Dissipative Particle Dynamics Simulation: A Review on Investigating Mesoscale Properties of Polymer Systems

Wang, Jihao; Han, Yuanjia; Xu, Zhiyang; Yang, Xiaozhen; Ramakrishna, Seeram; Liu, Yong

doi:10.1002/mame.202000724

Cited by 31 publications

(26 citation statements)

References 125 publications

(170 reference statements)

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“…We first used DPD simulations to study the supramolecular assembly behavior of PDPP at different volume fractions in H 2 O, as DPD simulation is currently recognized as a viable approach to intuitively study the polymeric supramolecular self‐assembly (SSA) process with evolving morphologies (Scheme S1, Supporting Information). [ 12 ] With an increase in the volume fraction, PDPP undergoes morphological transition from unimolecular micelles (0.5%) to nanospheres (5–10%), cylinders (20–25%), and multigeometrical aggregates (30–35%) in sequence, and the size of these assemblies also increases ( Figure 1a , Figure S1 and Videos S1 – S8 , Supporting Information). The radial distribution function (RDF) plot shows that PDPP could encapsulate hydrophobic Ppa inside the hydrophilic PEGylated dendritic peptide conjugate at any volume fraction (Figure S2 , Supporting Information).…”

Section: Resultsmentioning

confidence: 99%

Self‐Stabilized Supramolecular Assemblies Constructed from PEGylated Dendritic Peptide Conjugate for Augmenting Tumor Retention and Therapy

et al. 2021

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Supramolecular self-assemblies of dendritic peptides with well-organized nanostructures have great potential as multifunctional biomaterials, yet the complex self-assembly mechanism hampers their wide exploration. Herein, a self-stabilized supramolecular assembly (SSA) constructed from a PEGylated dendritic peptide conjugate (PEG-dendritic peptide-pyropheophorbide a, PDPP), for augmenting tumor retention and therapy, is reported. The supramolecular self-assembly process of PDPP is concentration-dependent with multiple morphologies. By tailoring the concentration of PDPP, the supramolecular self-assembly is driven by noncovalent interactions to form a variety of SSAs (unimolecular micelles, oligomeric aggregates, and multi-aggregates) with different sizes from nanometer to micrometer. SSAs at 100 nm with a spherical shape possess extremely high stability to prolong blood circulation about 4.8-fold higher than pyropheophorbide a (Ppa), and enhance tumor retention about eight-fold higher than Ppa on day 5 after injection, which leads to greatly boosting the in vivo photodynamic therapeutic efficiency. RNA-seq demonstrates that these effects of SSAs are related to the inhibition of MET-PI3K-Akt pathway. Overall, the supramolecular self-assembly mechanism for the synthetic PEGylated dendritic peptide conjugate sheds new light on the development of supramolecular assemblies for tumor therapy.

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Section: Resultsmentioning

confidence: 99%

Self‐Stabilized Supramolecular Assemblies Constructed from PEGylated Dendritic Peptide Conjugate for Augmenting Tumor Retention and Therapy

et al. 2021

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“…These interactions between beads can be fine-tuned to capture the macroscopic phenomena on larger time scales. The approach is more effective in studying the mesoscale properties, such as the flow of polymer fluids and the growth of self-assembled morphologies ( Wang et al, 2021a ).…”

