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Microorganisms
Volume 14
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10.3390/microorganisms14050954
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Article

Deep Learning-Guided Engineering of Bst DNA Polymerase Improves LAMP-Based Detection of Foodborne Pathogens

by
Haoting Chen
2,3,
Jingfeng Zhang
1,3,
Xiaoli Xu
1,
Huang Zhang
3,
Yanlei Chang
3,
Lei Shi
4,*,† and
Lichao Zhao
1,*,†
1
College of Food Science, Instrumental Analysis & Research Center, South China Agricultural University, Guangzhou 510642, China
2
College of Life Sciences, South China Agricultural University, Guangzhou 510642, China
3
Guangzhou Double Helix Gene Technology Co., Ltd., Guangzhou 510700, China
4
College of Life Sciences, Hebei Agricultural University, Baoding 071000, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Microorganisms 2026, 14(5), 954; https://doi.org/10.3390/microorganisms14050954
Submission received: 20 March 2026 / Revised: 21 April 2026 / Accepted: 21 April 2026 / Published: 23 April 2026
(This article belongs to the Section Food Microbiology)
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Abstract

Loop-mediated isothermal amplification (LAMP) is a widely used nucleic acid detection method, but its application is often limited by the suboptimal performance of wild-type Bacillus stearothermophilus (Bst) DNA polymerase. This study employed a combined deep learning and semi-rational design strategy to engineer Bst DNA polymerase. High-throughput screening identified the A0A150MFP3 sequence and the L105M mutation, which increased enzymatic activity by 32.92%. Fusion with the CL7 protein generated a CL7-Bst mutant with enhanced thermal stability and tolerance to common inhibitors, including 7% (v/v) ethanol, 0.18‰ (w/v) SDS, 80 mmol/L NaCl, and 0.8 mmol/L EDTA. Systematic optimization of the LAMP reaction system determined the optimal pH (9.0), enzyme concentration (0.20 U/μL), and temperature (64 °C). When applied to Escherichia coli O157:H7 detection, the CL7-Bst mutant achieved Tt values of 15.13 and 12.78 for crude and purified DNA, respectively, with a limit of detection of 1 × 103 CFU/mL. In summary, integrating deep learning with semi-rational design and fusion protein engineering yielded a high-performance DNA polymerase that facilitates rapid, sensitive, and field-deployable LAMP-based pathogen detection.
Keywords: Bst DNA polymerase; foodborne pathogen detection; deep learning; LAMP; fusion protein engineering Bst DNA polymerase; foodborne pathogen detection; deep learning; LAMP; fusion protein engineering
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MDPI and ACS Style

Chen, H.; Zhang, J.; Xu, X.; Zhang, H.; Chang, Y.; Shi, L.; Zhao, L. Deep Learning-Guided Engineering of Bst DNA Polymerase Improves LAMP-Based Detection of Foodborne Pathogens. Microorganisms 2026, 14, 954. https://doi.org/10.3390/microorganisms14050954

AMA Style

Chen H, Zhang J, Xu X, Zhang H, Chang Y, Shi L, Zhao L. Deep Learning-Guided Engineering of Bst DNA Polymerase Improves LAMP-Based Detection of Foodborne Pathogens. Microorganisms. 2026; 14(5):954. https://doi.org/10.3390/microorganisms14050954

Chicago/Turabian Style

Chen, Haoting, Jingfeng Zhang, Xiaoli Xu, Huang Zhang, Yanlei Chang, Lei Shi, and Lichao Zhao. 2026. "Deep Learning-Guided Engineering of Bst DNA Polymerase Improves LAMP-Based Detection of Foodborne Pathogens" Microorganisms 14, no. 5: 954. https://doi.org/10.3390/microorganisms14050954

APA Style

Chen, H., Zhang, J., Xu, X., Zhang, H., Chang, Y., Shi, L., & Zhao, L. (2026). Deep Learning-Guided Engineering of Bst DNA Polymerase Improves LAMP-Based Detection of Foodborne Pathogens. Microorganisms, 14(5), 954. https://doi.org/10.3390/microorganisms14050954

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