Data-Driven Prediction of Molecular Biotransformations in Food Fermentation
Fermentation products, together with food components, determine the sense, nutrition, and safety of fermented foods. Traditional methods of fermentation product identification are time-consuming and cumbersome, which cannot meet the increasing need for the identification of the extensive bioactive m...
Gespeichert in:
| Veröffentlicht in: | Journal of agricultural and food chemistry 2023-06, Vol.71 (22), p.8488-8496 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 8496 |
|---|---|
| container_issue | 22 |
| container_start_page | 8488 |
| container_title | Journal of agricultural and food chemistry |
| container_volume | 71 |
| creator | Zhang, Dachuan Jia, Cancan Sun, Dandan Gao, Chukun Fu, Dongheng Cai, Pengli Hu, Qian-Nan |
| description | Fermentation products, together with food components, determine the sense, nutrition, and safety of fermented foods. Traditional methods of fermentation product identification are time-consuming and cumbersome, which cannot meet the increasing need for the identification of the extensive bioactive metabolites produced during food fermentation. Hence, we propose a data-driven integrated platform (FFExplorer, http://www.rxnfinder.org/ffexplorer/) based on machine learning and data on 2,192,862 microbial sequence-encoded enzymes for computational prediction of fermentation products. Using FFExplorer, we explained the mechanism behind the disappearance of spicy taste during pepper fermentation and evaluated the detoxification effects of microbial fermentation for common food contaminants. FFExplorer will provide a valuable reference for inferring bioactive “dark matter” in fermented foods and exploring the application potential of microorganisms. |
| doi_str_mv | 10.1021/acs.jafc.3c01172 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2818057587</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2818057587</sourcerecordid><originalsourceid>FETCH-LOGICAL-a336t-4d834e9b37bc5c7e3da774500d338e05172739df25c822eff4e41c79685a20e73</originalsourceid><addsrcrecordid>eNp1kDFPwzAQRi0EoqWwM6GMDKSc7bh2RmgpIIpggNlynbOUKonBTpD49yRtYWM66e59n3SPkHMKUwqMXhsbpxvj7JRboFSyAzKmgkEqKFWHZAw9kyoxoyNyEuMGAJSQcExGXDKq8jwbk6eFaU26COUXNslrwKK0bembxLvk2Vdou8qE5Lb0bTBNdD7UZjjHpGySpfdFssRQY9Nut6fkyJkq4tl-Tsj78u5t_pCuXu4f5zer1HA-a9OsUDzDfM3l2gorkRdGykwAFJwrBNH_IXleOCasYgydyzCjVuYzJQwDlHxCLne9H8F_dhhbXZfRYlWZBn0XNVNUgZBCDSjsUBt8jAGd_ghlbcK3pqAHhbpXqAeFeq-wj1zs27t1jcVf4NdZD1ztgG3Ud6Hpn_2_7wdcyHxs</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2818057587</pqid></control><display><type>article</type><title>Data-Driven Prediction of Molecular Biotransformations in Food Fermentation</title><source>MEDLINE</source><source>American Chemical Society Journals</source><creator>Zhang, Dachuan ; Jia, Cancan ; Sun, Dandan ; Gao, Chukun ; Fu, Dongheng ; Cai, Pengli ; Hu, Qian-Nan</creator><creatorcontrib>Zhang, Dachuan ; Jia, Cancan ; Sun, Dandan ; Gao, Chukun ; Fu, Dongheng ; Cai, Pengli ; Hu, Qian-Nan</creatorcontrib><description>Fermentation products, together with food components, determine the sense, nutrition, and safety of fermented foods. Traditional methods of fermentation product identification are time-consuming and cumbersome, which cannot meet the increasing need for the identification of the extensive bioactive metabolites produced during food fermentation. Hence, we propose a data-driven integrated platform (FFExplorer, http://www.rxnfinder.org/ffexplorer/) based on machine learning and data on 2,192,862 microbial sequence-encoded enzymes for computational prediction of fermentation products. Using FFExplorer, we explained the mechanism behind the disappearance of spicy taste during pepper fermentation and evaluated the detoxification effects of microbial fermentation for common food contaminants. FFExplorer will provide a valuable reference for inferring bioactive “dark matter” in fermented foods and exploring the application potential of microorganisms.