Section: Basic Description Of MD Simulations For Polymersmentioning

confidence: 99%

Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design

2022

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In recent years, the synthesis of monomer sequence-defined polymers has expanded into broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing the characterization and inverse design of these polymer systems requires our fundamental understanding not only at the individual monomer level, but also considering the chain scales, such as polymer configuration, self-assembly, and phase separation. However, our accessibility to this field is still rudimentary due to the limitations of traditional design approaches, the complexity of chemical space along with the burdened cost and time issues that prevent us from unveiling the underlying monomer sequence-structure-property relationships. Fortunately, thanks to the recent advancements in molecular dynamics simulations and machine learning (ML) algorithms, the bottlenecks in the tasks of establishing the structure-function correlation of the polymer chains can be overcome. In this review, we will discuss the applications of the integration between ML techniques and coarse-grained molecular dynamics (CGMD) simulations to solve the current issues in polymer science at the chain level. In particular, we focus on the case studies in three important topics—polymeric configuration characterization, feed-forward property prediction, and inverse design—in which CGMD simulations are leveraged to generate training datasets to develop ML-based surrogate models for specific polymer systems and designs. By doing so, this computational hybridization allows us to well establish the monomer sequence-functional behavior relationship of the polymers as well as guide us toward the best polymer chain candidates for the inverse design in undiscovered chemical space with reasonable computational cost and time. Even though there are still limitations and challenges ahead in this field, we finally conclude that this CGMD/ML integration is very promising, not only in the attempt of bridging the monomeric and macroscopic characterizations of polymer materials, but also enabling further tailored designs for sequence-specific polymers with superior properties in many practical applications.

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“…First, according to previous work, [PCL-(PHEMA-A)-PPEGMA] 6 , 2,2 0 -diselenobis (phenylethanamine), gold atoms, and water were separated into small beads (Figure S1), and afterward, amorphous cells and discovery in Materials Studio 8.0 (Accelrys Inc.) were used to calculate the interaction parameters. [35][36][37] The DPD simulation of the formation of [PCL-b-P(HEMA-A$)-b-PPEGMA] 6 non-crosslinked micelles was performed using the Mesocite module in a 37 Â 37 Â 37 r c 3 cubic simulation box, in which periodic boundary conditions were applied in all directions. Subsequently, DPD simulation and Materials Studio (MS) script, based on the nearest-neighbor bonding principle, were used to explore the formation process of [PCL-(PHEMA-SeSe$)-PPEGMA] 6 crosslinked micelles.…”

Section: Computer Simulationmentioning

confidence: 99%

Diselenide‐bearing crosslinked micelles‐reduced and stabilized gold nanoparticles in‐situ

Lin

Yan

Pan

et al. 2021

J of Applied Polymer Sci

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Gold nanoparticles (GNPs), usually fabricated through reduction and stabilization two steps, have been widely used in biomedical field. Here, the six-armed star copolymer was synthesized and self-assembled into crosslinked micelles for the in-situ preparation of GNPs in one-step without external reductant.The copolymer and their precursors were synthesized by a combination of ring-opening polymerization (ROP), activators regenerated by electron transfer atom transfer radical polymerization (ARGET ATRP), and Michael addition reaction. Their molecular weights and structures were measured by gel permeation chromatography (GPC) and 1 H NMR. The diselenide bond of crosslinked micelles reduced AuCl 4 À to GNPs, and the crosslinked micelles stabilized the GNPs in-situ. Through increasing AuCl 4 À concentration, the collision probability between the GNPs increased, with particle diameters of 1.5, 2.3, and 3.0 nm. Moreover, the GNPs showed good stability, and the wavelength of maximum absorbance (λ max ) did not change under different dilution, temperature, and storage conditions. Furthermore, integrating dissipative particle dynamics (DPD) simulations and scripts with experiments, the formation process of crosslinked micelles, the stabilization process, and the size regulation of GNPs at mesoscopic scale were revealed. Therefore, this stable crosslinked micelles/GNPs hybrid material is expected to be used as a nanocarrier in an integrated diagnosis and treatment system.

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Dissipative Particle Dynamics Simulation: A Review on Investigating Mesoscale Properties of Polymer Systems

Cited by 31 publications

References 125 publications

Self‐Stabilized Supramolecular Assemblies Constructed from PEGylated Dendritic Peptide Conjugate for Augmenting Tumor Retention and Therapy

Self‐Stabilized Supramolecular Assemblies Constructed from PEGylated Dendritic Peptide Conjugate for Augmenting Tumor Retention and Therapy

Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design

Diselenide‐bearing crosslinked micelles‐reduced and stabilized gold nanoparticles in‐situ

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