Development of a coarse-grained lipid model, LIME 2.0, for DSPE using multistate iterative Boltzmann inversion and discontinuous molecular dynamics simulations

Wang, Kye Won; Wang, Yiming; Hall, Carol K.

doi:10.1016/j.fluid.2020.112704

Cited by 4 publications

(5 citation statements)

References 48 publications

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“…364−366 Similarly, Hall and co-workers employed ms-IBI to parametrize a transferable force field for phospholipids based upon simulated rdfs for multiple temperatures. 367 Alternatively, one can employ an "extended ensemble" variational principle to determine a set of transferable potentials that optimally describes the PMF for multiple systems across multiple state points. 234 For instance, Rudzinski et al employed the extended ensemble approach to develop transferable potentials for modeling poly(ethylene oxide)-based ionomers with varying degrees of sulfonation at a range of temperatures.…”

Section: Composition Dependencementioning

confidence: 99%

“…Indeed, Potter and Wilson demonstrated that ms-IBI pair-potentials provided high structural fidelity across the entire range of octane–benzene mixtures . McCabe and co-workers have recently employed ms-IBI to develop a transferable force field for stratum corneum membranes composed of sphingolipids and cholesterol. − Similarly, Hall and co-workers employed ms-IBI to parametrize a transferable force field for phospholipids based upon simulated rdfs for multiple temperatures …”

Section: Transferability and Thermodynamicsmentioning

confidence: 99%

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Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Noid

2023

J. Phys. Chem. B

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By averaging over atomic details, coarse-grained (CG) models provide profound computational and conceptual advantages for studying soft materials. In particular, bottom-up approaches develop CG models based upon information obtained from atomically detailed models. At least in principle, a bottom-up model can reproduce all the properties of an atomically detailed model that are observable at the resolution of the CG model. Historically, bottom-up approaches have accurately modeled the structure of liquids, polymers, and other amorphous soft materials, but have provided lower structural fidelity for more complex biomolecular systems. Moreover, they have also been plagued by unpredictable transferability and a poor description of thermodynamic properties. Fortunately, recent studies have reported dramatic advances in addressing these prior limitations. This Perspective reviews this remarkable progress, while focusing on its foundation in the basic theory of coarse-graining. In particular, we describe recent insights and advances for treating the CG mapping, for modeling many-body interactions, for addressing the state-point dependence of effective potentials, and even for reproducing atomic observables that are beyond the resolution of the CG model. We also outline outstanding challenges and promising directions in the field. We anticipate that the synthesis of rigorous theory and modern computational tools will result in practical bottom-up methods that not only are accurate and transferable but also provide predictive insight for complex systems.

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Section: Composition Dependencementioning

confidence: 99%

Section: Transferability and Thermodynamicsmentioning

confidence: 99%

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Noid

2023

J. Phys. Chem. B

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“…Furthermore, rigorous statistical mechanics theory can be applied to relate the ensemble of microscopic configurations with macroscopic thermodynamic properties. Classical molecular simulations have been applied to study a wide range of biomaterial systems such as supramolecular biopolymer, DNA-based bioconjugate, self-assembling peptides, − carbohydrate-based biomaterials, , and lipid membranes. − …”

Section: Methodsmentioning

confidence: 99%

Computational Design of Peptides for Biomaterials Applications

Wang

Stebe

Fuente-Núñez

et al. 2023

ACS Appl. Bio Mater.

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Computer-aided molecular design and protein engineering emerge as promising and active subjects in bioengineering and biotechnological applications. On one hand, due to the advancing computing power in the past decade, modeling toolkits and force fields have been put to use for accurate multiscale modeling of biomolecules including lipid, protein, carbohydrate, and nucleic acids. On the other hand, machine learning emerges as a revolutionary data analysis tool that promises to leverage physicochemical properties and structural information obtained from modeling in order to build quantitative protein structure−function relationships. We review recent computational works that utilize state-of-the-art computational methods to engineer peptides and proteins for various emerging biomedical, antimicrobial, and antifreeze applications. We also discuss challenges and possible future directions toward developing a roadmap for efficient biomolecular design and engineering.

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“…In a recent work, Moore et al introduced multistate iterative Boltzmann inversion (MS-IBI), wherein initial potential estimates and their subsequent updates were averaged across various thermodynamic states to derive a more broadly transferable potential . The MS-IBI has been applied to enhance CG transferability in many scenarios, such as lipid mixtures, enhanced temperature transferability, pressure transferability in shocked polymers, concentration transferability, and for reproducing thermal transport and phase transitions of bulk PE . However, despite increasing accuracy and thermodynamic consistency, MS-IBI has yet been shown to improve predictive capability for mechanical or dynamical properties …”

Section: Introductionmentioning

confidence: 99%

Derived Coarse-Grained Potentials for Semicrystalline Polymers with a Blended Multistate Iterative Boltzmann Inversion Method

Eghlidos,

Oswald

2023

J. Chem. Theory Comput.

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Development of a coarse-grained lipid model, LIME 2.0, for DSPE using multistate iterative Boltzmann inversion and discontinuous molecular dynamics simulations

Cited by 4 publications

References 48 publications

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Computational Design of Peptides for Biomaterials Applications

Derived Coarse-Grained Potentials for Semicrystalline Polymers with a Blended Multistate Iterative Boltzmann Inversion Method

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