Codon optimization with deep learning to enhance protein expression

Abstract 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 learni...

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Main Authors:	Hongguang Fu, Yanbing Liang, Xiuqin Zhong, ZhiLing Pan, Lei Huang, HaiLin Zhang, Yang Xu, Wei Zhou, Zhong Liu
Format:	Article
Language:	English
Published:	Nature Publishing Group 2020-10-01
Series:	Scientific Reports
Online Access:	https://doi.org/10.1038/s41598-020-74091-z

id	doaj-099d6ae85f104ec1a497335bc64d5ae2
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spelling	doaj-099d6ae85f104ec1a497335bc64d5ae22020-12-08T10:40:02ZengNature Publishing GroupScientific Reports2045-23222020-10-011011910.1038/s41598-020-74091-zCodon optimization with deep learning to enhance protein expressionHongguang Fu0Yanbing Liang1Xiuqin Zhong2ZhiLing Pan3Lei Huang4HaiLin Zhang5Yang Xu6Wei Zhou7Zhong Liu8University of Electronic Science and Technology of ChinaUniversity of Electronic Science and Technology of ChinaUniversity of Electronic Science and Technology of ChinaState Key Laboratory of Biotherapy, West China Hospital, Sichuan UniversityUniversity of Electronic Science and Technology of ChinaState Key Laboratory of Biotherapy, West China Hospital, Sichuan UniversityUniversity of Electronic Science and Technology of ChinaUniversity of Electronic Science and Technology of ChinaChengdu Institute of Computer Applications, Chinese Academy of SciencesAbstract 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 for Escherichia Coli were 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 for plasmodium falciparum candidate 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.https://doi.org/10.1038/s41598-020-74091-z
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Hongguang Fu Yanbing Liang Xiuqin Zhong ZhiLing Pan Lei Huang HaiLin Zhang Yang Xu Wei Zhou Zhong Liu
spellingShingle	Hongguang Fu Yanbing Liang Xiuqin Zhong ZhiLing Pan Lei Huang HaiLin Zhang Yang Xu Wei Zhou Zhong Liu Codon optimization with deep learning to enhance protein expression Scientific Reports
author_facet	Hongguang Fu Yanbing Liang Xiuqin Zhong ZhiLing Pan Lei Huang HaiLin Zhang Yang Xu Wei Zhou Zhong Liu
author_sort	Hongguang Fu
title	Codon optimization with deep learning to enhance protein expression
title_short	Codon optimization with deep learning to enhance protein expression
title_full	Codon optimization with deep learning to enhance protein expression
title_fullStr	Codon optimization with deep learning to enhance protein expression
title_full_unstemmed	Codon optimization with deep learning to enhance protein expression
title_sort	codon optimization with deep learning to enhance protein expression
publisher	Nature Publishing Group
series	Scientific Reports
issn	2045-2322
publishDate	2020-10-01
description	Abstract 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 for Escherichia Coli were 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 for plasmodium falciparum candidate 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.
url	https://doi.org/10.1038/s41598-020-74091-z
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Codon optimization with deep learning to enhance protein expression

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