Literature DB >> 27810775

Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification-Salvinorin A as a case study.

Xiaomeng Xu1, Shifan Ma1, Zhiwei Feng1, Guanxing Hu1, Lirong Wang2, Xiang-Qun Xie3.   

Abstract

Drug abuse is a serious problem worldwide. Recently, hallucinogens have been reported as a potential preventative and auxiliary therapy for substance abuse. However, the use of hallucinogens as a drug abuse treatment has potential risks, as the fundamental mechanisms of hallucinogens are not clear. So far, no scientific database is available for the mechanism research of hallucinogens. We constructed a hallucinogen-specific chemogenomics database by collecting chemicals, protein targets and pathways closely related to hallucinogens. This information, together with our established computational chemogenomics tools, such as TargetHunter and HTDocking, provided a one-step solution for the mechanism study of hallucinogens. We chose salvinorin A, a potent hallucinogen extracted from the plant Salvia divinorum, as an example to demonstrate the usability of our platform. With the help of HTDocking program, we predicted four novel targets for salvinorin A, including muscarinic acetylcholine receptor 2, cannabinoid receptor 1, cannabinoid receptor 2 and dopamine receptor 2. We looked into the interactions between salvinorin A and the predicted targets. The binding modes, pose and docking scores indicate that salvinorin A may interact with some of these predicted targets. Overall, our database enriched the information of systems pharmacological analysis, target identification and drug discovery for hallucinogens. Copyright Â
© 2016. Published by Elsevier Inc.

Entities:  

Keywords:  Chemogenomics database; Drug abuse; Hallucinogen; Salvinorin A; Systems pharmacology; Target identification

Mesh:

Substances:

Year:  2016        PMID: 27810775      PMCID: PMC5327504          DOI: 10.1016/j.jmgm.2016.08.001

Source DB:  PubMed          Journal:  J Mol Graph Model        ISSN: 1093-3263            Impact factor:   2.518


  73 in total

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  11 in total

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