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使用粗粒化分子动力学模拟研究 Pluronic L64 的相图。

Insights into the Phase Diagram of Pluronic L64 Using Coarse-Grained Molecular Dynamics Simulations.

机构信息

Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India.

Prescience Insilico Private Limited, Fifth Floor, Novel MSR Building, Marathahalli, Bengaluru, Karnataka 560037, India.

出版信息

J Phys Chem B. 2022 Jun 30;126(25):4731-4744. doi: 10.1021/acs.jpcb.2c02429. Epub 2022 Jun 15.

DOI:10.1021/acs.jpcb.2c02429
PMID:35708274
Abstract

We investigate the concentration-dependent phase diagram of pluronic L64 in aqueous media at 300 and 320 K using coarse-grained (CG) molecular dynamics (MD) simulations. The CG model is derived by adapting the Martini model for nonbonded interactions along with the Boltzmann inversion (BI) of bonded interactions from all-atom (AA) simulations. Our derived CG model successfully captures the experimentally observed micellar-, hexagonal-, lamellar-, and polymer-rich solution phase. The end-to-end distance reveals the conformational change from an open-chain structure in the micellar phase to a folded-chain structure in the lamellar phase, increasing the orientational order. An increase in temperature leads to expulsion of water molecules from the L64 moiety, suggesting an increase in L64 hydrophobicity. Thermodynamic analysis using the two-phase thermodynamics (2PT) method suggests the entropy of the system decreases with increasing L64 concentration and the decrease in free energy () with temperature is mainly driven by the entropic factor (-TS). Further, the increase in aggregation number at lower concentrations and self-assembly at very high concentrations is energetically driven, whereas the change from the micellar phase to the lamellar phase with increasing L64 concentration is entropically driven. Our model provides molecular insights into L64 phases which can be further explored to design functionality-based suprastructures for drug delivery and tissue engineering applications.

摘要

我们使用粗粒化(CG)分子动力学(MD)模拟研究了在 300 和 320 K 下,水溶液中聚环氧乙烷 L64 的浓度依赖性相图。CG 模型是通过适应用于非键相互作用的 Martini 模型以及从全原子(AA)模拟的键相互作用的 Boltzmann 反演(BI)得到的。我们推导出的 CG 模型成功地捕获了实验观察到的胶束相、六方相、层状相和富含聚合物的溶液相。末端到末端的距离揭示了从胶束相中的开链结构到层状相中的折叠链结构的构象变化,增加了取向有序性。温度升高会导致水分子从 L64 部分被挤出,表明 L64 的疏水性增加。使用两相热力学(2PT)方法进行的热力学分析表明,随着 L64 浓度的增加,系统的熵减小,随着温度的升高自由能()的减小主要由熵因子(-TS)驱动。此外,在较低浓度下聚集数的增加和在非常高浓度下的自组装是由能量驱动的,而随着 L64 浓度的增加从胶束相转变为层状相则是由熵驱动的。我们的模型提供了对 L64 相的分子见解,可以进一步探索用于药物输送和组织工程应用的基于功能的超结构设计。

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