Benchmark of Coacervate Formation and Mechanism Exploration Using the Martini Force Field DOI

Rongrong Zou,

Yiwei Wang, Xiu Zhang

et al.

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 25, 2025

Peptide-based coacervates are crucial for drug delivery due to their biocompatibility, versatility, high loading capacity, and cell penetration rates; however, stability mechanism phase behavior not fully understood. Additionally, although Martini is one of the most famous force fields capable describing coacervate formation with molecular details, a comprehensive benchmark its accuracy has been conducted. This research utilized 3.0 field machine learning algorithms explore representative peptide-based coacervates, including those composed polyaspartate (PAsp)/polyarginine (PArg), rmfp-1, sticker-and-spacer small molecules, HBpep molecules. We identified key driving forces such as Coulomb, cation–π, π–π interactions established three criteria determining in simulations. The results also indicate that while accurately captures trends, it tends underestimate Coulomb overestimate interactions. What more, our study on encapsulation derivative suggested loaded drugs were distributed surfaces clusters, awaiting experimental validation. employs simulation enhance understanding mechanisms benchmarking 3.0, thereby providing fundamental insights future investigations.

Language: Английский

Benchmark of Coacervate Formation and Mechanism Exploration Using the Martini Force Field DOI

Rongrong Zou,

Yiwei Wang, Xiu Zhang

et al.

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 25, 2025

Peptide-based coacervates are crucial for drug delivery due to their biocompatibility, versatility, high loading capacity, and cell penetration rates; however, stability mechanism phase behavior not fully understood. Additionally, although Martini is one of the most famous force fields capable describing coacervate formation with molecular details, a comprehensive benchmark its accuracy has been conducted. This research utilized 3.0 field machine learning algorithms explore representative peptide-based coacervates, including those composed polyaspartate (PAsp)/polyarginine (PArg), rmfp-1, sticker-and-spacer small molecules, HBpep molecules. We identified key driving forces such as Coulomb, cation–π, π–π interactions established three criteria determining in simulations. The results also indicate that while accurately captures trends, it tends underestimate Coulomb overestimate interactions. What more, our study on encapsulation derivative suggested loaded drugs were distributed surfaces clusters, awaiting experimental validation. employs simulation enhance understanding mechanisms benchmarking 3.0, thereby providing fundamental insights future investigations.

Language: Английский

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