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Food Science of Animal Products 2024, 2(1): 9240053 https://doi.org/10.26599/FSAP.2024.9240053
Research Article | Open Access | Issue | Published: 17 May 2024

Mining anti-hypertensive peptides in animal food through deep learning: a case study of gastrointestinal digestive products of royal jelly

Show Author's Information Fei Pan1,§,  Dongliang Liu2,§,  Tuohetisayipu Tuersuntuoheti3,4,  Huadong Xing2,  Zehui Zhu3,  Yu Fang1,  Lei Zhao3,  Liang Zhao3,  Xiangxin Li1,  Yingying Le2( ),  Qiannan Hu2( ),  Wenjun Peng1( ),  Wenli Tian1( )
State Key Laboratory of Resource Insects, Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University, Beijing 100048, China
Pony Testing International Group Co., Ltd., Beijing 100095, China

§These authors contributed equally to this work.

Keywords:
deep learning, peptide, angiotensin-I-converting enzyme inhibitory activity, virtual enzymatic digestion, animal-derived foods
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Pan F, Liu D, Tuersuntuoheti T, et al. Mining anti-hypertensive peptides in animal food through deep learning: a case study of gastrointestinal digestive products of royal jelly. Food Science of Animal Products, 2024, 2(1): 9240053. https://doi.org/10.26599/FSAP.2024.9240053
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Abstract Full text Electronic supplementary material About this article

To shorten the complex and time-consuming process of the identification method of the traditional food angiotensin-I-converting enzyme (ACE-I) inhibitory peptides, we propose AHTPeptideFusion based on a segmented fusion with the protein language model and deep learning. The statistical analysis found that hydrophobic amino acids, N-terminal valine is a dominant amino acid in the activity of ACE-I inhibitory peptides. In 12 machine learning (ML) algorithms, the transformer outperformed the other 11 models, with the best performance in predicting short and medium peptides. In the external dataset, AHTPeptideFusion fused by transformer and random forest (RF) showed excellent performance (accuracy > 0.9) in predicting ACE-I inhibitory peptides with lengths ranging from 2 to 15 amino acid residues and different activity distributions, and the reliability and accuracy of AHTPeptideFusion was demonstrated by synthetic peptide and ACE-I inhibition experiments. In addition, hydrogen bonding and electrostatic interaction between 4 synthetic peptides and active residues of ACE-I were found by molecular docking. To further explore the ACE-I inhibitory peptides from animal-derived foods, we established an automated pipeline consisting of the trinity of proteomics, virtual enzymatic digestion and AHTPeptideFusion, and tapped the ACE-I inhibitory peptide released from royal jelly after digestion in the gastrointestinal tract. In conclusion, this computational pipeline will become a powerful screening tool for active peptides from animal-derived foods, which can help food scientists accelerate the mining and design of active peptides from animal-derived foods. Overall, AHTPeptideFusion will be a powerful ACE-I inhibitor peptide prediction tool, it can help food scientists accelerate the mining and design of ACE-I inhibitory peptides.


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Electronic supplementary material
About this article

Mining anti-hypertensive peptides in animal food through deep learning: a case study of gastrointestinal digestive products of royal jelly

Show Author's information Fei Pan1,§, Dongliang Liu2,§, Tuohetisayipu Tuersuntuoheti3,4, Huadong Xing2, Zehui Zhu3, Yu Fang1, Lei Zhao3, Liang Zhao3, Xiangxin Li1, Yingying Le2( ), Qiannan Hu2( ), Wenjun Peng1( ), Wenli Tian1( )
State Key Laboratory of Resource Insects, Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University, Beijing 100048, China
Pony Testing International Group Co., Ltd., Beijing 100095, China

§These authors contributed equally to this work.

Abstract

To shorten the complex and time-consuming process of the identification method of the traditional food angiotensin-I-converting enzyme (ACE-I) inhibitory peptides, we propose AHTPeptideFusion based on a segmented fusion with the protein language model and deep learning. The statistical analysis found that hydrophobic amino acids, N-terminal valine is a dominant amino acid in the activity of ACE-I inhibitory peptides. In 12 machine learning (ML) algorithms, the transformer outperformed the other 11 models, with the best performance in predicting short and medium peptides. In the external dataset, AHTPeptideFusion fused by transformer and random forest (RF) showed excellent performance (accuracy > 0.9) in predicting ACE-I inhibitory peptides with lengths ranging from 2 to 15 amino acid residues and different activity distributions, and the reliability and accuracy of AHTPeptideFusion was demonstrated by synthetic peptide and ACE-I inhibition experiments. In addition, hydrogen bonding and electrostatic interaction between 4 synthetic peptides and active residues of ACE-I were found by molecular docking. To further explore the ACE-I inhibitory peptides from animal-derived foods, we established an automated pipeline consisting of the trinity of proteomics, virtual enzymatic digestion and AHTPeptideFusion, and tapped the ACE-I inhibitory peptide released from royal jelly after digestion in the gastrointestinal tract. In conclusion, this computational pipeline will become a powerful screening tool for active peptides from animal-derived foods, which can help food scientists accelerate the mining and design of active peptides from animal-derived foods. Overall, AHTPeptideFusion will be a powerful ACE-I inhibitor peptide prediction tool, it can help food scientists accelerate the mining and design of ACE-I inhibitory peptides.

Keywords: deep learning, peptide, angiotensin-I-converting enzyme inhibitory activity, virtual enzymatic digestion, animal-derived foods

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Publication history

Received: 26 March 2024
Revised: 19 April 2024
Accepted: 25 April 2024
Published: 17 May 2024
Issue date: March 2024

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© Beijing Academy of Food Sciences 2024.

Acknowledgements

This work was supported by the National Key R&D Program of China (2023YFF1103800) and the National Natural Science Foundation of China (32372328; 31972087). Moreover, we are particularly grateful to Dr. Du Zhenjiao for providing valuable suggestions for this study.

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Food Science of Animal Products published by Tsinghua University Press. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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