</description><identifier>ISSN: 0021-8561</identifier><identifier>EISSN: 1520-5118</identifier><identifier>DOI: 10.1021/acs.jafc.3c01172</identifier><identifier>PMID: 37218994</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><subject>Biotechnology and Biological Transformations ; Fermentation ; Fermented Foods ; Food ; Food Microbiology</subject><ispartof>Journal of agricultural and food chemistry, 2023-06, Vol.71 (22), p.8488-8496</ispartof><rights>2023 American Chemical Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a336t-4d834e9b37bc5c7e3da774500d338e05172739df25c822eff4e41c79685a20e73</citedby><cites>FETCH-LOGICAL-a336t-4d834e9b37bc5c7e3da774500d338e05172739df25c822eff4e41c79685a20e73</cites><orcidid>0000-0003-2467-6286 ; 0000-0002-7910-6558</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/acs.jafc.3c01172$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/acs.jafc.3c01172$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>314,780,784,2756,27067,27915,27916,56729,56779</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37218994$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Dachuan</creatorcontrib><creatorcontrib>Jia, Cancan</creatorcontrib><creatorcontrib>Sun, Dandan</creatorcontrib><creatorcontrib>Gao, Chukun</creatorcontrib><creatorcontrib>Fu, Dongheng</creatorcontrib><creatorcontrib>Cai, Pengli</creatorcontrib><creatorcontrib>Hu, Qian-Nan</creatorcontrib><title>Data-Driven Prediction of Molecular Biotransformations in Food Fermentation</title><title>Journal of agricultural and food chemistry</title><addtitle>J. Agric. Food Chem</addtitle><description>Fermentation products, together with food components, determine the sense, nutrition, and safety of fermented foods. Traditional methods of fermentation product identification are time-consuming and cumbersome, which cannot meet the increasing need for the identification of the extensive bioactive metabolites produced during food fermentation. Hence, we propose a data-driven integrated platform (FFExplorer, http://www.rxnfinder.org/ffexplorer/) based on machine learning and data on 2,192,862 microbial sequence-encoded enzymes for computational prediction of fermentation products. Using FFExplorer, we explained the mechanism behind the disappearance of spicy taste during pepper fermentation and evaluated the detoxification effects of microbial fermentation for common food contaminants. FFExplorer will provide a valuable reference for inferring bioactive “dark matter” in fermented foods and exploring the application potential of microorganisms.</description><subject>Biotechnology and Biological Transformations</subject><subject>Fermentation</subject><subject>Fermented Foods</subject><subject>Food</subject><subject>Food Microbiology</subject><issn>0021-8561</issn><issn>1520-5118</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kDFPwzAQRi0EoqWwM6GMDKSc7bh2RmgpIIpggNlynbOUKonBTpD49yRtYWM66e59n3SPkHMKUwqMXhsbpxvj7JRboFSyAzKmgkEqKFWHZAw9kyoxoyNyEuMGAJSQcExGXDKq8jwbk6eFaU26COUXNslrwKK0bembxLvk2Vdou8qE5Lb0bTBNdD7UZjjHpGySpfdFssRQY9Nut6fkyJkq4tl-Tsj78u5t_pCuXu4f5zer1HA-a9OsUDzDfM3l2gorkRdGykwAFJwrBNH_IXleOCasYgydyzCjVuYzJQwDlHxCLne9H8F_dhhbXZfRYlWZBn0XNVNUgZBCDSjsUBt8jAGd_ghlbcK3pqAHhbpXqAeFeq-wj1zs27t1jcVf4NdZD1ztgG3Ud6Hpn_2_7wdcyHxs</recordid><startdate>20230607</startdate><enddate>20230607</enddate><creator>Zhang, Dachuan</creator><creator>Jia, Cancan</creator><creator>Sun, Dandan</creator><creator>Gao, Chukun</creator><creator>Fu, Dongheng</creator><creator>Cai, Pengli</creator><creator>Hu, Qian-Nan</creator><general>American Chemical Society</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-2467-6286</orcidid><orcidid>https://orcid.org/0000-0002-7910-6558</orcidid></search><sort><creationdate>20230607</creationdate><title>Data-Driven