Sequence–Activity Relationship of Angiotensin-Converting Enzyme Inhibitory Peptides Derived from Food Proteins, Based on a New Deep Learning Model
Food-derived peptides are usually safe natural drug candidates that can potentially inhibit the angiotensin-converting enzyme (ACE). The wet experiments used to identify ACE inhibitory peptides (ACEiPs) are time-consuming and costly, making it important and urgent to reduce the scope of experimental...
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MDPI AG
2024-11-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/13/22/3550 |
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| author | Dongya Qin Xiao Liang Linna Jiao Ruihong Wang Yi Zhao Wenjun Xue Jinhong Wang Guizhao Liang |
| author_facet | Dongya Qin Xiao Liang Linna Jiao Ruihong Wang Yi Zhao Wenjun Xue Jinhong Wang Guizhao Liang |
| author_sort | Dongya Qin |
| collection | DOAJ |
| description | Food-derived peptides are usually safe natural drug candidates that can potentially inhibit the angiotensin-converting enzyme (ACE). The wet experiments used to identify ACE inhibitory peptides (ACEiPs) are time-consuming and costly, making it important and urgent to reduce the scope of experimental validation through bioinformatics methods. Here, we construct an ACE inhibitory peptide predictor (ACEiPP) using optimized amino acid descriptors (AADs) and long- and short-term memory neural networks. Our results show that combined-AAD models exhibit more efficient feature transformation ability than single-AAD models, especially the training model with the optimal descriptors as the feature inputs, which exhibits the highest predictive ability in the independent test (Acc = 0.9479 and AUC = 0.9876), with a significant performance improvement compared to the existing three predictors. The model can effectively characterize the structure–activity relationship of ACEiPs. By combining the model with database mining, we used ACEiPP to screen four ACEiPs with multiple reported functions. We also used ACEiPP to predict peptides from 21,249 food-derived proteins in the Database of Food-derived Bioactive Peptides (DFBP) and construct a library of potential ACEiPs to facilitate the discovery of new anti-ACE peptides. |
| format | Article |
| id | doaj-art-f0cdd76cc74348c9bcf239ce24d0766b |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-f0cdd76cc74348c9bcf239ce24d0766b2024-11-26T18:04:18ZengMDPI AGFoods2304-81582024-11-011322355010.3390/foods13223550Sequence–Activity Relationship of Angiotensin-Converting Enzyme Inhibitory Peptides Derived from Food Proteins, Based on a New Deep Learning ModelDongya Qin0Xiao Liang1Linna Jiao2Ruihong Wang3Yi Zhao4Wenjun Xue5Jinhong Wang6Guizhao Liang7Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, ChinaKey Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, ChinaKey Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, ChinaKey Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, ChinaKey Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, ChinaKey Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, ChinaKey Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, ChinaKey Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, ChinaFood-derived peptides are usually safe natural drug candidates that can potentially inhibit the angiotensin-converting enzyme (ACE). The wet experiments used to identify ACE inhibitory peptides (ACEiPs) are time-consuming and costly, making it important and urgent to reduce the scope of experimental validation through bioinformatics methods. Here, we construct an ACE inhibitory peptide predictor (ACEiPP) using optimized amino acid descriptors (AADs) and long- and short-term memory neural networks. Our results show that combined-AAD models exhibit more efficient feature transformation ability than single-AAD models, especially the training model with the optimal descriptors as the feature inputs, which exhibits the highest predictive ability in the independent test (Acc = 0.9479 and AUC = 0.9876), with a significant performance improvement compared to the existing three predictors. The model can effectively characterize the structure–activity relationship of ACEiPs. By combining the model with database mining, we used ACEiPP to screen four ACEiPs with multiple reported functions. We also used ACEiPP to predict peptides from 21,249 food-derived proteins in the Database of Food-derived Bioactive Peptides (DFBP) and construct a library of potential ACEiPs to facilitate the discovery of new anti-ACE peptides.https://www.mdpi.com/2304-8158/13/22/3550angiotensin-converting enzyme inhibitory peptide (ACEiP)food-derived peptidedeep learningamino acid descriptor |
| spellingShingle | Dongya Qin Xiao Liang Linna Jiao Ruihong Wang Yi Zhao Wenjun Xue Jinhong Wang Guizhao Liang Sequence–Activity Relationship of Angiotensin-Converting Enzyme Inhibitory Peptides Derived from Food Proteins, Based on a New Deep Learning Model Foods angiotensin-converting enzyme inhibitory peptide (ACEiP) food-derived peptide deep learning amino acid descriptor |
| title | Sequence–Activity Relationship of Angiotensin-Converting Enzyme Inhibitory Peptides Derived from Food Proteins, Based on a New Deep Learning Model |
| title_full | Sequence–Activity Relationship of Angiotensin-Converting Enzyme Inhibitory Peptides Derived from Food Proteins, Based on a New Deep Learning Model |
| title_fullStr | Sequence–Activity Relationship of Angiotensin-Converting Enzyme Inhibitory Peptides Derived from Food Proteins, Based on a New Deep Learning Model |
| title_full_unstemmed | Sequence–Activity Relationship of Angiotensin-Converting Enzyme Inhibitory Peptides Derived from Food Proteins, Based on a New Deep Learning Model |
| title_short | Sequence–Activity Relationship of Angiotensin-Converting Enzyme Inhibitory Peptides Derived from Food Proteins, Based on a New Deep Learning Model |
| title_sort | sequence activity relationship of angiotensin converting enzyme inhibitory peptides derived from food proteins based on a new deep learning model |
| topic | angiotensin-converting enzyme inhibitory peptide (ACEiP) food-derived peptide deep learning amino acid descriptor |
| url | https://www.mdpi.com/2304-8158/13/22/3550 |
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