Genome mining of tailoring enzymes from biosynthetic gene clusters for synthetic biology: A case study with fungal methyltransferases

Liwen Zhang; Yang Liu; Kang Chen; Qun Yue; Chen Wang; Linan Xie; István Molnár; Yuquan Xu

doi:10.1016/j.ymben.2025.08.001

Genome mining of tailoring enzymes from biosynthetic gene clusters for synthetic biology: A case study with fungal methyltransferases

Liwen Zhang^*
, Yang Liu
, Kang Chen
, Qun Yue
, Chen Wang
, Linan Xie
, István Molnár^*
, Yuquan Xu^*

^*Corresponding author for this work

BA53 Industrial biotechnology and food

Chinese Academy of Agricultural Sciences
China Hebei Construction & Geotechnical Investigation Group Ltd. (CHCI)

Research output: Contribution to journal › Article › Scientific › peer-review

2 Citations (Scopus)

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Abstract

Harnessing the potential of tailoring enzymes within fungal natural product (NP) biosynthetic gene clusters (BGCs) can significantly enhance NP diversity and production efficiency via artificially constructed microbial cell factories. To achieve this, an efficient genome mining method is crucial, especially since the functions of many putative enzymes in databases are unknown. As a test case, we aimed to identify methyltransferases (MTs) that modify a polyketide substrate without a known cognate MT. 16,748 putative MTs were annotated in 101,321 fungal BGCs and grouped into orthologous families. Three methods were explored to prioritize suitable enzymes. Among these, the machine learning method proved superior, with 11 out of 15 tested MTs successfully methylating the test substrate. This demonstrates the effectiveness of machine learning to mine tailoring enzymes that modify selected compounds, aiding synthetic biology in optimizing NP biosynthesis and facilitating the production of “unnatural products” for pharmaceutical or other bioindustrial applications.

Original language	English
Pages (from-to)	125-135
Number of pages	11
Journal	Metabolic Engineering
Volume	92
DOIs	https://doi.org/10.1016/j.ymben.2025.08.001
Publication status	Published - Nov 2025
MoE publication type	A1 Journal article-refereed

Funding

This work was supported by the National Key Research and Development Program of China (Grant No. 2023YFA0914700 to Y.X.), the National Natural Science Foundation of China (32070064 and 32270069 to L.Z.), the Central Public-Interest Scientific Institution Basal Research Fund (to C.W. and L.Z.), the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP to Y.X. and L.Z., CAAS-ZDRW202308 to Q.Y.), and VTT Technical Research Centre of Finland (to I.M.).

Keywords

Biocatalysis
Biosynthetic gene clusters
Combinatorial synthetic biology
Machine learning
Natural products
Tailoring enzymes

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title = "Genome mining of tailoring enzymes from biosynthetic gene clusters for synthetic biology: A case study with fungal methyltransferases",

abstract = "Harnessing the potential of tailoring enzymes within fungal natural product (NP) biosynthetic gene clusters (BGCs) can significantly enhance NP diversity and production efficiency via artificially constructed microbial cell factories. To achieve this, an efficient genome mining method is crucial, especially since the functions of many putative enzymes in databases are unknown. As a test case, we aimed to identify methyltransferases (MTs) that modify a polyketide substrate without a known cognate MT. 16,748 putative MTs were annotated in 101,321 fungal BGCs and grouped into orthologous families. Three methods were explored to prioritize suitable enzymes. Among these, the machine learning method proved superior, with 11 out of 15 tested MTs successfully methylating the test substrate. This demonstrates the effectiveness of machine learning to mine tailoring enzymes that modify selected compounds, aiding synthetic biology in optimizing NP biosynthesis and facilitating the production of “unnatural products” for pharmaceutical or other bioindustrial applications.",

keywords = "Biocatalysis, Biosynthetic gene clusters, Combinatorial synthetic biology, Machine learning, Natural products, Tailoring enzymes",

author = "Liwen Zhang and Yang Liu and Kang Chen and Qun Yue and Chen Wang and Linan Xie and Istv{\'a}n Moln{\'a}r and Yuquan Xu",

year = "2025",

month = nov,

doi = "10.1016/j.ymben.2025.08.001",

language = "English",

volume = "92",

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journal = "Metabolic Engineering",

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AU - Zhang, Liwen

AU - Liu, Yang

AU - Chen, Kang

AU - Yue, Qun

AU - Wang, Chen

AU - Xie, Linan

AU - Molnár, István

AU - Xu, Yuquan

PY - 2025/11

Y1 - 2025/11

N2 - Harnessing the potential of tailoring enzymes within fungal natural product (NP) biosynthetic gene clusters (BGCs) can significantly enhance NP diversity and production efficiency via artificially constructed microbial cell factories. To achieve this, an efficient genome mining method is crucial, especially since the functions of many putative enzymes in databases are unknown. As a test case, we aimed to identify methyltransferases (MTs) that modify a polyketide substrate without a known cognate MT. 16,748 putative MTs were annotated in 101,321 fungal BGCs and grouped into orthologous families. Three methods were explored to prioritize suitable enzymes. Among these, the machine learning method proved superior, with 11 out of 15 tested MTs successfully methylating the test substrate. This demonstrates the effectiveness of machine learning to mine tailoring enzymes that modify selected compounds, aiding synthetic biology in optimizing NP biosynthesis and facilitating the production of “unnatural products” for pharmaceutical or other bioindustrial applications.

AB - Harnessing the potential of tailoring enzymes within fungal natural product (NP) biosynthetic gene clusters (BGCs) can significantly enhance NP diversity and production efficiency via artificially constructed microbial cell factories. To achieve this, an efficient genome mining method is crucial, especially since the functions of many putative enzymes in databases are unknown. As a test case, we aimed to identify methyltransferases (MTs) that modify a polyketide substrate without a known cognate MT. 16,748 putative MTs were annotated in 101,321 fungal BGCs and grouped into orthologous families. Three methods were explored to prioritize suitable enzymes. Among these, the machine learning method proved superior, with 11 out of 15 tested MTs successfully methylating the test substrate. This demonstrates the effectiveness of machine learning to mine tailoring enzymes that modify selected compounds, aiding synthetic biology in optimizing NP biosynthesis and facilitating the production of “unnatural products” for pharmaceutical or other bioindustrial applications.

KW - Biocatalysis

KW - Biosynthetic gene clusters

KW - Combinatorial synthetic biology

KW - Machine learning

KW - Natural products

KW - Tailoring enzymes

UR - https://www.scopus.com/pages/publications/105012481429

U2 - 10.1016/j.ymben.2025.08.001

DO - 10.1016/j.ymben.2025.08.001

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JO - Metabolic Engineering

JF - Metabolic Engineering

ER -

Genome mining of tailoring enzymes from biosynthetic gene clusters for synthetic biology: A case study with fungal methyltransferases

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