Machine learning-assisted rational design and evolution of novel signal peptides in Yarrowia lipolytica
Microbial proteins hold great promise as sustainable alternatives for future protein sources, and oleaginous yeast Yarrowia lipolytica has emerged as a recognized platform for heterologous protein expression and secretion. N-terminal signal peptides (SPs) are crucial for directing proteins to the se...
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KeAi Communications Co., Ltd.
2025-12-01
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| Series: | Synthetic and Systems Biotechnology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405805X2500105X |
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| author | Zizhao Wu Wenhao Chen Yuxiang Hong Yongkai Wang Peng Xu |
| author_facet | Zizhao Wu Wenhao Chen Yuxiang Hong Yongkai Wang Peng Xu |
| author_sort | Zizhao Wu |
| collection | DOAJ |
| description | Microbial proteins hold great promise as sustainable alternatives for future protein sources, and oleaginous yeast Yarrowia lipolytica has emerged as a recognized platform for heterologous protein expression and secretion. N-terminal signal peptides (SPs) are crucial for directing proteins to the secretion pathway, which offers advantages in both academic and industrial protein production. Although some of the innate SPs of Y. lipolytica have been reported, there is a growing need to expand the genetic toolkit of SPs to support the increasing use of Y. lipolytica as a cell factory for overproduction of various secretory proteins. In this study, we employed an efficient evolutionary approach to rapidly evolve the innate SP XPR2-pre by leveraging Gibson assembly with two synthetic overlapping oligos containing high portion of degenerate nucleotides. Using Nanoluc (Nluc) luciferase as a robust reporter, we characterized the intracellular and extracellular enzymatic activity of 447 SP mutants and identified previously undescribed SPs exhibiting superior performance compared to XPR2-pre in Nluc luciferase secretion, with improvements of up to 2.91-fold of enzymatic activity in the supernatant. The generalizability of the top-performing SPs was evaluated using three additional heterologous enzymes (β-galactosidase, α-amylase, and PET hydrolase). Our results confirmed their versatility across different proteins with protein-specific efficiency. Additionally, based on our screening, we also evaluated the performance of different feature engineering strategies and machine learning models in the design and prediction of SP mutants. This study integrated rational design, directed evolution and machine learning to identify novel SPs, expanding the repertoire of signal peptides and benefiting secretory protein overexpression in Y. lipolytica. |
| format | Article |
| id | doaj-art-d6f445e66ad54febbfbe0e5012aea806 |
| institution | Kabale University |
| issn | 2405-805X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Synthetic and Systems Biotechnology |
| spelling | doaj-art-d6f445e66ad54febbfbe0e5012aea8062025-08-20T03:40:33ZengKeAi Communications Co., Ltd.Synthetic and Systems Biotechnology2405-805X2025-12-011041275128310.1016/j.synbio.2025.07.008Machine learning-assisted rational design and evolution of novel signal peptides in Yarrowia lipolyticaZizhao Wu0Wenhao Chen1Yuxiang Hong2Yongkai Wang3Peng Xu4Department of Chemical Engineering, Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion (MATEC), Guangdong Technion – Israel Institute of Technology, Shantou, 515063, China; The Wolfson Department of Chemical Engineering, Technion – Israel Institute of Technology, Haifa, 3200003, IsraelDepartment of Chemical Engineering, Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion (MATEC), Guangdong Technion – Israel Institute of Technology, Shantou, 515063, ChinaDepartment of Chemical Engineering, Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion (MATEC), Guangdong Technion – Israel Institute of Technology, Shantou, 515063, ChinaDepartment of Chemical Engineering, Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion (MATEC), Guangdong Technion – Israel Institute of Technology, Shantou, 515063, ChinaDepartment of Chemical Engineering, Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion (MATEC), Guangdong Technion – Israel Institute of Technology, Shantou, 515063, China; Center for Lipid Engineering, Muyuan Laboratory, Zhengzhou, 450016, Henan, China; Corresponding author. Department of Chemical Engineering, Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion (MATEC), Guangdong Technion – Israel Institute of Technology, Shantou, 515063, China.Microbial proteins hold great promise as sustainable alternatives for future protein sources, and oleaginous yeast Yarrowia lipolytica has emerged as a recognized platform for heterologous protein expression and secretion. N-terminal signal peptides (SPs) are crucial for directing proteins to the secretion pathway, which offers advantages in both academic and industrial protein production. Although some of the innate SPs of Y. lipolytica have been reported, there is a growing need to expand the genetic toolkit of SPs to support the increasing use of Y. lipolytica as a cell factory for overproduction of various secretory proteins. In this study, we employed an efficient evolutionary approach to rapidly evolve the innate SP XPR2-pre by leveraging Gibson assembly with two synthetic overlapping oligos containing high portion of degenerate nucleotides. Using Nanoluc (Nluc) luciferase as a robust reporter, we characterized the intracellular and extracellular enzymatic activity of 447 SP mutants and identified previously undescribed SPs exhibiting superior performance compared to XPR2-pre in Nluc luciferase secretion, with improvements of up to 2.91-fold of enzymatic activity in the supernatant. The generalizability of the top-performing SPs was evaluated using three additional heterologous enzymes (β-galactosidase, α-amylase, and PET hydrolase). Our results confirmed their versatility across different proteins with protein-specific efficiency. Additionally, based on our screening, we also evaluated the performance of different feature engineering strategies and machine learning models in the design and prediction of SP mutants. This study integrated rational design, directed evolution and machine learning to identify novel SPs, expanding the repertoire of signal peptides and benefiting secretory protein overexpression in Y. lipolytica.http://www.sciencedirect.com/science/article/pii/S2405805X2500105XSiganl peptidesDirected evolutionRational DesignMachine learningYarrowia lipolyticaProtein secretion |
| spellingShingle | Zizhao Wu Wenhao Chen Yuxiang Hong Yongkai Wang Peng Xu Machine learning-assisted rational design and evolution of novel signal peptides in Yarrowia lipolytica Synthetic and Systems Biotechnology Siganl peptides Directed evolution Rational Design Machine learning Yarrowia lipolytica Protein secretion |
| title | Machine learning-assisted rational design and evolution of novel signal peptides in Yarrowia lipolytica |
| title_full | Machine learning-assisted rational design and evolution of novel signal peptides in Yarrowia lipolytica |
| title_fullStr | Machine learning-assisted rational design and evolution of novel signal peptides in Yarrowia lipolytica |
| title_full_unstemmed | Machine learning-assisted rational design and evolution of novel signal peptides in Yarrowia lipolytica |
| title_short | Machine learning-assisted rational design and evolution of novel signal peptides in Yarrowia lipolytica |
| title_sort | machine learning assisted rational design and evolution of novel signal peptides in yarrowia lipolytica |
| topic | Siganl peptides Directed evolution Rational Design Machine learning Yarrowia lipolytica Protein secretion |
| url | http://www.sciencedirect.com/science/article/pii/S2405805X2500105X |
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