In living cells, proteins self-assemble into large functional structures based on specific interactions between molecularly complex patches. Because of this complexity, protein self-assembly results from a competition between a large number of distinct interaction energies, of the order of one per pair of patches. However, current self-assembly models typically ignore this aspect, and the principles by which it determines the large-scale structure of protein assemblies are largely unknown. Here, we use Monte Carlo simulations and machine learning to start to unravel these principles. We observe that despite widespread geometrical frustration, aggregates of particles with complex interactions fall within only a few categories that often display high degrees of spatial order, including crystals, fibers, and oligomers. We then successfully identify the most relevant aspect of the interaction complexity in predicting these outcomes, namely, the particles’ ability to form periodic structures. Our results provide a first extensive characterization of the rich design space associated with identical particles with complex interactions and could inspire engineered self-assembling nano-objects as well as help us to understand the emergence of robust functional protein structures. Published by the American Physical Society 2024

中文翻译：

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具有复杂相互作用的粒子如何自组装？


在活细胞中，蛋白质根据分子复杂片段之间的特定相互作用自组装成大型功能结构。由于这种复杂性，蛋白质自组装是大量不同相互作用能之间竞争的结果，每对补丁一个量级。然而，目前的自组装模型通常忽略了这一方面，并且它决定蛋白质组装大规模结构的原理在很大程度上是未知的。在这里，我们使用 Monte Carlo 模拟和机器学习来开始解开这些原则。我们观察到，尽管存在普遍的几何挫折，但具有复杂相互作用的粒子聚集体仅属于通常表现出高度空间秩序的少数类别，包括晶体、纤维和低聚物。然后，我们成功地确定了在预测这些结果时相互作用复杂性最相关的方面，即粒子形成周期性结构的能力。我们的结果首次提供了与具有复杂相互作用的相同粒子相关的丰富设计空间的广泛表征，并可能激发工程自组装纳米物体的灵感，并帮助我们了解稳健功能性蛋白质结构的出现。 美国物理学会 2024 年出版