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Liu M, Yang J, He Y, Cao F, Li W, Han W. VmmScore: An umami peptide prediction and receptor matching program based on a deep learning approach. Comput Biol Med 2024; 179:108814. [PMID: 38944902 DOI: 10.1016/j.compbiomed.2024.108814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/17/2024] [Accepted: 06/24/2024] [Indexed: 07/02/2024]
Abstract
Peptides, with recognized physiological and medical implications, such as the ability to lower blood pressure and lipid levels, are central to our research on umami taste perception. This study introduces a computational strategy to tackle the challenge of identifying optimal umami receptors for these peptides. Our VmmScore algorithm includes two integral components: Mlp4Umami, a predictive module that evaluates the umami taste potential of peptides, and mm-Score, which enhances the receptor matching process through a machine learning-optimized molecular docking and scoring system. This system encompasses the optimization of docking structures, clustering of umami peptides, and a comparative analysis of docking energies across peptide clusters, streamlining the receptor identification process. Employing machine learning, our method offers a strategic approach to the intricate task of umami receptor determination. We undertook virtual screening of peptides derived from Lateolabrax japonicus, experimentally verifying the umami taste of three identified peptides and determining their corresponding receptors. This work not only advances our understanding of the mechanisms behind umami taste perception but also provides a rapid and cost-effective method for peptide screening. The source code is publicly accessible at https://github.com/heyigacu/mlp4umami/, encouraging further scientific exploration and collaborative efforts within the research community.
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Affiliation(s)
- Minghao Liu
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
| | - Jiuliang Yang
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
| | - Yi He
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
| | - Fuyan Cao
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
| | - Wannan Li
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
| | - Weiwei Han
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
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2
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Yang Z, Wang Z, Wang R, Zhang W. A Novel Dry-Cured Ham Broth-Derived Peptide JHBp2 Effectively Inhibits Salmonella typhimurium In Vitro: Integrated Metabolomic, Proteomic, and Molecular Simulation Analyses. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:14433-14447. [PMID: 38866717 DOI: 10.1021/acs.jafc.4c01531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
JHBp2 is a peptide purified from Jinhua ham broth with antibacterial activity against Salmonella typhimurium. Untargeted metabolomics and label-free quantitative proteomics were used to analyze metabolic and protein expression changes in S. typhimurium after JHBp2 treatment. Cell wall and membrane damage results indicate that JHBp2 has membrane-disruptive properties, causing leakage of intracellular nucleic acids and proteins. Metabolomics revealed 516 differentially expressed metabolites, involving cofactor biosynthesis, purine metabolism, ABC transporters, glutathione metabolism, pyrimidine metabolism, etc. Proteomics detected 735 differentially expressed proteins, involving pyruvate metabolism, amino acid biosynthesis, purine metabolism, carbon metabolism, glycolysis/gluconeogenesis, etc. RT-qPCR and proteomics results showed a positive correlation, and molecular docking demonstrated stable binding of JHBp2 to some differentially expressed proteins. In summary, JHBp2 could disrupt the S. typhimurium cell wall and membrane structure, interfere with synthesis of membrane-related proteins, trigger intracellular substance leak, and reduce levels of enzymes and metabolites involved in energy metabolism, amino acid anabolism, and nucleotide anabolism.
