TastepepAI, An artificial intelligence platform for taste peptide de novo design

• URL: http://arxiv.org/abs/2502.12167v1
• Date: Thu, 13 Feb 2025 03:09:14 GMT
• Title: TastepepAI, An artificial intelligence platform for taste peptide de novo design
• Authors: Jianda Yue, Tingting Li, Jian Ouyang, Jiawei Xu, Hua Tan, Zihui Chen, Changsheng Han, Huanyu Li, Songping Liang, Zhonghua Liu, Zhonghua Liu, Ying Wang,
• Abstract summary: TastePepAI is a comprehensive artificial intelligence framework for customized taste peptide design and safety assessment. Our model incorporates a novel taste-avoidance mechanism, allowing for selective flavor exclusion. Using this integrated platform, we successfully identified 73 peptides exhibiting sweet, salty, and umami.
• Score: 10.809939164353658
• License:
• Abstract: 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.

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