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Expanding potential targets of herbal chemicals by node2vec based on herb–drug interactions

BACKGROUND: The identification of chemical–target interaction is key to pharmaceutical research and development, but the unclear materials basis and complex mechanisms of traditional medicine (TM) make it difficult, especially for low-content chemicals which are hard to test in experiments. In this...

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Autores principales: Zhang, Dai-yan, Cui, Wen-qing, Hou, Ling, Yang, Jing, Lyu, Li-yang, Wang, Ze-yu, Linghu, Ke-Gang, He, Wen-bin, Yu, Hua, Hu, Yuan-jia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10233865/
https://www.ncbi.nlm.nih.gov/pubmed/37264453
http://dx.doi.org/10.1186/s13020-023-00763-3
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author Zhang, Dai-yan
Cui, Wen-qing
Hou, Ling
Yang, Jing
Lyu, Li-yang
Wang, Ze-yu
Linghu, Ke-Gang
He, Wen-bin
Yu, Hua
Hu, Yuan-jia
author_facet Zhang, Dai-yan
Cui, Wen-qing
Hou, Ling
Yang, Jing
Lyu, Li-yang
Wang, Ze-yu
Linghu, Ke-Gang
He, Wen-bin
Yu, Hua
Hu, Yuan-jia
author_sort Zhang, Dai-yan
collection PubMed
description BACKGROUND: The identification of chemical–target interaction is key to pharmaceutical research and development, but the unclear materials basis and complex mechanisms of traditional medicine (TM) make it difficult, especially for low-content chemicals which are hard to test in experiments. In this research, we aim to apply the node2vec algorithm in the context of drug-herb interactions for expanding potential targets and taking advantage of molecular docking and experiments for verification. METHODS: Regarding the widely reported risks between cardiovascular drugs and herbs, Salvia miltiorrhiza (Danshen, DS) and Ligusticum chuanxiong (Chuanxiong, CX), which are widely used in the treatment of cardiovascular disease (CVD), and approved drugs for CVD form the new dataset as an example. Three data groups DS-drug, CX-drug, and DS-CX-drug were applied to serve as the context of drug-herb interactions for link prediction. Three types of datasets were set under three groups, containing information from chemical-target connection (CTC), chemical-chemical connection (CCC) and protein–protein interaction (PPI) in increasing steps. Five algorithms, including node2vec, were applied as comparisons. Molecular docking and pharmacological experiments were used for verification. RESULTS: Node2vec represented the best performance with average AUROC and AP values of 0.91 on the datasets “CTC, CCC, PPI”. Targets of 32 herbal chemicals were identified within 43 predicted edges of herbal chemicals and drug targets. Among them, 11 potential chemical-drug target interactions showed better binding affinity by molecular docking. Further pharmacological experiments indicated caffeic acid increased the thermal stability of the protein GGT1 and ligustilide and low-content chemical neocryptotanshinone induced mRNA change of FGF2 and MTNR1A, respectively. CONCLUSIONS: The analytical framework and methods established in the study provide an important reference for researchers in discovering herb–drug interactions, alerting clinical risks, and understanding complex mechanisms of TM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13020-023-00763-3.
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spelling pubmed-102338652023-06-02 Expanding potential targets of herbal chemicals by node2vec based on herb–drug interactions Zhang, Dai-yan Cui, Wen-qing Hou, Ling Yang, Jing Lyu, Li-yang Wang, Ze-yu Linghu, Ke-Gang He, Wen-bin Yu, Hua Hu, Yuan-jia Chin Med Research BACKGROUND: The identification of chemical–target interaction is key to pharmaceutical research and development, but the unclear materials basis and complex mechanisms of traditional medicine (TM) make it difficult, especially for low-content chemicals which are hard to test in experiments. In this research, we aim to apply the node2vec algorithm in the context of drug-herb interactions for expanding potential targets and taking advantage of molecular docking and experiments for verification. METHODS: Regarding the widely reported risks between cardiovascular drugs and herbs, Salvia miltiorrhiza (Danshen, DS) and Ligusticum chuanxiong (Chuanxiong, CX), which are widely used in the treatment of cardiovascular disease (CVD), and approved drugs for CVD form the new dataset as an example. Three data groups DS-drug, CX-drug, and DS-CX-drug were applied to serve as the context of drug-herb interactions for link prediction. Three types of datasets were set under three groups, containing information from chemical-target connection (CTC), chemical-chemical connection (CCC) and protein–protein interaction (PPI) in increasing steps. Five algorithms, including node2vec, were applied as comparisons. Molecular docking and pharmacological experiments were used for verification. RESULTS: Node2vec represented the best performance with average AUROC and AP values of 0.91 on the datasets “CTC, CCC, PPI”. Targets of 32 herbal chemicals were identified within 43 predicted edges of herbal chemicals and drug targets. Among them, 11 potential chemical-drug target interactions showed better binding affinity by molecular docking. Further pharmacological experiments indicated caffeic acid increased the thermal stability of the protein GGT1 and ligustilide and low-content chemical neocryptotanshinone induced mRNA change of FGF2 and MTNR1A, respectively. CONCLUSIONS: The analytical framework and methods established in the study provide an important reference for researchers in discovering herb–drug interactions, alerting clinical risks, and understanding complex mechanisms of TM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13020-023-00763-3. BioMed Central 2023-06-01 /pmc/articles/PMC10233865/ /pubmed/37264453 http://dx.doi.org/10.1186/s13020-023-00763-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhang, Dai-yan
Cui, Wen-qing
Hou, Ling
Yang, Jing
Lyu, Li-yang
Wang, Ze-yu
Linghu, Ke-Gang
He, Wen-bin
Yu, Hua
Hu, Yuan-jia
Expanding potential targets of herbal chemicals by node2vec based on herb–drug interactions
title Expanding potential targets of herbal chemicals by node2vec based on herb–drug interactions
title_full Expanding potential targets of herbal chemicals by node2vec based on herb–drug interactions
title_fullStr Expanding potential targets of herbal chemicals by node2vec based on herb–drug interactions
title_full_unstemmed Expanding potential targets of herbal chemicals by node2vec based on herb–drug interactions
title_short Expanding potential targets of herbal chemicals by node2vec based on herb–drug interactions
title_sort expanding potential targets of herbal chemicals by node2vec based on herb–drug interactions
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10233865/
https://www.ncbi.nlm.nih.gov/pubmed/37264453
http://dx.doi.org/10.1186/s13020-023-00763-3
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