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Affiliation(s)
- Ziyi Yang
- Key Laboratory of Meat Processing and Quality Control, Ministry of Education China, Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Zixu Wang
- Key Laboratory of Meat Processing and Quality Control, Ministry of Education China, Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Ruoxin Wang
- Key Laboratory of Meat Processing and Quality Control, Ministry of Education China, Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Wangang Zhang
- Key Laboratory of Meat Processing and Quality Control, Ministry of Education China, Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
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3
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Zhang W, Guan H, Wang M, Wang W, Pu J, Zou H, Li D. Exploring the Relationship between Small Peptides and the T1R1/T1R3 Umami Taste Receptor for Umami Peptide Prediction: A Combined Approach. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:13262-13272. [PMID: 38775286 DOI: 10.1021/acs.jafc.4c00187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
Umami peptides are known for enhancing the taste experience by binding to oral umami T1R1 and T1R3 receptors. Among them, small peptides (composed of 2-4 amino acids) constitute nearly 40% of reported umami peptides. Given the diversity in amino acids and peptide sequences, umami small peptides possess tremendous untapped potential. By investigating 168,400 small peptides, we screened candidates binding to T1R1/T1R3 through molecular docking and molecular dynamics simulations, explored bonding types, amino acid characteristics, preferred binding sites, etc. Utilizing three-dimensional molecular descriptors, bonding information, and a back-propagation neural network, we developed a predictive model with 90.3% accuracy, identifying 24,539 potential umami peptides. Clustering revealed three classes with distinct logP (-2.66 ± 1.02, -3.52 ± 0.93, -2.44 ± 1.23) and asphericity (0.28 ± 0.12, 0.26 ± 0.11, 0.25 ± 0.11), indicating significant differences in shape and hydrophobicity (P < 0.05) among potential umami peptides binding to T1R1/T1R3. Following clustering, nine representative peptides (CQ, DP, NN, CSQ, DMC, TGS, DATE, HANR, and STAN) were synthesized and confirmed to possess umami taste through sensory evaluations and electronic tongue analyses. In summary, this study provides insights into exploring small peptide interactions with umami receptors, advancing umami peptide prediction models.
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Affiliation(s)
- Wenyuan Zhang
- College of Food Science and Engineering, Shandong Agricultural University, Key Laboratory of Food Nutrition and Human Health in Universities of Shandong, Taian 271018, People's Republic of China
| | - Hui Guan
- College of Food Science and Engineering, Shandong Agricultural University, Key Laboratory of Food Nutrition and Human Health in Universities of Shandong, Taian 271018, People's Republic of China
| | - Miaomiao Wang
- College of Food Science and Engineering, Shandong Agricultural University, Key Laboratory of Food Nutrition and Human Health in Universities of Shandong, Taian 271018, People's Republic of China
| | - Wenyu Wang
- College of Food Science and Engineering, Shandong Agricultural University, Key Laboratory of Food Nutrition and Human Health in Universities of Shandong, Taian 271018, People's Republic of China
| | - Jianyu Pu
- College of Food Science and Engineering, Shandong Agricultural University, Key Laboratory of Food Nutrition and Human Health in Universities of Shandong, Taian 271018, People's Republic of China
| | - Hui Zou
- College of Food Science and Engineering, Shandong Agricultural University, Key Laboratory of Food Nutrition and Human Health in Universities of Shandong, Taian 271018, People's Republic of China
| | - Dapeng Li
- College of Food Science and Engineering, Shandong Agricultural University, Key Laboratory of Food Nutrition and Human Health in Universities of Shandong, Taian 271018, People's Republic of China
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4
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Chu X, Zhu W, Li X, Su E, Wang J. Bitter flavors and bitter compounds in foods: identification, perception, and reduction techniques. Food Res Int 2024; 183:114234. [PMID: 38760147 DOI: 10.1016/j.foodres.2024.114234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 03/09/2024] [Accepted: 03/12/2024] [Indexed: 05/19/2024]
Abstract
Bitterness is one of the five basic tastes generally considered undesirable. The widespread presence of bitter compounds can negatively affect the palatability of foods. The classification and sensory evaluation of bitter compounds have been the focus in recent research. However, the rigorous identification of bitter tastes and further studies to effectively mask or remove them have not been thoroughly evaluated. The present paper focuses on identification of bitter compounds in foods, structural-based activation of bitter receptors, and strategies to reduce bitter compounds in foods. It also discusses the roles of metabolomics and virtual screening analysis in bitter taste. The identification of bitter compounds has seen greater success through metabolomics with multivariate statistical analysis compared to conventional chromatography, HPLC, LC-MS, and NMR techniques. However, to avoid false positives, sensory recognition should be combined. Bitter perception involves the structural activation of bitter taste receptors (TAS2Rs). Only 25 human TAS2Rs have been identified as responsible for recognizing numerous bitter compounds, showcasing their high structural diversity to bitter agonists. Thus, reducing bitterness can be achieved through several methods. Traditionally, the removal or degradation of bitter substances has been used for debittering, while the masking of bitterness presents a new effective approach to improving food flavor. Future research in food bitterness should focus on identifying unknown bitter compounds in food, elucidating the mechanisms of activation of different receptors, and developing debittering techniques based on the entire food matrix.
