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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....
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689418/ https://www.ncbi.nlm.nih.gov/pubmed/36429332 http://dx.doi.org/10.3390/foods11223742 |
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author | Jiang, Liangzhen Jiang, Jici Wang, Xiao Zhang, Yin Zheng, Bowen Liu, Shuqi Zhang, Yiting Liu, Changying Wan, Yan Xiang, Dabing Lv, Zhibin |
author_facet | Jiang, Liangzhen Jiang, Jici Wang, Xiao Zhang, Yin Zheng, Bowen Liu, Shuqi Zhang, Yiting Liu, Changying Wan, Yan Xiang, Dabing Lv, Zhibin |
author_sort | Jiang, Liangzhen |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-9689418 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96894182022-11-25 IUP-BERT: Identification of Umami Peptides Based on BERT Features Jiang, Liangzhen Jiang, Jici Wang, Xiao Zhang, Yin Zheng, Bowen Liu, Shuqi Zhang, Yiting Liu, Changying Wan, Yan Xiang, Dabing Lv, Zhibin Foods Article 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. MDPI 2022-11-21 /pmc/articles/PMC9689418/ /pubmed/36429332 http://dx.doi.org/10.3390/foods11223742 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) |
spellingShingle | Article Jiang, Liangzhen Jiang, Jici Wang, Xiao Zhang, Yin Zheng, Bowen Liu, Shuqi Zhang, Yiting Liu, Changying Wan, Yan Xiang, Dabing Lv, Zhibin IUP-BERT: Identification of Umami Peptides Based on BERT Features |
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 | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689418/ https://www.ncbi.nlm.nih.gov/pubmed/36429332 http://dx.doi.org/10.3390/foods11223742 |
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