Unified rational protein engineering with sequence-based deep representation learning
Unified rational protein engineering with sequence-based deep representation learning
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DOI：
10.1038/s41592-019-0598-1
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发表时间：
2019-12-01
期刊：
影响因子：
48
通讯作者：
Church, George M.
中科院分区：
文献类型：
--
作者：
Alley, Ethan C.;Khimulya, Grigory;Church, George M.
理性蛋白质工程需要对蛋白质功能有整体理解。在这里，我们将深度学习应用于未标记的氨基酸序列，以将蛋白质的基本特征提炼成统计代表，该统计表示在语义上富含且结构上，进化和生物物理上的基础。我们表明，在此统一表示（UNIREP）之上构建的最简单模型广泛适用，并且可以概括为未见的序列空间区域。我们的数据驱动方法可以预测天然和从头设计的蛋白质的稳定性，以及分子多样性突变体的定量功能，与最先进的方法竞争。 Unirep进一步实现了蛋白质工程任务的两个数量级效率提高。 Unirep是可以在蛋白质工程信息学上应用的基本蛋白质特征的多功能摘要。
Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded. We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space. Our data-driven approach predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-the-art methods. UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task. UniRep is a versatile summary of fundamental protein features that can be applied across protein engineering informatics.