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- Title
Identify Bitter Peptides by Using Deep Representation Learning Features.
- Authors
Jiang, Jici; Lin, Xinxu; Jiang, Yueqi; Jiang, Liangzhen; Lv, Zhibin
- Abstract
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.
- Subjects
BITTERNESS (Taste); DEEP learning; PEPTIDES; MACHINE learning; AMINO acid sequence; FEATURE extraction; DIETARY supplements
- Publication
International Journal of Molecular Sciences, 2022, Vol 23, Issue 14, p7877
- ISSN
1661-6596
- Publication type
Article
- DOI
10.3390/ijms23147877