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Identify Bitter Peptides by Using Deep Representation Learning Features
A bitter taste often identifies hazardous compounds and it is generally avoided by most animals and humans. Bitterness of hydrolyzed proteins is caused by the presence of bitter peptides. To improve palatability, bitter peptides need to be identified experimentally in a time-consuming and expensive...
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/PMC9315524/ https://www.ncbi.nlm.nih.gov/pubmed/35887225 http://dx.doi.org/10.3390/ijms23147877 |
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author | Jiang, Jici Lin, Xinxu Jiang, Yueqi Jiang, Liangzhen Lv, Zhibin |
author_facet | Jiang, Jici Lin, Xinxu Jiang, Yueqi Jiang, Liangzhen Lv, Zhibin |
author_sort | Jiang, Jici |
collection | PubMed |
description | A bitter taste often identifies hazardous compounds and it is generally avoided by most animals and humans. Bitterness of hydrolyzed proteins is caused by the presence of bitter peptides. To improve palatability, bitter peptides need to be identified experimentally in a time-consuming and expensive process, before they can be removed or degraded. Here, we report the development of a machine learning prediction method, iBitter-DRLF, which is based on a deep learning pre-trained neural network feature extraction method. It uses three sequence embedding techniques, soft symmetric alignment (SSA), unified representation (UniRep), and bidirectional long short-term memory (BiLSTM). These were initially combined into various machine learning algorithms to build several models. After optimization, the combined features of UniRep and BiLSTM were finally selected, and the model was built in combination with a light gradient boosting machine (LGBM). The results showed that the use of deep representation learning greatly improves the ability of the model to identify bitter peptides, achieving accurate prediction based on peptide sequence data alone. By helping to identify bitter peptides, iBitter-DRLF can help research into improving the palatability of peptide therapeutics and dietary supplements in the future. A webserver is available, too. |
format | Online Article Text |
id | pubmed-9315524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93155242022-07-27 Identify Bitter Peptides by Using Deep Representation Learning Features Jiang, Jici Lin, Xinxu Jiang, Yueqi Jiang, Liangzhen Lv, Zhibin Int J Mol Sci Article A bitter taste often identifies hazardous compounds and it is generally avoided by most animals and humans. Bitterness of hydrolyzed proteins is caused by the presence of bitter peptides. To improve palatability, bitter peptides need to be identified experimentally in a time-consuming and expensive process, before they can be removed or degraded. Here, we report the development of a machine learning prediction method, iBitter-DRLF, which is based on a deep learning pre-trained neural network feature extraction method. It uses three sequence embedding techniques, soft symmetric alignment (SSA), unified representation (UniRep), and bidirectional long short-term memory (BiLSTM). These were initially combined into various machine learning algorithms to build several models. After optimization, the combined features of UniRep and BiLSTM were finally selected, and the model was built in combination with a light gradient boosting machine (LGBM). The results showed that the use of deep representation learning greatly improves the ability of the model to identify bitter peptides, achieving accurate prediction based on peptide sequence data alone. By helping to identify bitter peptides, iBitter-DRLF can help research into improving the palatability of peptide therapeutics and dietary supplements in the future. A webserver is available, too. MDPI 2022-07-17 /pmc/articles/PMC9315524/ /pubmed/35887225 http://dx.doi.org/10.3390/ijms23147877 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, Jici Lin, Xinxu Jiang, Yueqi Jiang, Liangzhen Lv, Zhibin Identify Bitter Peptides by Using Deep Representation Learning Features |
title | Identify Bitter Peptides by Using Deep Representation Learning Features |
title_full | Identify Bitter Peptides by Using Deep Representation Learning Features |
title_fullStr | Identify Bitter Peptides by Using Deep Representation Learning Features |
title_full_unstemmed | Identify Bitter Peptides by Using Deep Representation Learning Features |
title_short | Identify Bitter Peptides by Using Deep Representation Learning Features |
title_sort | identify bitter peptides by using deep representation learning features |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315524/ https://www.ncbi.nlm.nih.gov/pubmed/35887225 http://dx.doi.org/10.3390/ijms23147877 |
work_keys_str_mv | AT jiangjici identifybitterpeptidesbyusingdeeprepresentationlearningfeatures AT linxinxu identifybitterpeptidesbyusingdeeprepresentationlearningfeatures AT jiangyueqi identifybitterpeptidesbyusingdeeprepresentationlearningfeatures AT jiangliangzhen identifybitterpeptidesbyusingdeeprepresentationlearningfeatures AT lvzhibin identifybitterpeptidesbyusingdeeprepresentationlearningfeatures |