Presentation Information
[P01-111]Development of translation-enhancing pepteides to boost protein production in microorganisms
○Teruyo Ojima-Kato1, Khairil Anwar1, Gentaro Yokoyama2,3, Chie Motono2, Hideo Nakano1, Michiaki Hamada2,3 (1. Nagoya University (Japan), 2. National Institute of Advanced Industrial Science and Technology (Japan), 3. Waseda University (Japan))
Keywords:
protein,Escherichia coli,Bacillus subtilis,translation,ribosome
[Purpose]
Efficient protein production in microorganisms is essential for advancing sustainable biomanufacturing and modern life sciences. However, many recombinant proteins are difficult to express at high levels in microbial hosts due to limitations in transcription and translation.
In this study, we focused on developing short translation-enhancing peptides (TEPs) that can be fused to target proteins to boost their production by enhancing translation efficiency.
[Method]
Using artificially randomized tetrapeptide libraries, we screened TEPs with varying strengths in Escherichia coli and Bacillus subtilis through systematic and quantitative analysis of translation efficiency. For TEPs identified in E. coli, we further constructed a machine-learning model to predict novel TEP sequences that were not obtained experimentally.
[Results]
We successfully identified TEPs with varying strengths in E. coli and B. subtilis. Application of these TEPs to several recombinant proteins, including eukaryotic proteins, resulted in substantial increases in expression levels, with some cases showing dramatic improvements compared to controls.
In B. subtilis, mRNA levels were reduced while protein yields increased, which is consistent with our previous results in E. coli, suggesting that TEPs primarily enhance translation efficiency rather than transcription. In addition, the machine-learning model successfully predicted novel TEP sequences that improved protein production, demonstrating its potential as a design tool.
[Consideration]
These results suggest that short nascent peptide sequences can modulate translation efficiency, likely by affecting ribosome behavior. One possible explanation is the alleviation of translational bottlenecks, such as ribosome stalling. Although the precise mechanism remains unclear, the consistent effects observed across different proteins and host organisms indicate that TEP-mediated enhancement is broadly applicable.
[Conclusion]
In this study, we developed translation-enhancing peptides (TEPs) that effectively improve protein production in microbial systems. The combination of library-based screening and machine-learning–assisted design enables efficient identification of functional TEPs.
This approach provides a simple and versatile strategy for enhancing recombinant protein production and has strong potential for applications in metabolic engineering and sustainable biomanufacturing.
Efficient protein production in microorganisms is essential for advancing sustainable biomanufacturing and modern life sciences. However, many recombinant proteins are difficult to express at high levels in microbial hosts due to limitations in transcription and translation.
In this study, we focused on developing short translation-enhancing peptides (TEPs) that can be fused to target proteins to boost their production by enhancing translation efficiency.
[Method]
Using artificially randomized tetrapeptide libraries, we screened TEPs with varying strengths in Escherichia coli and Bacillus subtilis through systematic and quantitative analysis of translation efficiency. For TEPs identified in E. coli, we further constructed a machine-learning model to predict novel TEP sequences that were not obtained experimentally.
[Results]
We successfully identified TEPs with varying strengths in E. coli and B. subtilis. Application of these TEPs to several recombinant proteins, including eukaryotic proteins, resulted in substantial increases in expression levels, with some cases showing dramatic improvements compared to controls.
In B. subtilis, mRNA levels were reduced while protein yields increased, which is consistent with our previous results in E. coli, suggesting that TEPs primarily enhance translation efficiency rather than transcription. In addition, the machine-learning model successfully predicted novel TEP sequences that improved protein production, demonstrating its potential as a design tool.
[Consideration]
These results suggest that short nascent peptide sequences can modulate translation efficiency, likely by affecting ribosome behavior. One possible explanation is the alleviation of translational bottlenecks, such as ribosome stalling. Although the precise mechanism remains unclear, the consistent effects observed across different proteins and host organisms indicate that TEP-mediated enhancement is broadly applicable.
[Conclusion]
In this study, we developed translation-enhancing peptides (TEPs) that effectively improve protein production in microbial systems. The combination of library-based screening and machine-learning–assisted design enables efficient identification of functional TEPs.
This approach provides a simple and versatile strategy for enhancing recombinant protein production and has strong potential for applications in metabolic engineering and sustainable biomanufacturing.
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