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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...

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Detalles Bibliográficos
Autores principales: Jiang, Jici, Lin, Xinxu, Jiang, Yueqi, Jiang, Liangzhen, Lv, Zhibin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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
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AT lvzhibin identifybitterpeptidesbyusingdeeprepresentationlearningfeatures