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In silico identification of A1 agonists and A2a inhibitors in pain based on molecular docking strategies and dynamics simulations

Most recently, the adenosine is considered as one of the most promising targets for treating pain, with few side effects. It exists in the central nervous system, and plays a key role in nociceptive afferent pathway. It is reported that the A1 receptor (A1R) could inhibit Ca(2+) channels to reduce t...

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Autores principales: Xu, Guangya, Zhang, Shutao, Zheng, Lulu, Hu, Zhongjiao, Cheng, Lijia, Chen, Lvlin, Li, Jun, Shi, Zheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984648/
https://www.ncbi.nlm.nih.gov/pubmed/34677752
http://dx.doi.org/10.1007/s11302-021-09808-4
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author Xu, Guangya
Zhang, Shutao
Zheng, Lulu
Hu, Zhongjiao
Cheng, Lijia
Chen, Lvlin
Li, Jun
Shi, Zheng
author_facet Xu, Guangya
Zhang, Shutao
Zheng, Lulu
Hu, Zhongjiao
Cheng, Lijia
Chen, Lvlin
Li, Jun
Shi, Zheng
author_sort Xu, Guangya
collection PubMed
description Most recently, the adenosine is considered as one of the most promising targets for treating pain, with few side effects. It exists in the central nervous system, and plays a key role in nociceptive afferent pathway. It is reported that the A1 receptor (A1R) could inhibit Ca(2+) channels to reduce the pain like analgesic mechanism of morphine. And, A2a receptor (A2aR) was reported to enhance the accumulation of AMP (cAMP) and released peptides from sensory neurons, resulting in constitutive activation of pain. Much evidence showed that A1R and A2aR could be served as the interesting targets for the treatment of pain. Herein, virtual screening was utilized to identify the small molecule compounds towards A1R and A2aR, and top six molecules were considered as candidates via amber scores. The molecular dynamic (MD) simulations and molecular mechanics/generalized born surface area (MM/GBSA) were employed to further analyze the affinity and binding stability of the six molecules towards A1R and A2aR. Moreover, energy decomposition analysis showed significant residues in A1R and A2aR, including His1383, Phe1276, and Glu1277. It provided basics for discovery of novel agonists and antagonists. Finally, the agonists of A1R (ZINC19943625, ZINC13555217, and ZINC04698406) and inhibitors of A2aR (ZINC19370372, ZINC20176051, and ZINC57263068) were successfully recognized. Taken together, our discovered small molecules may serve as the promising candidate agents for future pain research.
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spelling pubmed-99846482023-03-05 In silico identification of A1 agonists and A2a inhibitors in pain based on molecular docking strategies and dynamics simulations Xu, Guangya Zhang, Shutao Zheng, Lulu Hu, Zhongjiao Cheng, Lijia Chen, Lvlin Li, Jun Shi, Zheng Purinergic Signal Original Article Most recently, the adenosine is considered as one of the most promising targets for treating pain, with few side effects. It exists in the central nervous system, and plays a key role in nociceptive afferent pathway. It is reported that the A1 receptor (A1R) could inhibit Ca(2+) channels to reduce the pain like analgesic mechanism of morphine. And, A2a receptor (A2aR) was reported to enhance the accumulation of AMP (cAMP) and released peptides from sensory neurons, resulting in constitutive activation of pain. Much evidence showed that A1R and A2aR could be served as the interesting targets for the treatment of pain. Herein, virtual screening was utilized to identify the small molecule compounds towards A1R and A2aR, and top six molecules were considered as candidates via amber scores. The molecular dynamic (MD) simulations and molecular mechanics/generalized born surface area (MM/GBSA) were employed to further analyze the affinity and binding stability of the six molecules towards A1R and A2aR. Moreover, energy decomposition analysis showed significant residues in A1R and A2aR, including His1383, Phe1276, and Glu1277. It provided basics for discovery of novel agonists and antagonists. Finally, the agonists of A1R (ZINC19943625, ZINC13555217, and ZINC04698406) and inhibitors of A2aR (ZINC19370372, ZINC20176051, and ZINC57263068) were successfully recognized. Taken together, our discovered small molecules may serve as the promising candidate agents for future pain research. Springer Netherlands 2021-10-22 2023-03 /pmc/articles/PMC9984648/ /pubmed/34677752 http://dx.doi.org/10.1007/s11302-021-09808-4 Text en © The Author(s) 2021 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/) .
spellingShingle Original Article
Xu, Guangya
Zhang, Shutao
Zheng, Lulu
Hu, Zhongjiao
Cheng, Lijia
Chen, Lvlin
Li, Jun
Shi, Zheng
In silico identification of A1 agonists and A2a inhibitors in pain based on molecular docking strategies and dynamics simulations
title In silico identification of A1 agonists and A2a inhibitors in pain based on molecular docking strategies and dynamics simulations
title_full In silico identification of A1 agonists and A2a inhibitors in pain based on molecular docking strategies and dynamics simulations
title_fullStr In silico identification of A1 agonists and A2a inhibitors in pain based on molecular docking strategies and dynamics simulations
title_full_unstemmed In silico identification of A1 agonists and A2a inhibitors in pain based on molecular docking strategies and dynamics simulations
title_short In silico identification of A1 agonists and A2a inhibitors in pain based on molecular docking strategies and dynamics simulations
title_sort in silico identification of a1 agonists and a2a inhibitors in pain based on molecular docking strategies and dynamics simulations
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984648/
https://www.ncbi.nlm.nih.gov/pubmed/34677752
http://dx.doi.org/10.1007/s11302-021-09808-4
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