IUP-BERT: Identification of Umami Peptides Based on BERT Features
Umami is an important widely-used taste component of food seasoning. Umami peptides are specific structural peptides endowing foods with a favorable umami taste. Laboratory approaches used to identify umami peptides are time-consuming and labor-intensive, which are not feasible for rapid screening....
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/11/22/3742 |
| _version_ | 1797465327156592640 |
|---|---|
| author | Liangzhen Jiang Jici Jiang Xiao Wang Yin Zhang Bowen Zheng Shuqi Liu Yiting Zhang Changying Liu Yan Wan Dabing Xiang Zhibin Lv |
| author_facet | Liangzhen Jiang Jici Jiang Xiao Wang Yin Zhang Bowen Zheng Shuqi Liu Yiting Zhang Changying Liu Yan Wan Dabing Xiang Zhibin Lv |
| author_sort | Liangzhen Jiang |
| collection | DOAJ |
| description | Umami is an important widely-used taste component of food seasoning. Umami peptides are specific structural peptides endowing foods with a favorable umami taste. Laboratory approaches used to identify umami peptides are time-consuming and labor-intensive, which are not feasible for rapid screening. Here, we developed a novel peptide sequence-based umami peptide predictor, namely iUP-BERT, which was based on the deep learning pretrained neural network feature extraction method. After optimization, a single deep representation learning feature encoding method (BERT: bidirectional encoder representations from transformer) in conjugation with the synthetic minority over-sampling technique (SMOTE) and support vector machine (SVM) methods was adopted for model creation to generate predicted probabilistic scores of potential umami peptides. Further extensive empirical experiments on cross-validation and an independent test showed that iUP-BERT outperformed the existing methods with improvements, highlighting its effectiveness and robustness. Finally, an open-access iUP-BERT web server was built. To our knowledge, this is the first efficient sequence-based umami predictor created based on a single deep-learning pretrained neural network feature extraction method. By predicting umami peptides, iUP-BERT can help in further research to improve the palatability of dietary supplements in the future. |
| first_indexed | 2024-03-09T18:19:57Z |
| format | Article |
| id | doaj.art-c744630ce242471ebb8961bcc9d42e24 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-09T18:19:57Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-c744630ce242471ebb8961bcc9d42e242023-11-24T08:23:32ZengMDPI AGFoods2304-81582022-11-011122374210.3390/foods11223742IUP-BERT: Identification of Umami Peptides Based on BERT FeaturesLiangzhen Jiang0Jici Jiang1Xiao Wang2Yin Zhang3Bowen Zheng4Shuqi Liu5Yiting Zhang6Changying Liu7Yan Wan8Dabing Xiang9Zhibin Lv10College of Food and Biological Engineering, Chengdu University, Chengdu 610106, ChinaDepartment of Medical Instruments and Information, College of Biomedical Engineering, Sichuan University, Chengdu 610041, ChinaCollege of Food and Biological Engineering, Chengdu University, Chengdu 610106, ChinaCollege of Food and Biological Engineering, Chengdu University, Chengdu 610106, ChinaDepartment of Medical Instruments and Information, College of Biomedical Engineering, Sichuan University, Chengdu 610041, ChinaCollege of Food and Biological Engineering, Chengdu University, Chengdu 610106, ChinaCollege of Biology, Southwest Jiaotong University, Chengdu 610031, ChinaCollege of Food and Biological Engineering, Chengdu University, Chengdu 610106, ChinaCollege of Food and Biological Engineering, Chengdu University, Chengdu 610106, ChinaCollege of Food and Biological Engineering, Chengdu University, Chengdu 610106, ChinaDepartment of Medical Instruments and Information, College of Biomedical Engineering, Sichuan University, Chengdu 610041, ChinaUmami is an important widely-used taste component of food seasoning. Umami peptides are specific structural peptides endowing foods with a favorable umami taste. Laboratory approaches used to identify umami peptides are time-consuming and labor-intensive, which are not feasible for rapid screening. Here, we developed a novel peptide sequence-based umami peptide predictor, namely iUP-BERT, which was based on the deep learning pretrained neural network feature extraction method. After optimization, a single deep representation learning feature encoding method (BERT: bidirectional encoder representations from transformer) in conjugation with the synthetic minority over-sampling technique (SMOTE) and support vector machine (SVM) methods was adopted for model creation to generate predicted probabilistic scores of potential umami peptides. Further extensive empirical experiments on cross-validation and an independent test showed that iUP-BERT outperformed the existing methods with improvements, highlighting its effectiveness and robustness. Finally, an open-access iUP-BERT web server was built. To our knowledge, this is the first efficient sequence-based umami predictor created based on a single deep-learning pretrained neural network feature extraction method. By predicting umami peptides, iUP-BERT can help in further research to improve the palatability of dietary supplements in the future.https://www.mdpi.com/2304-8158/11/22/3742umami peptidepredictiondeep learningBERTSMOTE |
| spellingShingle | Liangzhen Jiang Jici Jiang Xiao Wang Yin Zhang Bowen Zheng Shuqi Liu Yiting Zhang Changying Liu Yan Wan Dabing Xiang Zhibin Lv IUP-BERT: Identification of Umami Peptides Based on BERT Features Foods umami peptide prediction deep learning BERT SMOTE |
| title | IUP-BERT: Identification of Umami Peptides Based on BERT Features |
| title_full | IUP-BERT: Identification of Umami Peptides Based on BERT Features |
| title_fullStr | IUP-BERT: Identification of Umami Peptides Based on BERT Features |
| title_full_unstemmed | IUP-BERT: Identification of Umami Peptides Based on BERT Features |
| title_short | IUP-BERT: Identification of Umami Peptides Based on BERT Features |
| title_sort | iup bert identification of umami peptides based on bert features |
| topic | umami peptide prediction deep learning BERT SMOTE |
| url | https://www.mdpi.com/2304-8158/11/22/3742 |
| work_keys_str_mv | AT liangzhenjiang iupbertidentificationofumamipeptidesbasedonbertfeatures AT jicijiang iupbertidentificationofumamipeptidesbasedonbertfeatures AT xiaowang iupbertidentificationofumamipeptidesbasedonbertfeatures AT yinzhang iupbertidentificationofumamipeptidesbasedonbertfeatures AT bowenzheng iupbertidentificationofumamipeptidesbasedonbertfeatures AT shuqiliu iupbertidentificationofumamipeptidesbasedonbertfeatures AT yitingzhang iupbertidentificationofumamipeptidesbasedonbertfeatures AT changyingliu iupbertidentificationofumamipeptidesbasedonbertfeatures AT yanwan iupbertidentificationofumamipeptidesbasedonbertfeatures AT dabingxiang iupbertidentificationofumamipeptidesbasedonbertfeatures AT zhibinlv iupbertidentificationofumamipeptidesbasedonbertfeatures |