In Food chemistry
Taste peptides with umami/bitterness play a role in food attributes. However, the taste mechanisms of peptides are not fully understood, and the identification of these peptides is time-consuming. Here, we created a taste peptide database by collecting the reported taste peptide information. Eight key molecular descriptors from di/tri-peptides were selected and obtained by modeling screening. A gradient boosting decision tree model named Umami_YYDS (89.6% accuracy) was established by data enhancement, comparison algorithm and model optimization. Our model showed a great prediction performance compared to other models, and its outstanding ability was verified by sensory experiments. To provide a convenient approach, we deployed a prediction website based on Umami_YYDS and uploaded the Auto_Taste_ML machine learning package. In summary, we established the system TastePeptides-Meta, containing a taste peptide database TastePeptidesDB an umami/bitter taste prediction model Umami_YYDS and an open-source machine learning package Auto_Taste_ML, which were helpful for rapid screening of umami peptides.
Cui Zhiyong, Zhang Zhiwei, Zhou Tianxing, Zhou Xueke, Zhang Yin, Meng Hengli, Wang Wenli, Liu Yuan
2022-Nov-09
Machine learning, Peptides, TastePeptidesDB, Umami prediction