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Affiliation(s)
- Xinyu Chu
- Department of Food Science and Technology, College of Light Industry and Food Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Wangsheng Zhu
- Engineering Technology Research Center for Plant Cell of Anhui Province, West Anhui University, Anhui 237012, China
| | - Xue Li
- Department of Food Science and Technology, College of Light Industry and Food Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Erzheng Su
- Department of Food Science and Technology, College of Light Industry and Food Engineering, Nanjing Forestry University, Nanjing 210037, China; Co-innovation Center for the Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry University, Nanjing 210037, China; Co-Innovation Center of Efficient Procession of Forest Resources, Nanjing Forestry University, Nanjing 210037, China
| | - Jiahong Wang
- Department of Food Science and Technology, College of Light Industry and Food Engineering, Nanjing Forestry University, Nanjing 210037, China; Co-innovation Center for the Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry University, Nanjing 210037, China; Co-Innovation Center of Efficient Procession of Forest Resources, Nanjing Forestry University, Nanjing 210037, China.
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Xu Y, Chen G, Cui Z, Wang Y, Wang W, Blank I, Zhang Y, Xu C, Yang Y, Liu Y. Novel Umami Peptides from Mushroom ( Agaricus bisporus) and Their Umami Enhancing Effect via Virtual Screening and Molecular Simulation. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024. [PMID: 38608250 DOI: 10.1021/acs.jafc.3c04608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/14/2024]
Abstract
This study aimed to identify novel umami peptides in Agaricus bisporus and investigate their umami enhancing effect. We virtually screened 155 potential umami peptides from the ultrasound-assisted A. bisporus hydrolysate according to Q values, iUmami-SCM, Umami_YYDS, and Tastepeptides_DM models, and molecular docking. Five peptides (AGKNTNGSQF, DEAVARGATF, REESDFQSSF, SEETTTGVHH, and WNNDAFQSSTN) were synthesized for sensory evaluation and kinetic analysis. The result showed that the umami thresholds of the five peptides were in the range of 0.21-0.40 mmol/L. Notably, REESDFQSSF, SEETTTGVHH, and WNNDAFQSSTN had low dissociation constant (KD) values and high affinity for the T1R1-VFT receptor. The enhancing effect of the three peptides with MSG or IMP was investigated by sensory evaluation, kinetic analysis, and molecular dynamics simulations. In stable complexes, ARG_277 in T1R1 played a major role in umami peptide binding to T1R1-VFT. These results provide a theoretical basis for future screening of umami peptides and improving the umami taste of food containing mushrooms.
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Affiliation(s)
- Yeling Xu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Gaole Chen
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Zhiyong Cui
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Yueming Wang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Wenli Wang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Imre Blank
- Zhejiang Yiming Food Co., Ltd., Jiuting Center Huting North Street No.199, Shanghai 201600, China
| | - Yin Zhang
- Key Laboratory of Meat Processing of Sichuan, Chengdu University, Chengdu 610106, China
| | - Changhua Xu
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Yan Yang
- Institute of Edible Fungi, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
| | - Yuan Liu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
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6
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Meng H, Cui Z, Yu Y, Li Y, Jiang S, Liu Y. From Molecular Dynamics to Taste Sensory Perception: A Comprehensive Study on the Interaction of Umami Peptides with the T1R1/T1R3-VFT Receptor. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:6533-6543. [PMID: 38488059 DOI: 10.1021/acs.jafc.3c09598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
The research on the umami receptor-ligand interaction is crucial for understanding umami perception. This study integrated molecular simulations, sensory evaluation, and biosensor technology to analyze the interaction between umami peptides and the umami receptor T1R1/T1R3-VFT. Molecular dynamics simulations were used to investigate the dissociation process of seven umami peptides with the umami receptor T1R1/T1R3-VFT, and by calculating the potential mean force curve using the Jarzynski equation, it was found that the binding free energy of umami peptide is between -58.80 and -12.17 kcal/mol, which had a strong correlation with the umami intensity obtained by time intensity sensory evaluation. Through correlation analysis, the dissociation rate constants (0.0126-0.394 1/s) of umami peptides were found to have a great impact on umami perception. The faster the dissociation rate of umami peptides from receptors, the stronger the perceived intensity of the umami taste. This research aims to elucidate the relationship between the umami peptide-receptor interaction and umami perception, providing theoretical support for the exploration of umami perception mechanisms.
