MPEPE, a predictive approach to improve protein expression in E. coli based on deep learning

Zundan Ding, Feifei Guan, Guoshun Xu, Yuchen Wang, Yaru Yan, Wei Zhang, Ningfeng Wu, Bin Yao, Huoqing Huang, Tamir Tuller, Jian Tian

Research output: Contribution to journalArticlepeer-review

Abstract

The expression of proteins in Escherichia coli is often essential for their characterization, modification, and subsequent application. Gene sequence is the major factor contributing expression. In this study, we used the expression data from 6438 heterologous proteins under the same expression condition in E. coli to construct a deep learning classifier for screening high- and low-expression proteins. In conjunction with conserved residue analysis to minimize functional disruption, a mutation predictor for enhanced protein expression (MPEPE) was proposed to identify mutations conducive to protein expression. MPEPE identified mutation sites in laccase 13B22 and the glucose dehydrogenase FAD-AtGDH, that significantly increased both expression levels and activity of these proteins. Additionally, a significant correlation of 0.46 between the predicted high level expression propensity with the constructed models and the protein abundance of endogenous genes in E. coli was also been detected. Therefore, the study provides foundational insights into the relationship between specific amino acid usage, codon usage, and protein expression, and is essential for research and industrial applications.

Original languageEnglish
Pages (from-to)1142-1153
Number of pages12
JournalComputational and Structural Biotechnology Journal
Volume20
DOIs
StatePublished - Jan 2022

Keywords

  • Deep learning
  • MPEPE
  • Mutation
  • Protein expression

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