Efforts to engineer high-performance protein-based materials inspired by nature have mostly focused on altering naturally occurring sequences to confer the desired functionalities, whereas de novo design lags significantly behind and calls for unconventional innovative approaches. Here, using partially disordered elastin-like polypeptides (ELPs) as initial building blocks this work shows that de novo engineering of protein materials can be accelerated through hybrid biomimetic design, which this work achieves by integrating computational modeling, deep neural network, and recombinant DNA technology. This generalizable approach involves incorporating a series of de novo-designed sequences with α-helical conformation and genetically encoding them into biologically inspired intrinsically disordered repeating motifs. The new ELP variants maintain structural conformation and showed tunable supramolecular self-assembly out of thermal equilibrium with phase behavior in vitro. This work illustrates the effective translation of the predicted molecular designs in structural and functional materials. The proposed methodology can be applied to a broad range of partially disordered biomacromolecules and potentially pave the way toward the discovery of novel structural proteins.

中文翻译：

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通过混合仿生-从头预测分子设计加速基于 ELP 的材料工程

受大自然启发，设计高性能蛋白质材料的努力主要集中在改变天然存在的序列以赋予所需的功能，而从头设计明显落后，需要非常规的创新方法。在这里，使用部分无序弹性蛋白样多肽（ELP）作为初始构建块，这项工作表明，可以通过混合仿生设计加速蛋白质材料的从头工程，这项工作通过集成计算模型、深度神经网络和重组 DNA 来实现这一目标技术。这种可推广的方法涉及将一系列从头设计的具有 α 螺旋构象的序列结合起来，并将它们基因编码成受生物学启发的本质上无序的重复基序。新的 ELP 变体保持结构构象，并在体外表现出热平衡外的可调节超分子自组装和相行为。这项工作说明了预测的分子设计在结构和功能材料中的有效转化。所提出的方法可应用于广泛的部分无序生物大分子，并可能为发现新型结构蛋白铺平道路。