A plethora of key biological events occur at the cellular membrane where the large spatiotemporal scales necessitate dimensionality reduction or coarse-graining approaches over conventional all-atom molecular dynamics simulation. Constructing coarse-grained descriptions of membranes systematically from statistical mechanical principles has largely remained challenging due to the necessity of capturing amphipathic self-assembling behavior in coarse-grained models. We show that bottom-up coarse-grained lipid models can possess metastable morphological behavior and that this potential metastability has ramifications for accurate development and training. We in turn develop a training algorithm which evades metastability issues by linking model training to self-assembling behavior, and demonstrate its robustness via construction of solvent-free coarse-grained models of various phospholipid membranes, including lipid species such as phosphatidylcholines, phosphatidylserines, sphingolipids, and cholesterol. The resulting coarse-grained lipid models are orders of magnitude faster than their atomistic counterparts while retaining structural fidelity and constitute a promising direction for the development of coarse-grained models of realistic cell membranes.

中文翻译：

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增强自下而上的粗粒磷脂的组装特性。


细胞膜上发生了大量关键生物事件，与传统的全原子分子动力学模拟相比，大时空尺度需要降维或粗粒度方法。由于需要在粗粒模型中捕获两亲性自组装行为，因此从统计力学原理系统地构建膜的粗粒度描述在很大程度上仍然具有挑战性。我们表明，自下而上的粗粒脂质模型可以具有亚稳态形态行为，并且这种潜在的亚稳态对准确的开发和训练有影响。反过来，我们开发了一种训练算法，通过将模型训练与自组装行为联系起来来规避亚稳性问题，并通过构建各种磷脂膜的无溶剂粗粒模型来证明其稳健性，包括磷脂酰胆碱、磷脂酰丝氨酸、鞘脂和胆固醇等脂质物种。所得的粗粒脂质模型比原子脂质模型快几个数量级，同时保持结构保真度，构成了开发真实细胞膜粗粒模型的有前途的方向。