Kelin; Gräter, Frauke; Xia, Fei

doi:10.3389/fmolb.2020.632122

Cited by 8 publications

(18 citation statements)

References 87 publications

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“…Coarse-grained (CG) models − have been widely used to simulate complex biological systems such as cell membranes, macromolecular complexes, and supramolecular systems . By eliminating the less important degrees of all-atom (AA) models with the CG beads, CG models could significantly accelerate the molecular dynamics (MD) simulation by a magnitude of 1–3 orders and achieve the time scale of milliseconds . The performance of CG models not only depends on the resolution of their CG beads but also on the accuracy of the CG force fields.…”

Section: Introductionmentioning

confidence: 99%

“…6 By eliminating the less important degrees of all-atom (AA) models with the CG beads, CG models could significantly accelerate the molecular dynamics (MD) simulation by a magnitude of 1−3 orders and achieve the time scale of milliseconds. 7 The performance of CG models not only depends on the resolution of their CG beads but also on the accuracy of the CG force fields. The CG models, successfully developed and applied over decades, have spanned a large range of resolutions from the high-resolution CG models of the subresidue level 8−14 to that beyond residues, 15,16 while their potential energy functions have adopted various expressions such as those used by the classical force fields, 17−19 those by the empirical elastic network models, 20,21 or those of some specific forms as in the Go ̅ models.…”

Section: Introductionmentioning

confidence: 99%

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Accurate and Efficient Estimation of Lennard–Jones Interactions for Coarse-Grained Particles via a Potential Matching Method

Zhang

Wang

Xia

et al. 2022

J. Chem. Theory Comput.

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The Lennard–Jones (LJ) energy functions are commonly used to describe the nonbonded interactions in bulk coarse-grained (CG) models, which contribute significantly to the stabilization of a local binding configuration or a self-assembly system. In many cases, systematic development of the LJ interaction parameters in a CG model requires a comprehensive sampling of the objective molecules at the all-atom (AA) level, which is therefore extremely time-consuming for large systems. Inspired by the concept of electrostatic potential (ESP), we define the LJ static potential (LJSP), by which the embedding potential energy surface can be constructed analytically. A semianalytic approach, namely, the LJSP matching method, is developed here to derive the CG parameters by minimizing the LJSP difference between the AA and the CG models, which provides a universal way to derive the CG LJ parameters from the AA models without doing presampling. The LJSP matching method is successful not only in deriving the LJ interaction energy landscape in the CG models for proteins, lipids, and DNA but also in reproducing the critical properties such as intermediate structures and enthalpy contributions as exemplified in simulating the self-assembly process of the dipalmitoylphosphatidylcholine (DPPC) lipids.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accurate and Efficient Estimation of Lennard–Jones Interactions for Coarse-Grained Particles via a Potential Matching Method

Zhang

Wang

Xia

et al. 2022

J. Chem. Theory Comput.

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“…Recently, the UCG models based on the fluctuation maximization with harmonic interactions were developed for the glutamine-binding protein and lactoferrin and were able to correctly describe the protein conformational transitions . Relatedly, a network-based UCG model was reported to effectively assess the mechanical properties of microtubules …”

Section: Ultra-coarse-graining: Machinery To Generalize Representabil...mentioning

confidence: 99%

“… 319 Relatedly, a network-based UCG model was reported to effectively assess the mechanical properties of microtubules. 320 …”

Section: Ultra-coarse-graining: Machinery To Generalize Representabil...mentioning

confidence: 99%

Bottom-up Coarse-Graining: Principles and Perspectives

Jin

Pak

Durumeric

et al. 2022

J. Chem. Theory Comput.

129

147

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Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.

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“…The advantage of UCG models lies in their smaller number of beads, which allows the simulation to reach the time scale of microseconds. 47…”

Section: Introductionmentioning

confidence: 99%

Development of multiscale ultra-coarse-grained models for the SARS-CoV-2 virion from cryo-electron microscopy data

Zhang

Xia

et al. 2023

Phys. Chem. Chem. Phys.

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Coarse-Grained Simulation of Mechanical Properties of Single Microtubules With Micrometer Length

Cited by 8 publications

References 87 publications

Accurate and Efficient Estimation of Lennard–Jones Interactions for Coarse-Grained Particles via a Potential Matching Method

Accurate and Efficient Estimation of Lennard–Jones Interactions for Coarse-Grained Particles via a Potential Matching Method

Bottom-up Coarse-Graining: Principles and Perspectives

Development of multiscale ultra-coarse-grained models for the SARS-CoV-2 virion from cryo-electron microscopy data

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