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Abstract：
De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in computational design strategies, de novo design of protein structures remains challenging, given the vast size of the sequence-structure space. AlphaFold2 (AF2), a state-of-the-art neural network architecture, achieved remarkable accuracy in predicting protein structures from amino acid sequences. This raises the question whether AF2 has learned the principles of protein folding sufficiently for de novo design. Here, we sought to answer this question by inverting the AF2 network, using the prediction weight set and a loss function to bias the generated sequences to adopt a target fold. Initial design trials resulted in de novo designs with an overrepresentation of hydrophobic residues on the protein surface compared to their natural protein family, requiring additional surface optimization. In silico validation of the designs showed protein structures with the correct fold, a hydrophilic surface and a densely packed hydrophobic core. In vitro validation showed that 7 out of 39 designs were folded and stable in solution with high melting temperatures. In summary, our design workflow solely based on AF2 does not seem to fully capture basic principles of de novo protein design, as observed in the protein surface\'s hydrophobic vs. hydrophilic patterning. However, with minimal post-design intervention, these pipelines generated viable sequences as assessed experimental characterization. Thus, such pipelines show the potential to contribute to solving outstanding challenges in de novo protein design.
摘要：
从头蛋白质设计增强了我们对控制蛋白质折叠和相互作用的原理的理解，并有可能通过新型蛋白质功能的工程彻底改变生物技术。尽管计算设计策略最近取得了进展，蛋白质结构的从头设计仍然具有挑战性，考虑到序列结构空间的巨大尺寸。AlphaFold2(AF2),最先进的神经网络架构，在从氨基酸序列预测蛋白质结构方面取得了显著的准确性。这提出了一个问题，即AF2是否已经充分了解了蛋白质折叠的原理以进行从头设计。这里,我们试图通过反转AF2网络来回答这个问题，使用预测权重集和损失函数将生成的序列偏置为采用目标折叠。初步设计试验导致从头设计，与天然蛋白质家族相比，蛋白质表面上的疏水性残基过多。需要额外的表面优化。设计的计算机验证显示蛋白质结构具有正确的折叠，亲水表面和密集堆积的疏水核心。体外验证显示,39种设计中的7种在具有高解链温度的溶液中是折叠和稳定的。总之,我们的设计工作流程仅基于AF2似乎并没有完全捕获从头蛋白设计的基本原理，如在蛋白质表面观察到的疏水性与亲水图案。然而,只需最少的设计后干预，这些管道产生了可行的序列作为评估的实验表征。因此,这样的流水线显示出有助于解决从头蛋白设计中的突出挑战的潜力。本文受版权保护。保留所有权利。