Prediction of Molecular Biotransformations in Food Fermentation</title><author>Zhang, Dachuan ; Jia, Cancan ; Sun, Dandan ; Gao, Chukun ; Fu, Dongheng ; Cai, Pengli ; Hu, Qian-Nan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a336t-4d834e9b37bc5c7e3da774500d338e05172739df25c822eff4e41c79685a20e73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Biotechnology and Biological Transformations</topic><topic>Fermentation</topic><topic>Fermented Foods</topic><topic>Food</topic><topic>Food Microbiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Dachuan</creatorcontrib><creatorcontrib>Jia, Cancan</creatorcontrib><creatorcontrib>Sun, Dandan</creatorcontrib><creatorcontrib>Gao, Chukun</creatorcontrib><creatorcontrib>Fu, Dongheng</creatorcontrib><creatorcontrib>Cai, Pengli</creatorcontrib><creatorcontrib>Hu, Qian-Nan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of agricultural and food chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Dachuan</au><au>Jia, Cancan</au><au>Sun, Dandan</au><au>Gao, Chukun</au><au>Fu, Dongheng</au><au>Cai, Pengli</au><au>Hu, Qian-Nan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data-Driven Prediction of Molecular Biotransformations in Food Fermentation</atitle><jtitle>Journal of agricultural and food chemistry</jtitle><addtitle>J. Agric. Food Chem</addtitle><date>2023-06-07</date><risdate>2023</risdate><volume>71</volume><issue>22</issue><spage>8488</spage><epage>8496</epage><pages>8488-8496</pages><issn>0021-8561</issn><eissn>1520-5118</eissn><abstract>Fermentation products, together with food components, determine the sense, nutrition, and safety of fermented foods. Traditional methods of fermentation product identification are time-consuming and cumbersome, which cannot meet the increasing need for the identification of the extensive bioactive metabolites produced during food fermentation. Hence, we propose a data-driven integrated platform (FFExplorer, http://www.rxnfinder.org/ffexplorer/) based on machine learning and data on 2,192,862 microbial sequence-encoded enzymes for computational prediction of fermentation products. Using FFExplorer, we explained the mechanism behind the disappearance of spicy taste during pepper fermentation and evaluated the detoxification effects of microbial fermentation for common food contaminants. FFExplorer will provide a valuable reference for inferring bioactive “dark matter” in fermented foods and exploring the application potential of microorganisms.</abstract><cop>United States</cop><pub>American Chemical Society</pub><pmid>37218994</pmid><doi>10.1021/acs.jafc.3c01172</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-2467-6286</orcidid><orcidid>https://orcid.org/0000-0002-7910-6558</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0021-8561 |
| ispartof | Journal of agricultural and food chemistry, 2023-06, Vol.71 (22), p.8488-8496 |
| issn | 0021-8561 1520-5118 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2818057587 |
| source | MEDLINE; American Chemical Society Journals |
| subjects | Biotechnology and Biological Transformations Fermentation Fermented Foods Food Food Microbiology |
| title | Data-Driven Prediction of Molecular Biotransformations in Food Fermentation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T22%3A19%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data-Driven%20Prediction%20of%20Molecular%20Biotransformations%20in%20Food%20Fermentation&rft.jtitle=Journal%20of%20agricultural%20and%20food%20chemistry&rft.au=Zhang,%20Dachuan&rft.date=2023-06-07&rft.volume=71&rft.issue=22&rft.spage=8488&rft.epage=8496&rft.pages=8488-8496&rft.issn=0021-8561&rft.eissn=1520-5118&rft_id=info:doi/10.1021/acs.jafc.3c01172&rft_dat=%3Cproquest_cross%3E2818057587%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2818057587&rft_id=info:pmid/37218994&rfr_iscdi=true |