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Affiliation(s)
- Hengli Meng
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhiyong Cui
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanyang Yu
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yingqiu Li
- Secondary College of Cereals and Tourism, Guangxi Vocational College of Technology and Business, Nanning 530005, China
| | - Shui Jiang
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuan Liu
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
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Cui Z, Meng H, Zhou T, Yu Y, Gu J, Zhang Z, Zhu Y, Zhang Y, Liu Y, Wang W. Noteworthy Consensus Effects of D/E Residues in Umami Peptides Used for Designing the Novel Umami Peptides. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:2789-2800. [PMID: 38278623 DOI: 10.1021/acs.jafc.3c07026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
Aspartic acid (D) and glutamic acid (E) play vital roles in the umami peptides. To understand their exact mechanism of action, umami peptides were collected and cut into 1/2/3/4 fragments. Connecting D/E to the N/C-termini of the fragments formed D/E consensus effect groups (DEEGs), and all fragments containing DEEG were summarized according to the ratio and ranking obtained in the above four situations. The interaction patterns between peptides in DEEG and T1R1/T1R3-VFD were compared by statistical analysis and molecular docking, and the most conservative contacts were found to be HdB_277_ARG and HdB_148_SER. The molecular docking score of the effector peptides significantly dropped compared to that of their original peptides (-1.076 ± 0.658 kcal/mol, p value < 0.05). Six types of consensus fingerprints were set according to the Top7 contacts. The exponential of relative umami was linearly correlated with ΔGbind (R2 = 0.961). Under the D/E consensus effect, the electrostatic effect of the umami peptide was improved, and the energy gap between the highest occupied molecular orbital-the least unoccupied molecular orbital (HOMO-LUMO) was decreased. The shortest path map showed that the peptides had similar T1R1-T1R3 recognition pathways. This study helps to reveal umami perception rules and provides support for the efficient screening of umami peptides based on the material richness in D/E sequences.
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Affiliation(s)
- Zhiyong Cui
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Hengli Meng
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Tianxing Zhou
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
- Department of Bioinformatics, Faculty of Science, The University of Melbourne, Victoria 3010, Australia
| | - Yanyang Yu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Jiaming Gu
- College of Humanities and Development Studies, China Agricultural University, Beijing 100083, P. R. China
| | - Zhiwei Zhang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Yiwen Zhu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Yin Zhang
- Key Laboratory of Meat Processing of Sichuan, Chengdu University, Chengdu 610106, P. R. China
| | - Yuan Liu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Wenli Wang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
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Zhu Y, Chen S, Yin H, Han X, Xu M, Wang W, Zhang Y, Feng X, Liu Y. Classification of oolong tea varieties based on computer vision and convolutional neural networks. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:1630-1637. [PMID: 37842747 DOI: 10.1002/jsfa.13049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/10/2023] [Accepted: 10/16/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND In the contemporary food industry, accurate and rapid differentiation of oolong tea varieties holds paramount importance for traceability and quality control. However, achieving this remains a formidable challenge. This study addresses this lacuna by employing machine learning algorithms - namely support vector machines (SVMs) and convolutional neural networks (CNNs) - alongside computer vision techniques for the automated classification of oolong tea leaves based on visual attributes. RESULTS An array of 13 distinct characteristics, encompassing color and texture, were identified from five unique oolong tea varieties. To fortify the robustness of the predictive models, data augmentation and image cropping methods were employed. A comparative analysis of SVM- and CNN-based models revealed that the ResNet50 model achieved a high Top-1 accuracy rate exceeding 93%. This robust performance substantiates the efficacy of the implemented methodology for rapid and precise oolong tea classification. CONCLUSION The study elucidates that the integration of computer vision with machine learning algorithms constitutes a promising, non-invasive approach for the quick and accurate categorization of oolong tea varieties. The findings have significant ramifications for process monitoring, quality assurance, authenticity validation and adulteration detection within the tea industry. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Yiwen Zhu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Siyuan Chen
