Back to Search Start Over

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

Authors :
Xu G
Zhang S
Zheng L
Hu Z
Cheng L
Chen L
Li J
Shi Z
Source :
Purinergic signalling [Purinergic Signal] 2023 Mar; Vol. 19 (1), pp. 87-97. Date of Electronic Publication: 2021 Oct 22.
Publication Year :
2023

Abstract

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 <superscript>2+</superscript> 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.<br /> (© 2021. The Author(s).)

Details

Language :
English
ISSN :
1573-9546
Volume :
19
Issue :
1
Database :
MEDLINE
Journal :
Purinergic signalling
Publication Type :
Academic Journal
Accession number :
34677752
Full Text :
https://doi.org/10.1007/s11302-021-09808-4