Wang Yiwei, Zou Rongrong, Zhou Yeqiang, Zheng Yi, Peng Chuan, Liu Yang, Tan Hong, Fu Qiang, Ding Mingming
College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University Chengdu 610065 China
Chem Sci. 2024 Jul 29;15(33):13442-13451. doi: 10.1039/d4sc03061c. eCollection 2024 Aug 22.
Coacervates play a pivotal role in protein-based drug delivery research, yet their drug encapsulation and release mechanisms remain poorly understood. Here, we utilized the Martini model to investigate bovine serum albumin (BSA) protein encapsulation and release within polylysine/polyglutamate (PLys/PGlu) coacervates. Our findings emphasize the importance of ingredient addition sequence in coacervate formation and encapsulation rates, attributed to preference contact between oppositely charged proteins and poly(amino acid)s. Notably, coacervates composed of β-sheet poly(amino acid)s demonstrate greater BSA encapsulation efficiency due to their reduced entropy and flexibility. Furthermore, we examined the pH responsiveness of coacervates, shedding light on the dissolution process driven by Coulomb forces. By leveraging machine learning algorithms to analyze simulation results, our research advances the understanding of coacervate-based drug delivery systems, with the ultimate goal of optimizing therapeutic outcomes.
凝聚层在基于蛋白质的药物递送研究中起着关键作用，但其药物包封和释放机制仍知之甚少。在此，我们利用Martini模型研究了聚赖氨酸/聚谷氨酸（PLys/PGlu）凝聚层中牛血清白蛋白（BSA）的包封和释放。我们的研究结果强调了成分添加顺序在凝聚层形成和包封率中的重要性，这归因于带相反电荷的蛋白质和聚氨基酸之间的优先接触。值得注意的是，由β-折叠聚氨基酸组成的凝聚层由于其降低的熵和灵活性，表现出更高的BSA包封效率。此外，我们研究了凝聚层的pH响应性，揭示了由库仑力驱动的溶解过程。通过利用机器学习算法分析模拟结果，我们的研究推动了对基于凝聚层的药物递送系统的理解，最终目标是优化治疗效果。
