快速参数化的Martini3模型碎片和小分子
Magdalena Szczuka1,2, Gilberto P Pereira3,4,5, Luis J Walter6
1Centre de Intégrative (CBI), Laboratoire de Microbiologie et Génétique Moléculaires (LMGM), Université de Toulouse, CNRS, Toulouse 31400, France.
Journal of chemical theory and computation
|December 20, 2025
概括
自动MartiniM3使用Martini 3力场自动创建小分子粗粒度模型. 这种工具可以更快,高吞吐量模拟分子动力学,提高研究人员的准确性和可访问性.
科学领域:
- 计算化学和生物物理.
- 模拟方法的开发.模拟方法的开发.
背景情况:
- 粗粒度分子动力学 (CGMD) 模拟,如使用马蒂尼3力场的模拟,简化了复杂的分子系统,用于延长时间和系统大小的研究.
- 马丁尼3提供了更大的灵活性,具有更多的珠子类型和大小,促进研究小分子在蛋白质 - 连接体相互作用和膜透等现象中的研究.
- 现有的自动粗粒化工具主要支持较旧的Martini版本,为Martini 3兼容的小分子建模创造了一个空白.
研究的目的:
- 介绍Auto-MartiniM3,一个用于专门为Martini 3力场生成小分子粗粒模型的自动化工具.
- 通过与专家设计的模型和实验数据进行比较,验证Auto-MartiniM3生成模型的准确性和实用性.
- 评估该计划对各种分子系统的粗粒度模型高吞吐量生成的能力.
主要方法:
- 开发和实施Auto-MartiniM3程序,用于自动化粗粒加工.
- 使用81种来自Martini数据库的各种小分子进行验证,比较结构和热力学特性.
- 通过溶液转位和两层脂质之间的自由能量计算来评估模型行为.
- 对复杂分子建模的评估,包括咖啡因与腺A2A受体结合.
主要成果:
- Auto-MartiniM3成功生成了81个小分子的粗粒度模型,与专家定义的模型有很好的一致性.
- 使用Auto-MartiniM3模型的模拟准确地预测了脂质双层中溶液转位和自由能量概况.
- 该工具在建模复杂系统方面表现出有效性,例如咖啡因受体相互作用.
- 对大型数据集的部署证实了该计划在快速,高吞吐量模型生成方面的潜力.
结论:
- Auto-MartiniM3为Martini 3力场提供了一种高效准确的解决方案,用于自动化小分子的粗粒度.
- 该工具显著降低了在涉及小分子的研究中使用Martini 3的障碍,提高了研究可访问性.
- 自动-MartiniM3准备成为计算化学和生物物理学中高通量分子建模的宝贵资源.
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