Rotamer-free protein sequence design based on deep learning and self-consistency
Rotamer-free protein sequence design based on deep learning and self-consistency
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DOI：
10.1038/s43588-022-00273-6
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发表时间：
2022-07-01
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影响因子：
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通讯作者：
Liu, Haiyan
中科院分区：
文献类型：
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作者：
Liu, Yufeng;Zhang, Lu;Liu, Haiyan
先前提出的几种深度学习方法用于设计自主折叠成给定蛋白质骨架的氨基酸序列，在计算测试中产生了有希望的结果，但在湿实验中并没有优于传统的基于能量函数的方法。在这里，我们提出了ABACUS-R方法，该方法使用编码器-解码器网络，该网络使用多任务学习策略进行训练，以从其三维局部环境中预测中心残基的侧链类型，该三维局部环境除了其他特征之外还包括周围侧链的类型但不包括构象。这消除了重建和优化侧链结构的需要，并大大简化了序列设计过程。因此，迭代地将编码器-解码器应用于不同的中心残基能够产生靶骨架的自洽的整体序列。湿实验的结果，包括通过X射线晶体学解决的五个结构，表明ABACUS-R在成功率和设计精度方面优于最先进的基于能量函数的方法。
Several previously proposed deep learning methods to design amino acid sequences that autonomously fold into a given protein backbone yielded promising results in computational tests but did not outperform conventional energy function-based methods in wet experiments. Here we present the ABACUS-R method, which uses an encoder-decoder network trained using a multitask learning strategy to predict the sidechain type of a central residue from its three-dimensional local environment, which includes, besides other features, the types but not the conformations of the surrounding sidechains. This eliminates the need to reconstruct and optimize sidechain structures, and drastically simplifies the sequence design process. Thus iteratively applying the encoder-decoder to different central residues is able to produce self-consistent overall sequences for a target backbone. Results of wet experiments, including five structures solved by X-ray crystallography, show that ABACUS-R outperforms state-of-the-art energy function-based methods in success rate and design precision.