Mechanisms of Action of a Herbal Formula Huangqi Guizhi Wuwu Tang for the Management of Post-Stroke Related Numbness and Weakness: A Computational Molecular Docking Study

Stroke-related numbness and weakness (SRNW) are resultant symptoms of post-stroke sufferers. Existing research has supported the use of Huangqi Guizhi Wuwu Tang (HGWT) particularly for SRNW; however, their mechanisms of action have not been fully elucidated. Therefore, this study aimed to investigate the mechanisms of action of HGWT components targeting SRNW-related proteins through a computational molecular docking approach. Target proteins associated with SRNW were identified through DrugBank database and Open Targets database. Chemical compounds from each herb of HGWT were identified from the Traditional Chinese Medicine Systems Pharmacology and Analysis Platform (TCMSP). Autodock Vina was utilized and the cut-off criterion applied for protein-ligand complexes was a binding affinity score of ≤ -9.5 kcal/mol; selected protein-ligand complexes were identified using 3D and 2D structural analyses. The protein targets PDE5A and ESR1 have highlighted interactions with compounds (BS040, DZ006, DZ058, DZ118, and HQ066) which are the key molecules in the management of SRNW. PDE5A have bioactivity with the amino acid residues (Val230, Asn252, Gln133 and Thr166) throughout PDE5A-cGMP-PKG pathways which involved reduction in myofilament responsiveness. ESR1 were predicted to be critical active with site residue (Leu346, Glu419 and Leu387) and its proteoglycans pathway involving CD44v3/CD44 that activates rho-associated protein kinase 1 (ROCK1) and ankyrin increasing vascular smooth muscle. In conclusion, HGWT may provide therapeutic benefits through strong interactions between herbal compounds and target proteins of PDE5A and ESR1. Further experimental studies are needed to unequivocally support this result which can be valuable to increase the quality of life of post-stroke patients. Keywords Herbal medicine, Complementary and alternative medicine, Natural product, Post-stroke, Computational analysis