Loop optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade‐off through cross‐strategy between machine learning and the B‐factor analysis
Abstract Trichoderma reesei endoglucanases (EGs) have limited industrial applications due to its low thermostability and activity. Here, we aimed to improve the thermostability of EGs from T. reesei without reducing its activity counteracting the activity–stability trade‐off. A cross‐strategy combin...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-02-01
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| Series: | GCB Bioenergy |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/gcbb.13011 |
| _version_ | 1797957024913293312 |
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| author | Le Gao Qi Guo Ruinan Xu Haofan Dong Chichun Zhou Zhuohang Yu Zhaokun Zhang Lixian Wang Xiaoyi Chen Xin Wu |
| author_facet | Le Gao Qi Guo Ruinan Xu Haofan Dong Chichun Zhou Zhuohang Yu Zhaokun Zhang Lixian Wang Xiaoyi Chen Xin Wu |
| author_sort | Le Gao |
| collection | DOAJ |
| description | Abstract Trichoderma reesei endoglucanases (EGs) have limited industrial applications due to its low thermostability and activity. Here, we aimed to improve the thermostability of EGs from T. reesei without reducing its activity counteracting the activity–stability trade‐off. A cross‐strategy combination of machine learning and B‐factor analysis was used to predict beneficial amino acid substitution in EG loop optimization. Experimental validation showed single‐site mutated EG concomitantly improved enzymatic activity and thermal properties by 17.21%–18.06% and 49.85%–62.90%, respectively, compared with wild‐type EGs. Furthermore, the mechanism explained mutant variants had lower root mean square deviation values and a more stable overall structure than the wild type. According to this study, EGs loop optimization is crucial for balancing the activity–stability trade‐off, which may provide new insights into how loop region function interacts with enzymatic characteristics. Moreover, the cross‐strategy between machine learning and B‐factor analysis improved superior enzyme activity–stability performance, which integrated structure‐dependent and sequence‐dependent information. |
| first_indexed | 2024-04-10T23:57:51Z |
| format | Article |
| id | doaj.art-6a3a483afda54d02992f789e58fddd05 |
| institution | Directory Open Access Journal |
| issn | 1757-1693 1757-1707 |
| language | English |
| last_indexed | 2024-04-10T23:57:51Z |
| publishDate | 2023-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | GCB Bioenergy |
| spelling | doaj.art-6a3a483afda54d02992f789e58fddd052023-01-10T10:19:36ZengWileyGCB Bioenergy1757-16931757-17072023-02-0115212814210.1111/gcbb.13011Loop optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade‐off through cross‐strategy between machine learning and the B‐factor analysisLe Gao0Qi Guo1Ruinan Xu2Haofan Dong3Chichun Zhou4Zhuohang Yu5Zhaokun Zhang6Lixian Wang7Xiaoyi Chen8Xin Wu9Dalian Polytechnic University Dalian ChinaTianjin Institute of Industrial Biotechnology Chinese Academy of Sciences, National Technology Innovation Center of Synthetic Biology Tianjin ChinaDalian Polytechnic University Dalian ChinaTianjin Institute of Industrial Biotechnology Chinese Academy of Sciences, National Technology Innovation Center of Synthetic Biology Tianjin ChinaSchool of Engineering Dali University Dali ChinaSchool of Engineering Dali University Dali ChinaTianjin Institute of Industrial Biotechnology Chinese Academy of Sciences, National Technology Innovation Center of Synthetic Biology Tianjin ChinaTianjin Institute of Industrial Biotechnology Chinese Academy of Sciences, National Technology Innovation Center of Synthetic Biology Tianjin ChinaDalian Polytechnic University Dalian ChinaTianjin Institute of Industrial Biotechnology Chinese Academy of Sciences, National Technology Innovation Center of Synthetic Biology Tianjin ChinaAbstract Trichoderma reesei endoglucanases (EGs) have limited industrial applications due to its low thermostability and activity. Here, we aimed to improve the thermostability of EGs from T. reesei without reducing its activity counteracting the activity–stability trade‐off. A cross‐strategy combination of machine learning and B‐factor analysis was used to predict beneficial amino acid substitution in EG loop optimization. Experimental validation showed single‐site mutated EG concomitantly improved enzymatic activity and thermal properties by 17.21%–18.06% and 49.85%–62.90%, respectively, compared with wild‐type EGs. Furthermore, the mechanism explained mutant variants had lower root mean square deviation values and a more stable overall structure than the wild type. According to this study, EGs loop optimization is crucial for balancing the activity–stability trade‐off, which may provide new insights into how loop region function interacts with enzymatic characteristics. Moreover, the cross‐strategy between machine learning and B‐factor analysis improved superior enzyme activity–stability performance, which integrated structure‐dependent and sequence‐dependent information.https://doi.org/10.1111/gcbb.13011activity–stability trade‐offB‐factorendoglucanaseloop optimizationmachine learning approachesthermostability |
| spellingShingle | Le Gao Qi Guo Ruinan Xu Haofan Dong Chichun Zhou Zhuohang Yu Zhaokun Zhang Lixian Wang Xiaoyi Chen Xin Wu Loop optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade‐off through cross‐strategy between machine learning and the B‐factor analysis GCB Bioenergy activity–stability trade‐off B‐factor endoglucanase loop optimization machine learning approaches thermostability |
| title | Loop optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade‐off through cross‐strategy between machine learning and the B‐factor analysis |
| title_full | Loop optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade‐off through cross‐strategy between machine learning and the B‐factor analysis |
| title_fullStr | Loop optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade‐off through cross‐strategy between machine learning and the B‐factor analysis |
| title_full_unstemmed | Loop optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade‐off through cross‐strategy between machine learning and the B‐factor analysis |
| title_short | Loop optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade‐off through cross‐strategy between machine learning and the B‐factor analysis |
| title_sort | loop optimization of trichoderma reesei endoglucanases for balancing the activity stability trade off through cross strategy between machine learning and the b factor analysis |
| topic | activity–stability trade‐off B‐factor endoglucanase loop optimization machine learning approaches thermostability |
| url | https://doi.org/10.1111/gcbb.13011 |
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