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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.12167 |
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| _version_ | 1866929718742220800 |
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| author | Yue, Jianda Li, Tingting Ouyang, Jian Xu, Jiawei Tan, Hua Chen, Zihui Han, Changsheng Li, Huanyu Liang, Songping Liu, Zhonghua Liu, Zhonghua Wang, Ying |
| author_facet | Yue, Jianda Li, Tingting Ouyang, Jian Xu, Jiawei Tan, Hua Chen, Zihui Han, Changsheng Li, Huanyu Liang, Songping Liu, Zhonghua Liu, Zhonghua Wang, Ying |
| contents | Taste peptides have emerged as promising natural flavoring agents attributed to their unique organoleptic properties, high safety profile, and potential health benefits. However, the de novo identification of taste peptides derived from animal, plant, or microbial sources remains a time-consuming and resource-intensive process, significantly impeding their widespread application in the food industry. Here, we present TastePepAI, a comprehensive artificial intelligence framework for customized taste peptide design and safety assessment. As the key element of this framework, a loss-supervised adaptive variational autoencoder (LA-VAE) is implemented to efficiently optimizes the latent representation of sequences during training and facilitates the generation of target peptides with desired taste profiles. Notably, our model incorporates a novel taste-avoidance mechanism, allowing for selective flavor exclusion. Subsequently, our in-house developed toxicity prediction algorithm (SpepToxPred) is integrated in the framework to undergo rigorous safety evaluation of generated peptides. Using this integrated platform, we successfully identified 73 peptides exhibiting sweet, salty, and umami, significantly expanding the current repertoire of taste peptides. This work demonstrates the potential of TastePepAI in accelerating taste peptide discovery for food applications and provides a versatile framework adaptable to broader peptide engineering challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TastepepAI, An artificial intelligence platform for taste peptide de novo design Yue, Jianda Li, Tingting Ouyang, Jian Xu, Jiawei Tan, Hua Chen, Zihui Han, Changsheng Li, Huanyu Liang, Songping Liu, Zhonghua Liu, Zhonghua Wang, Ying Machine Learning Artificial Intelligence Taste peptides have emerged as promising natural flavoring agents attributed to their unique organoleptic properties, high safety profile, and potential health benefits. However, the de novo identification of taste peptides derived from animal, plant, or microbial sources remains a time-consuming and resource-intensive process, significantly impeding their widespread application in the food industry. Here, we present TastePepAI, a comprehensive artificial intelligence framework for customized taste peptide design and safety assessment. As the key element of this framework, a loss-supervised adaptive variational autoencoder (LA-VAE) is implemented to efficiently optimizes the latent representation of sequences during training and facilitates the generation of target peptides with desired taste profiles. Notably, our model incorporates a novel taste-avoidance mechanism, allowing for selective flavor exclusion. Subsequently, our in-house developed toxicity prediction algorithm (SpepToxPred) is integrated in the framework to undergo rigorous safety evaluation of generated peptides. Using this integrated platform, we successfully identified 73 peptides exhibiting sweet, salty, and umami, significantly expanding the current repertoire of taste peptides. This work demonstrates the potential of TastePepAI in accelerating taste peptide discovery for food applications and provides a versatile framework adaptable to broader peptide engineering challenges. |
| title | TastepepAI, An artificial intelligence platform for taste peptide de novo design |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.12167 |