In biological systems, proteins can be attracted to curved or stretched regions of lipid bilayers by sensing hydrophobic defects in the lipid packing on the membrane surface. Here, we present an efficient end-state free energy calculation method to quantify such sensing in molecular dynamics simulations. We illustrate that lipid packing defect sensing can be defined as the difference in mechanical work required to stretch a membrane with and without a peptide bound to the surface. We also demonstrate that a peptide’s ability to concurrently induce excess leaflet area (tension) and elastic softening─a property we call the “characteristic area of sensing” (CHAOS)─and lipid packing sensing behavior are in fact two sides of the same coin. In essence, defect sensing displays a peptide’s propensity to generate tension. The here-proposed mechanical pathway is equally accurate yet, computationally, about 40 times less costly than the commonly used alchemical pathway (thermodynamic integration), allowing for more feasible free energy calculations in atomistic simulations. This enabled us to directly compare the Martini 2 and 3 coarse-grained and the CHARMM36 atomistic force fields in terms of relative binding free energies for six representative peptides including the curvature sensor ALPS and two antiviral amphipathic helices (AH). We observed that Martini 3 qualitatively reproduces experimental trends while producing substantially lower (relative) binding free energies and shallower membrane insertion depths compared to atomistic simulations. In contrast, Martini 2 tends to overestimate (relative) binding free energies. Finally, we offer a glimpse into how our end-state-based free energy method can enable the inverse design of optimal lipid packing defect sensing peptides when used in conjunction with our recently developed evolutionary molecular dynamics (Evo-MD) method. We argue that these optimized defect sensors─aside from their biomedical and biophysical relevance─can provide valuable targets for the development of lipid force fields.

中文翻译：

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两亲肽对脂质包装缺陷传感的有效量化：将 Martini 2 和 3 与 CHARMM36 进行比较

在生物系统中，蛋白质可以通过检测膜表面脂质堆积中的疏水缺陷被吸引到脂质双层的弯曲或拉伸区域。在这里，我们提出了一种有效的终态自由能计算方法来量化分子动力学模拟中的这种传感。我们说明脂质包装缺陷传感可以定义为拉伸膜所需的机械功的差异，无论是否有肽结合到表面。我们还证明了肽同时诱导过度小叶面积（张力）和弹性软化的能力——我们称之为“感知特征区域”（CHAOS）的特性——和脂质堆积感知行为实际上是同一枚硬币的两个方面。本质上，缺陷感应显示肽产生张力的倾向。这里提出的机械路径同样准确，但在计算上，比常用的炼金术路径（热力学积分）成本低约 40 倍，允许在原子模拟中进行更可行的自由能计算。这使我们能够直接比较 Martini 2 和 3 粗粒力场和 CHARMM36 原子力场，以包括曲率传感器 ALPS 和两个抗病毒两亲螺旋 (AH) 在内的六种代表性肽的相对结合自由能。我们观察到，与原子模拟相比，Martini 3 定性地再现了实验趋势，同时产生了显着更低（相对）的结合自由能和更浅的膜插入深度。相反，Martini 2 倾向于高估（相对）结合自由能。最后，当与我们最近开发的进化分子动力学 (Evo-MD) 方法结合使用时，我们提供了我们基于末端状态的自由能方法如何能够实现最佳脂质包装缺陷传感肽的逆向设计的一瞥。我们认为，这些优化的缺陷传感器——除了它们的生物医学和生物物理相关性——可以为脂质力场的发展提供有价值的目标。