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

Abstract

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