Codon optimization with deep learning to enhance protein expression.
Codon optimization with deep learning to enhance protein expression.
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通过深度学习优化密码子以增强蛋白质表达
DOI：
10.1038/s41598-020-74091-z
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发表时间：
2020-10-19
影响因子：
4.6
通讯作者：
Liu Z
中科院分区：
文献类型：
--
作者：
Fu H;Liang Y;Zhong X;Pan Z;Huang L;Zhang H;Xu Y;Zhou W;Liu Z
异源表达是重组蛋白质产生的合成的主要方法，对于本文，现有的优化方法是基于生物学指数的密码子框的概念可以通过将DNA序列重新编码为密码子序列，而忽略了碱基的顺序。通过双向长期记忆条件随机场来训练与大肠杆菌的密码子优化模型的相应氨基酸的序列注释。除了比较密码子适应指数外，恶性疟原虫候选疫苗和聚合酶酸性蛋白的蛋白质表达实验它们是与原始序列和Genewiz和Thermofisher的优化序列进行比较的。
Heterologous expression is the main approach for recombinant protein production ingenetic synthesis, for which codon optimization is necessary. The existing optimization methods are based on biological indexes. In this paper, we propose a novel codon optimization method based on deep learning. First, we introduce the concept of codon boxes, via which DNA sequences can be recoded into codon box sequences while ignoring the order of bases. Then, the problem of codon optimization can be converted to sequence annotation of corresponding amino acids with codon boxes. The codon optimization models forEscherichia Coliwere trained by the Bidirectional Long-Short-Term Memory Conditional Random Field. Theoretically, deep learning is a good method to obtain the distribution characteristics of DNA. In addition to the comparison of the codon adaptation index, protein expression experiments forplasmodium falciparumcandidate vaccine and polymerase acidic protein were implemented for comparison with the original sequences and the optimized sequences from Genewiz and ThermoFisher. The results show that our method for enhancing protein expression is efficient and competitive.
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