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Hanzhe Yin
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Xihao Han
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Menghan Xu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Wenli Wang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Yin Zhang
- Key Laboratory of Meat Processing of Sichuan, Chengdu University, Chengdu, China
| | - Xiaoxiao Feng
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Yuan Liu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
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Li Z, Li D, Pan D, Xia Q, Sun Y, Du L, He J, Zhou C, Geng F, Cao J. Insights into the mechanism of extracellular proteases from Penicillium on myofibrillar protein hydrolysis and volatile compound evolutions. Food Res Int 2024; 175:113774. [PMID: 38129063 DOI: 10.1016/j.foodres.2023.113774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/06/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023]
Abstract
To investigate the mechanism of Penicillium proteases on the hydrolysis of myofibrillar protein (MP) and volatile compound evolutions, enzymatic characteristics of Penicillium proteases, hydrolysis capacities for MP, interactions between Penicillium proteases and MP, and profile changes of volatile compounds were investigated. P. aethiopicum (PA) and P. chrysogenum (PC) proteases showed the largest hydrolysis activities at pH 9.0 and 7.0, and were identified as alkaline serine protease and serine protease by LC-MS/MS, respectively. The proteases of PA and PC significantly degraded myosin and actin, and PA protease showed higher hydrolysis capacity for myosin than that of PC protease, which was confirmed by higher proteolysis index (56.06 %) and lower roughness (3.99 nm) of MP after PA treatment. Molecular docking revealed that hydrogen bond and hydrophobic interaction were the major interaction forces of Penicillium proteases with myosin and actin, and PA protease showed more binding sites with myosin compared with PC protease. The total content of free amino acids increased to 6.02-fold for PA treatment and to 5.51-fold for PC treatment after 4 h hydrolysis of MP, respectively. GC-MS showed that aromatic aldehydes and pyrazines in PA showed the largest increase compared with the control and PC during the hydrolysis of MP. Correlation analysis demonstrated that Phe, Leu and Ile were positively related with the accumulation of benzaldehyde, benzeneacetaldehyde, 2,4-dimethyl benzaldehyde and 2,5-dimethyl pyrazine.
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Affiliation(s)
- Zimu Li
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province; College of Food Science and Pharmaceutical Sciences, Ningbo University, Ningbo 315211, China
| | - Danni Li
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province; College of Food Science and Pharmaceutical Sciences, Ningbo University, Ningbo 315211, China
| | - Daodong Pan
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province; College of Food Science and Pharmaceutical Sciences, Ningbo University, Ningbo 315211, China
| | - Qiang Xia
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province; College of Food Science and Pharmaceutical Sciences, Ningbo University, Ningbo 315211, China
| | - Yangying Sun
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province; College of Food Science and Pharmaceutical Sciences, Ningbo University, Ningbo 315211, China
| | - Lihui Du
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province; College of Food Science and Pharmaceutical Sciences, Ningbo University, Ningbo 315211, China
| | - Jun He
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province; College of Food Science and Pharmaceutical Sciences, Ningbo University, Ningbo 315211, China
| | - Changyu Zhou
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province; College of Food Science and Pharmaceutical Sciences, Ningbo University, Ningbo 315211, China; China Food Flavor and Nutrition Health Innovation Center, Beijing Technology and Business University, Beijing 100048, China.
| | - Fang Geng
- Meat Processing Key Laboratory of Sichuan Province, School of Food and Biological Engineering, Chengdu University, No. 2025 Chengluo Avenue, Chengdu 610106, China
| | - Jinxuan Cao
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province; College of Food Science and Pharmaceutical Sciences, Ningbo University, Ningbo 315211, China; China Food Flavor and Nutrition Health Innovation Center, Beijing Technology and Business University, Beijing 100048, China.
