Journal Article

IUP-BERT: Identification of Umami Peptides Based on BERT Features

Jiang, Liangzhen, Jiang, Jici, Zhang, Yin, Liu, Shuqi, Zheng, Bowen, Xiang, Dabing, Lv, Zhibin, Liu, Changying, Zhang, Yiting, Wan, Yan, Wang, Xiao

2022

Overview

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.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Algorithms / Amino acids / BERT / Chemical properties / Coders / Conjugation / Datasets / Deep learning / Dietary supplements / Feature extraction / Food / Food research / Food science / Identification and classification / Language / Machine learning / Methods / Neural networks / Optimization / Palatability / Peptides / prediction / Proteins / Representation learning / SMOTE / Support vector machines / Taste / Umami / umami peptide