In Silico Discovery and Sensory Validation of Umami Peptides in Fermented Sausages: A Study Integrating Deep Learning and Molecular Modeling
Deep learning has great potential in the field of functional peptide prediction. This study combines metagenomics and deep learning to efficiently discover potential umami peptides in fermented sausages. A candidate peptide library was generated using metagenomic data from fermented sausages, an int...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/14/14/2422 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850077667488956416 |
|---|---|
| author | Haochen Geng Chunming Xu Huijun Ma Youxu Dai Ziyou Jiang Mingyue Yang Danyang Zhu |
| author_facet | Haochen Geng Chunming Xu Huijun Ma Youxu Dai Ziyou Jiang Mingyue Yang Danyang Zhu |
| author_sort | Haochen Geng |
| collection | DOAJ |
| description | Deep learning has great potential in the field of functional peptide prediction. This study combines metagenomics and deep learning to efficiently discover potential umami peptides in fermented sausages. A candidate peptide library was generated using metagenomic data from fermented sausages, an integrated deep learning model was constructed for prediction, and SHAP (SHapley Additive exPlanations) interpretability analysis was performed to elucidate the key amino acid features and contributions of the model in predicting umami peptides, screening the top ten peptides with the highest predicted probability. Subsequently, molecular docking was performed to assess the binding stability of these peptides with the umami receptor T1R1/T1R3, selecting the three peptides DDSMAATGL, DGEEDASM, and DEEEVDI with the most stable binding for further study. Docking analysis revealed the important roles of the key receptor residues Glu301, Arg277, Lys328, and His71 in hydrogen bond formation. Molecular dynamics simulations validated the robust integrity of the peptide–receptor associations. Finally, sensory evaluation demonstrated that these three peptides possessed significant umami characteristics, with low umami thresholds (0.11, 0.37, and 0.44 mg/mL, respectively). This study, based on metagenomics and deep learning, provides a high-throughput strategy for the discovery and validation of functional peptides. |
| format | Article |
| id | doaj-art-1e490f3692ec4ed6ab4bf44112ecb296 |
| institution | DOAJ |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-1e490f3692ec4ed6ab4bf44112ecb2962025-08-20T02:45:45ZengMDPI AGFoods2304-81582025-07-011414242210.3390/foods14142422In Silico Discovery and Sensory Validation of Umami Peptides in Fermented Sausages: A Study Integrating Deep Learning and Molecular ModelingHaochen Geng0Chunming Xu1Huijun Ma2Youxu Dai3Ziyou Jiang4Mingyue Yang5Danyang Zhu6School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Languages and Communication, Beijing Technology and Business University, Beijing 100048, ChinaDeep learning has great potential in the field of functional peptide prediction. This study combines metagenomics and deep learning to efficiently discover potential umami peptides in fermented sausages. A candidate peptide library was generated using metagenomic data from fermented sausages, an integrated deep learning model was constructed for prediction, and SHAP (SHapley Additive exPlanations) interpretability analysis was performed to elucidate the key amino acid features and contributions of the model in predicting umami peptides, screening the top ten peptides with the highest predicted probability. Subsequently, molecular docking was performed to assess the binding stability of these peptides with the umami receptor T1R1/T1R3, selecting the three peptides DDSMAATGL, DGEEDASM, and DEEEVDI with the most stable binding for further study. Docking analysis revealed the important roles of the key receptor residues Glu301, Arg277, Lys328, and His71 in hydrogen bond formation. Molecular dynamics simulations validated the robust integrity of the peptide–receptor associations. Finally, sensory evaluation demonstrated that these three peptides possessed significant umami characteristics, with low umami thresholds (0.11, 0.37, and 0.44 mg/mL, respectively). This study, based on metagenomics and deep learning, provides a high-throughput strategy for the discovery and validation of functional peptides.https://www.mdpi.com/2304-8158/14/14/2422deep learningmolecular dockingumami peptidespeptide prediction |
| spellingShingle | Haochen Geng Chunming Xu Huijun Ma Youxu Dai Ziyou Jiang Mingyue Yang Danyang Zhu In Silico Discovery and Sensory Validation of Umami Peptides in Fermented Sausages: A Study Integrating Deep Learning and Molecular Modeling Foods deep learning molecular docking umami peptides peptide prediction |
| title | In Silico Discovery and Sensory Validation of Umami Peptides in Fermented Sausages: A Study Integrating Deep Learning and Molecular Modeling |
| title_full | In Silico Discovery and Sensory Validation of Umami Peptides in Fermented Sausages: A Study Integrating Deep Learning and Molecular Modeling |
| title_fullStr | In Silico Discovery and Sensory Validation of Umami Peptides in Fermented Sausages: A Study Integrating Deep Learning and Molecular Modeling |
| title_full_unstemmed | In Silico Discovery and Sensory Validation of Umami Peptides in Fermented Sausages: A Study Integrating Deep Learning and Molecular Modeling |
| title_short | In Silico Discovery and Sensory Validation of Umami Peptides in Fermented Sausages: A Study Integrating Deep Learning and Molecular Modeling |
| title_sort | in silico discovery and sensory validation of umami peptides in fermented sausages a study integrating deep learning and molecular modeling |
| topic | deep learning molecular docking umami peptides peptide prediction |
| url | https://www.mdpi.com/2304-8158/14/14/2422 |
| work_keys_str_mv | AT haochengeng insilicodiscoveryandsensoryvalidationofumamipeptidesinfermentedsausagesastudyintegratingdeeplearningandmolecularmodeling AT chunmingxu insilicodiscoveryandsensoryvalidationofumamipeptidesinfermentedsausagesastudyintegratingdeeplearningandmolecularmodeling AT huijunma insilicodiscoveryandsensoryvalidationofumamipeptidesinfermentedsausagesastudyintegratingdeeplearningandmolecularmodeling AT youxudai insilicodiscoveryandsensoryvalidationofumamipeptidesinfermentedsausagesastudyintegratingdeeplearningandmolecularmodeling AT ziyoujiang insilicodiscoveryandsensoryvalidationofumamipeptidesinfermentedsausagesastudyintegratingdeeplearningandmolecularmodeling AT mingyueyang insilicodiscoveryandsensoryvalidationofumamipeptidesinfermentedsausagesastudyintegratingdeeplearningandmolecularmodeling AT danyangzhu insilicodiscoveryandsensoryvalidationofumamipeptidesinfermentedsausagesastudyintegratingdeeplearningandmolecularmodeling |