Department of Physics, IIT Madras, Chennai, India.
Soft Matter. 2019 Feb 27;15(9):2071-2080. doi: 10.1039/c8sm02623h.
The ability of proteins to sense and/or generate membrane curvature is crucial for many biological processes inside the cell. We introduce a model for the binding and unbinding of curvature inducing proteins on vesicles using Dynamic Triangulation Monte Carlo (DTMC) simulations. In our study, the interaction between membrane curvature and protein binding is characterised by the binding affinity parameter μ, which indicates the interaction strength. We demonstrate that both sensing and generation of curvature can be observed in the same system as a function of the protein binding affinity on the membrane. Our results show that at low μ values, proteins only sense membrane curvature, whereas at high μ values, they induce curvature. The transition between sensing and generation regimes is marked by a sharp change in the μ-dependence of the protein bound fraction. We present ways to quantitatively characterise these two regimes. We also observe that imposing tension on the membrane (through internal excess pressure for liposomes) extends the region of curvature sensing in the parameter space.
蛋白质感知和/或产生膜曲率的能力对于细胞内的许多生物过程至关重要。我们使用动态三角化蒙特卡罗（DTMC）模拟为囊泡上的曲率诱导蛋白的结合和解离引入了一个模型。在我们的研究中，膜曲率和蛋白结合之间的相互作用由结合亲和力参数 μ 来描述，μ 表示相互作用强度。我们证明，作为膜上蛋白结合亲和力的函数，可以在同一系统中观察到曲率的感应和产生。我们的结果表明，在低 μ 值时，蛋白质只能感应膜曲率，而在高 μ 值时，它们会诱导曲率。在感应和产生两种状态之间的转变由蛋白结合分数对 μ 的依赖性的急剧变化来标记。我们提出了定量描述这两种状态的方法。我们还观察到，对膜施加张力（通过脂质体内部的过剩压力）会扩大曲率感应区域在参数空间中的范围。