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10
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Ayres L, Benavidez T, Varillas A, Linton J, Whitehead DC, Garcia CD. Predicting Antioxidant Synergism via Artificial Intelligence and Benchtop Data. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:15644-15655. [PMID: 37796649 DOI: 10.1021/acs.jafc.3c05462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
Lipid oxidation is a major issue affecting products containing unsaturated fatty acids as ingredients or components, leading to the formation of low molecular weight species with diverse functional groups that impart off-odors and off-flavors. Aiming to control this process, antioxidants are commonly added to these products, often deployed as combinations of two or more compounds, a strategy that allows for lowering the amount used while boosting the total antioxidant capacity of the formulation. While this approach allows for minimizing the potential organoleptic and toxic effects of these compounds, predicting how these mixtures of antioxidants will behave has traditionally been one of the most challenging tasks, often leading to simple additive, antagonistic, or synergistic effects. Approaches to understanding these interactions have been predominantly empirically driven but thus far, inefficient and unable to account for the complexity and multifaceted nature of antioxidant responses. To address this current gap in knowledge, we describe the use of an artificial intelligence model based on deep learning architecture to predict the type of interaction (synergistic, additive, and antagonistic) of antioxidant combinations. Here, each mixture was associated with a combination index value (CI) and used as input for our model, which was challenged against a test (n = 140) data set. Despite the encouraging preliminary results, this algorithm failed to provide accurate predictions of oxidation experiments performed in-house using binary mixtures of phenolic antioxidants and a lard sample. To overcome this problem, the AI algorithm was then enhanced with various amounts of experimental data (antioxidant power data assessed by the TBARS assay), demonstrating the importance of having chemically relevant experimental data to enhance the model's performance and provide suitable predictions with statistical relevance. We believe the proposed method could be used as an auxiliary tool in benchmark analysis routines, offering a novel strategy to enable broader and more rational predictions related to the behavior of antioxidant mixtures.
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Affiliation(s)
- Lucas Ayres
- Department of Chemistry, Clemson University, Clemson, South Carolina 29634, United States
| | - Tomás Benavidez
- INFIQC-CONICET, Department of Physical Chemistry, National University of Córdoba, Cordoba 5000, Argentina
| | - Armelle Varillas
- South Carolina Governor's School for Science and Mathematics, Hartsville, South Carolina 29550, United States
| | - Jeb Linton
- Department of Chemistry, Clemson University, Clemson, South Carolina 29634, United States
| | - Daniel C Whitehead
- Department of Chemistry, Clemson University, Clemson, South Carolina 29634, United States
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11
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Cui Z, Wu B, Blank I, Yu Y, Gu J, Zhou T, Zhang Y, Wang W, Liu Y. TastePeptides-EEG: An Ensemble Model for Umami Taste Evaluation Based on Electroencephalogram and Machine Learning. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:13430-13439. [PMID: 37639501 DOI: 10.1021/acs.jafc.3c04611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
In the field of food, the sensory evaluation of food still relies on the results of manual sensory evaluation, but the results of human sensory evaluation are not universal, and there is a problem of speech fraud. This work proposed an electroencephalography (EEG)-based analysis method that effectively enables the identification of umami/non-umami substances. First, the key features were extracted using percentage conversion, standardization, and significance screening, and based on these features, the top four models were selected from 19 common binary classification algorithms as submodels. Then, the support vector machine (SVM) algorithm was used to fit the outputs of these four submodels to establish TastePeptides-EEG. The validation set of the model achieved a judgment accuracy of 90.2%, and the test set achieved a judgment accuracy of 77.8%. This study discovered the frequency change of α wave in umami taste perception and found the frequency response delay phenomenon of the F/RT/C area under umami taste stimulation for the first time. The model is published at www.tastepeptides-meta.com/TastePeptides-EEG, which is convenient for relevant researchers to speed up the analysis of umami perception and provide help for the development of the next generation of brain-computer interfaces for flavor perception.
