Innovations in Microbial Enzyme Production: From AI-Driven Design to Industrial Bioprocessing

A special issue of Fermentation (ISSN 2311-5637). This special issue belongs to the section "Industrial Fermentation".

Deadline for manuscript submissions: 15 April 2026 | Viewed by 226

Special Issue Editors

College of Food Science and Light Industry, Nanjing Tech University, Nanjing, China
Interests: synthetic biology; microbial metabolism; enzyme engineering; fermentation engineering

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Guest Editor
College of Food Science and Light Industry, Nanjing Tech University, Nanjing, China
Interests: food enzyme; biocatalysis; microbial cell factory; fermentation control; immobilization of enzymes; nanozyme

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Guest Editor
Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
Interests: bacterial communication; antimicrobial; microbiome interactions

Special Issue Information

Dear Colleagues,

Enzymes serve as core biocatalysts enabling sustainable manufacturing across the pharmaceutical, food, and daily chemical industries. Engineering high-performance enzymes (e.g., glycosyltransferases, lipases, plastic-degrading enzymes, and diagnostic enzymes) in robust microbial cell factories promotes the technological upgrading of traditional industries while creating high-value products with significant economic potential. Microbial expression systems remain indispensable for producing value-added compounds—from antibiotics and vitamins to biofuels and food flavors—through genetically engineered pathways.

Recent advances in genome editing, multi-omics technologies, and machine learning now transcend traditional enzyme discovery paradigms. These innovations enable the targeted mining of enzyme genes, rational design of protein stability and activity, and optimization of enzyme expression in host systems. Integrated with structural biology breakthroughs and AI-driven protein engineering, these tools allow for the systematic design of novel biocatalysts and the reconstruction of synthetic pathways in microbial factories for efficient multi-enzyme cascades featuring optimized cofactor regeneration.

This Special Issue focuses on, but is not limited to the following:

(1) AI-guided enzyme mining and directed evolution;

(2) High-density fermentation and pilot-scale production;

(3) Host-optimized enzyme expression and metabolic engineering;

(4) Industrial applications in pharmaceuticals, green chemistry, and functional biomaterials.

We welcome original research and reviews advancing enzymatic platforms that surpass chemical processes in sustainability and specificity, spanning fundamental discovery to industrial-scale implementation.

Dr. Yang Sun
Prof. Dr. Ling Jiang
Dr. Zhidong Zhang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fermentation is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • enzyme engineering
  • microbial cell factories
  • AI-guided enzyme mining
  • directed evolution
  • high-density fermentation
  • metabolic optimization
  • biocatalysts
  • industrial biotechnology
  • sustainable manufacturing
  • multi-enzyme cascadestranscriptional regulation

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Published Papers (1 paper)

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Research

16 pages, 1288 KB  
Article
Genome Mining of Acinetobacter nosocomialis J2 Using Artificial Intelligence Reveals a Highly Efficient Acid Phosphatase for Phosphate Solubilisation
by Kaixu Chen, Huiling Huang, Xiao Yu, Jing Zhang, Chunming Zhou, Zhong Yao, Zheng Xu, Yang Liu and Yang Sun
Fermentation 2026, 12(1), 64; https://doi.org/10.3390/fermentation12010064 (registering DOI) - 21 Jan 2026
Abstract
Excessive application of chemical fertilisers has led to soil phosphorus immobilisation and aquatic eutrophication, making the development of highly efficient acid/neutral phosphatases crucial for sustainable phosphorus utilisation. In this study, we systematically investigated strain J2, which was isolated from phosphate-contaminated soil in Laoshan, [...] Read more.
Excessive application of chemical fertilisers has led to soil phosphorus immobilisation and aquatic eutrophication, making the development of highly efficient acid/neutral phosphatases crucial for sustainable phosphorus utilisation. In this study, we systematically investigated strain J2, which was isolated from phosphate-contaminated soil in Laoshan, Nanjing, China. 16S rRNA gene sequence analysis identified this strain as Acinetobacter nosocomialis J2, with 99.78% sequence similarity. Whole-genome sequencing generated a 3.83 Mb genome with a GC content of 38.59%, revealing multiple phospho-metabolism-related enzyme genes, including phospholipase C and α/β-hydrolases. A large language model–based protein representation learning strategy was employed to mine acid/neutral phosphatase genes from the genome, in which the model learned contextual and functional features from known phosphatase sequences and was used to identify semantically similar genes within the J2 genome. This approach predicted nine phosphatase candidate sequences, including AnACPase, a putative acid/neutral phosphatase. Biochemical characterisation showed that AnACPase exhibits optimal activity at pH 6.0 and 50 °C, with a Km value of 0.2454 mmol/L for the p-NPP substrate, indicating high substrate affinity. Mn2+ and Ni2+ significantly enhanced enzyme activity, whereas Cu2+ and Zn2+ strongly inhibited it. Soil remediation experiments further validated the application potential of AnACPase, which solubilised 171.56 mg/kg of phosphate within seven days. Overall, this study highlights the advantages of deep learning-assisted genome mining for functional enzyme discovery and provides a novel technological pathway for the bioremediation of phosphorus-polluted soils. Full article
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