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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2021
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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. |
format | Online Article Text |
id | pubmed-9984648 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
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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