分子信息现场理论用于估计天然无序蛋白表面活性剂的临界胶束浓度。
Molecularly informed field theory for estimating critical micelle concentrations of intrinsically disordered protein surfactants.
机构信息
Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA.
Department of Chemistry, University of California, Berkeley, California 94720, USA.
出版信息
J Chem Phys. 2023 Dec 28;159(24). doi: 10.1063/5.0178910.
The critical micelle concentration (CMC) is a crucial parameter in understanding the self-assembly behavior of surfactants. In this study, we combine simulation and experiment to demonstrate the predictive capability of molecularly informed field theories in estimating the CMC of biologically based protein surfactants. Our simulation approach combines the relative entropy coarse-graining of small-scale atomistic simulations with large-scale field-theoretic simulations, allowing us to efficiently compute the free energy of micelle formation necessary for the CMC calculation while preserving chemistry-specific information about the underlying surfactant building blocks. We apply this methodology to a unique intrinsically disordered protein platform capable of a wide variety of tailored sequences that enable tunable micelle self-assembly. The computational predictions of the CMC closely match experimental measurements, demonstrating the potential of molecularly informed field theories as a valuable tool to investigate self-assembly in bio-based macromolecules systematically.
临界胶束浓度 (CMC) 是理解表面活性剂自组装行为的关键参数。在这项研究中，我们结合模拟和实验，展示了分子信息场论在预测生物基蛋白表面活性剂 CMC 方面的预测能力。我们的模拟方法结合了小尺度原子模拟的相对熵粗粒化和大尺度场论模拟，使我们能够高效地计算出形成胶束所需的自由能，从而计算 CMC，同时保留关于潜在表面活性剂构建块的特定于化学的信息。我们将这种方法应用于一种独特的、本质上无序的蛋白质平台，该平台能够拥有各种各样的定制序列，从而实现可调的胶束自组装。CMC 的计算预测与实验测量非常吻合，这表明分子信息场论作为一种有价值的工具，有潜力系统地研究生物大分子中的自组装行为。
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