Functional Hydrogen-Bonded Self-Sorting Networks

在生物学中，多个过程同时发生，以执行生命所必需的功能，例如制造和破坏细胞。要做到这一点，由几个不同的组件或构建块(例如蛋白质)组成的多组件体系结构必须在正确的时间和正确的地点以精确的控制进行组装。这种多组件体系结构可能还需要在另一个时间拆卸和重新组装成具有不同功能的新体系结构。因此，这些系统可以被视为以环境响应的方式形成不同功能架构的网络。使这种体系结构的组装和功能成为可能的是自分类--一个组件能够在许多其他组件存在的情况下识别其正确的合作伙伴组件(S)。合成化学还不能模仿功能性的自我分类网络。这项研究将解决这一挑战，并开发出第一个非自然(合成)功能的自分类网络。我们将使用氢键基序(HBMS)模式来构建自我排序网络，这种模式模仿用于存储遗传信息的DNA碱基对。然后，我们将使用这些HBMS来控制一系列催化和迭代的合成化学过程。从长远来看，这种方法可以用来制备具有工业重要性的精细化学品和药品，其方式类似于现代制造业中使用的装配线，在复杂性和效率方面具有优势。

In biology, multiple processes occur simultaneously to perform the functions necessary for life e.g. manufacture and destruction of cells. To do this, multicomponent architectures comprising several different components or building blocks (e.g. proteins) must assemble with exquisite control at the right time and in the right place. Such multiple component architectures may also need to disassemble and reassemble into new architectures with different function at another time. These systems can therefore be considered as networks that form different functional architectures in an environmentally responsive manner. What makes the assembly and function of such architectures possible is self-sorting - the ability of one component to recognise its correct partner component(s) in the presence of many others. Synthetic chemistry is not yet capable of mimicking functional self-sorting networks. This research will address this challenge and develop the first non-natural (synthetic) functional self-sorting networks. We will use patterns of hydrogen-bonding motifs (HBMs), which mimic the DNA base pairs used to store genetic information, to construct self-sorting networks. We will then use these HBMS to control a series of catalytic and iterative synthetic chemistry processes. In the long term such methods could be used to prepare industrially important fine chemicals and pharmaceuticals in a similar manner to assembly lines used in modern manufacturing, offering advantages in complexity and efficiency.