Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
Curr Opin Struct Biol. 2021 Jun;68:194-207. doi: 10.1016/j.sbi.2021.01.007. Epub 2021 Feb 24.
Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the past two decades, the recent application of deep neural networks to spatial constraint prediction and end-to-end model training has significantly improved the accuracy of protein structure prediction, largely solving the problem at the fold level for single-domain proteins. The field of protein design has also witnessed dramatic improvement, where noticeable examples have shown that information stored in neural-network models can be used to advance functional protein design. Thus, incorporation of deep learning techniques into different steps of protein folding and design approaches represents an exciting future direction and should continue to have a transformative impact on both fields.
蛋白质结构预测和设计可以被视为受相同折叠原理控制的两个相反过程。尽管在过去的二十年中进展停滞不前，但最近将深度神经网络应用于空间约束预测和端到端模型训练极大地提高了蛋白质结构预测的准确性，在很大程度上解决了单域蛋白质的折叠水平问题。蛋白质设计领域也取得了显著的进展，其中一些显著的例子表明，神经网络模型中存储的信息可以用于推进功能蛋白质设计。因此，将深度学习技术纳入蛋白质折叠和设计方法的不同步骤代表了一个令人兴奋的未来方向，应该继续对这两个领域产生变革性的影响。