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Affiliation(s)
- Zhiyong Cui
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ben Wu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Imre Blank
- Zhejiang Yiming Food Co, Ltd., Huting North Street 199, Shanghai 201615, China
| | - Yashu Yu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jiaming Gu
- College of Humanities and Development Studies, China Agricultural University, Beijing 100094 China
| | - Tianxing Zhou
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Bioinformatics, Faculty of Science, The University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Yin Zhang
- Key Laboratory of Meat Processing of Sichuan, Chengdu University, Chengdu 610106, China
| | - Wenli Wang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuan Liu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, China
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12
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Li M, Zhang X, Zhu Y, Zhang X, Cui Z, Zhang N, Sun Y, Yang Z, Wang W, Wang C, Zhang Y, Liu Y, Qing G. Identifying Umami Peptides Specific to the T1R1/T1R3 Receptor via Phage Display. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:12004-12014. [PMID: 37523494 DOI: 10.1021/acs.jafc.3c02471] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Umami peptides are small molecular weight oligopeptides that play a role in umami taste attributes. However, the identification of umami peptides is easily limited by environmental conditions, and the abundant source and high chromatographic separation efficiency remain difficult. Herein, we report a robust strategy based on a phage random linear heptapeptide library that targets the T1R1-Venus flytrap domain (T1R1-VFT). Two candidate peptides (MTLERPW and MNLHLSF) were readily identified with high affinity for T1R1-VFT binding (KD of MW-7 and MF-7 were 790 and 630 nM, respectively). The two peptides exhibited umami taste and significantly enhanced the umami intensity when added to the monosodium glutamate solution. Overall, this strategy shows that umami peptides could be developed via phage display technology for the first time. The phage display platform has a promising application to discover other taste peptides with affinity for taste receptors of interest and has more room for improvement in the future.
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Affiliation(s)
- Mingyang Li
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China
| | - Xiaoyu Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China
| | - Yiwen Zhu
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Xiancheng Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China
| | - Zhiyong Cui
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Ninglong Zhang
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Yue Sun
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China
| | - Zhiying Yang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China
| | - Wenli Wang
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Cunli Wang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China
| | - Yin Zhang
- Key Laboratory of Meat Processing of Sichuan, Chengdu University, Chengdu 610106, PR China
| | - Yuan Liu
- Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Guangyan Qing
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China
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Yu Y, Jiang S, Cui Z, Zhang N, Li M, Liu J, Meng H, Wang S, Zhang Y, Han J, Sun X, Zhao W, Liu Y. Bimetallic bionic taste sensor for perception of the synergistic effect of umami substances. Biosens Bioelectron 2023; 234:115357. [PMID: 37149968 DOI: 10.1016/j.bios.2023.115357] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/22/2023] [Accepted: 04/26/2023] [Indexed: 05/09/2023]
Abstract
Synergistic effect is one of the main properties of umami substances, elucidating the synergistic effect of umami is of great significance in the food industry. In this study, a bimetallic bionic taste sensor was developed to evaluate the synergistic effect of umami substances based on the perceptual mechanism of the human taste system. The Venus flytrap domain of T1R1 which is in charge of recognizing umami ligands was employed as the sensing element and self-assembled on the bimetallic nanomaterial (MoS2-PtPd) by Au-S bonding, the binding of receptors and ligands is characterized by changes of electrical signals. The sensor had good linearity (R2 > 0.99) and wide detection range in the detection of different kinds of umami substances (amino acids, nucleotides, organic acids, umami peptides) with detection limits as low as 0.03 pM. Comparing with electronic tongues, the sensor owned multiple characteristics of human taste system and could recognize the presence of synergistic effect of umami substances in a variety of real samples. Moreover, the differences in synergistic effect at different concentrations and ratios were also explored, the findings showed that the synergistic effect was more obvious at lower concentrations and balanced ratios of multiple umami substances added. The strategy would afford a promising platform for in-depth research on the mechanism of synergistic effect and multifunctional industrial applications.
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Affiliation(s)
- Yanyang Yu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China; School of Agricultural Engineering and Food Science, Shandong University of Technology, No.266 Xincun Xilu, Zibo, 255049, China
| | - Shui Jiang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Zhiyong Cui
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Ninglong Zhang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Mingyang Li
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jing Liu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China; School of Agricultural Engineering and Food Science, Shandong University of Technology, No.266 Xincun Xilu, Zibo, 255049, China
| | - Hengli Meng
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Shengnan Wang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yin Zhang
- Key Laboratory of Meat Processing of Sichuan, Chengdu University, Chengdu, 610106, China
| | - Jie Han
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No.266 Xincun Xilu, Zibo, 255049, China
| | - Xia Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No.266 Xincun Xilu, Zibo, 255049, China
| | - Wenping Zhao
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No.266 Xincun Xilu, Zibo, 255049, China.
| | - Yuan Liu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China; School of Agricultural Engineering and Food Science, Shandong University of Technology, No.266 Xincun Xilu, Zibo, 255049, China.
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