MetNC: Predicting Metabolites in vivo for Natural Compounds
Natural compounds (NCs) undergo complicated biotransformation in vivo to produce diverse forms of metabolites dynamically, many of which are of high medicinal value. Predicting the profiles of chemical products may help to narrow down possible candidates, yet current computational methods for predic...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Chemistry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2022.881975/full |
| _version_ | 1818200943924084736 |
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| author | Zikun Chen Deyu Yan Mou Zhang Wenhao Han Yuan Wang Shudi Xu Kailin Tang Jian Gao Jian Gao Zhiwei Cao Zhiwei Cao |
| author_facet | Zikun Chen Deyu Yan Mou Zhang Wenhao Han Yuan Wang Shudi Xu Kailin Tang Jian Gao Jian Gao Zhiwei Cao Zhiwei Cao |
| author_sort | Zikun Chen |
| collection | DOAJ |
| description | Natural compounds (NCs) undergo complicated biotransformation in vivo to produce diverse forms of metabolites dynamically, many of which are of high medicinal value. Predicting the profiles of chemical products may help to narrow down possible candidates, yet current computational methods for predicting biotransformation largely focus on synthetic compounds. Here, we proposed a method of MetNC, a tailor-made method for NC biotransformation prediction, after exploring the overall patterns of NC in vivo metabolism. Based on 850 pairs of the biotransformation dataset validated by comprehensive in vivo experiments with sourcing compounds from medicinal plants, MetNC was designed to produce a list of potential metabolites through simulating in vivo biotransformation and then prioritize true metabolites into the top list according to the functional groups in compound structures and steric hindrance around the reaction sites. Among the well-known peers of GLORYx and BioTransformer, MetNC gave the highest performance in both the metabolite coverage and the ability to short-list true products. More importantly, MetNC seemed to display an extra advantage in recommending the microbiota-transformed metabolites, suggesting its potential usefulness in the overall metabolism estimation. In summary, complemented to those techniques focusing on synthetic compounds, MetNC may help to fill the gap of natural compound metabolism and narrow down those products likely to be identified in vivo. |
| first_indexed | 2024-12-12T02:45:42Z |
| format | Article |
| id | doaj.art-b953790136a34fee9839902cdf9cd325 |
| institution | Directory Open Access Journal |
| issn | 2296-2646 |
| language | English |
| last_indexed | 2024-12-12T02:45:42Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemistry |
| spelling | doaj.art-b953790136a34fee9839902cdf9cd3252022-12-22T00:41:03ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462022-05-011010.3389/fchem.2022.881975881975MetNC: Predicting Metabolites in vivo for Natural CompoundsZikun Chen0Deyu Yan1Mou Zhang2Wenhao Han3Yuan Wang4Shudi Xu5Kailin Tang6Jian Gao7Jian Gao8Zhiwei Cao9Zhiwei Cao10Dept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, ChinaDept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, ChinaDept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, ChinaDept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, ChinaDept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, ChinaDept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, ChinaDept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, ChinaInternational Human Phenome Institutes, Shanghai, ChinaDepartment of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, ChinaDept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, ChinaSchool of Life Sciences, Fudan University, Shanghai, ChinaNatural compounds (NCs) undergo complicated biotransformation in vivo to produce diverse forms of metabolites dynamically, many of which are of high medicinal value. Predicting the profiles of chemical products may help to narrow down possible candidates, yet current computational methods for predicting biotransformation largely focus on synthetic compounds. Here, we proposed a method of MetNC, a tailor-made method for NC biotransformation prediction, after exploring the overall patterns of NC in vivo metabolism. Based on 850 pairs of the biotransformation dataset validated by comprehensive in vivo experiments with sourcing compounds from medicinal plants, MetNC was designed to produce a list of potential metabolites through simulating in vivo biotransformation and then prioritize true metabolites into the top list according to the functional groups in compound structures and steric hindrance around the reaction sites. Among the well-known peers of GLORYx and BioTransformer, MetNC gave the highest performance in both the metabolite coverage and the ability to short-list true products. More importantly, MetNC seemed to display an extra advantage in recommending the microbiota-transformed metabolites, suggesting its potential usefulness in the overall metabolism estimation. In summary, complemented to those techniques focusing on synthetic compounds, MetNC may help to fill the gap of natural compound metabolism and narrow down those products likely to be identified in vivo.https://www.frontiersin.org/articles/10.3389/fchem.2022.881975/fullnatural compoundsin vivo biotransformationmetabolitespredictionreaction rules |
| spellingShingle | Zikun Chen Deyu Yan Mou Zhang Wenhao Han Yuan Wang Shudi Xu Kailin Tang Jian Gao Jian Gao Zhiwei Cao Zhiwei Cao MetNC: Predicting Metabolites in vivo for Natural Compounds Frontiers in Chemistry natural compounds in vivo biotransformation metabolites prediction reaction rules |
| title | MetNC: Predicting Metabolites in vivo for Natural Compounds |
| title_full | MetNC: Predicting Metabolites in vivo for Natural Compounds |
| title_fullStr | MetNC: Predicting Metabolites in vivo for Natural Compounds |
| title_full_unstemmed | MetNC: Predicting Metabolites in vivo for Natural Compounds |
| title_short | MetNC: Predicting Metabolites in vivo for Natural Compounds |
| title_sort | metnc predicting metabolites in vivo for natural compounds |
| topic | natural compounds in vivo biotransformation metabolites prediction reaction rules |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2022.881975/full |
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