The traditional Chinese medicines (TCMs) have been used to treat diseases over a long history, but it is still a great challenge to uncover the underlying mechanisms for their therapeutic effects due to the complexity of their ingredients. Based on a novel network pharmacology-based approach, we explored in this study the potential therapeutic targets of Liuwei Dihuang (LWDH) decoction in its neuroendocrine immunomodulation (NIM) function. We not only collected the known targets of the compounds in LWDH but also predicted the targets for these compounds using the balanced substructure-drug-target networkbased inference (bSDTNBI), which is a target prediction method based on network inferring developed by our laboratory. A "target-(pathway)-target" (TPT) network, in which targets of LWDH were connected by relevant pathways, was constructed and divided into several separate modules with strong internal connections. Then the target module that contributes the most to NIM function was determined through a contribution scoring algorithm. Finally, the targets with the highest contribution score to NIM-related diseases in this target module were recommended as potential therapeutic targets of LWDH.
Free fatty acid receptor 1 (FFAR1), a member of class A in G-protein-coupled receptors (GPCRs), is a promising antidiabetic target. The crystal structure of FFAR1 revealed that one agonist (MK-8666) binds to the extracellular vestibule of this receptor, while another (AP8) occupies the surface pocket between transmembrane (TM) helices TM4 and TM5. In this study, we performed 1 μs unbiased molecular dynamics (MD) simulation on each of five systems, to uncover why two ligands in completely different sites both serve as agonists and how they exert a positive synergistic effect together. They are two agonist-bound systems (FFAR1_MK-8666 and FFAR1_AP8), a ternary complex system FFAR1_MK-8666_AP8, an antagonist-bound system (FFAR1_15i), and an unliganded (apo) system, among which the antagonist 15i-bound and apo systems were used as controls. The results showed that Y913.37 played a pivotal role in the activation process of FFAR1. The agonist could disrupt the Y913.37-centered residue interaction network within protein, whereas the antagonist could stabilize the network. Furthermore, our simulations revealed that the hydrophobic layer amino acid residues next to the transmission switch (CWXP) formed a gate and could open only upon agonist activation, which might exert an important role in the formation of water pathway. These results would be helpful for elucidating the molecular activation mechanism of FFAR1 and provide insights into the design and discovery of novel allosteric agonists of FFAR1 for the treatment of type 2 diabetes mellitus (T2DM).
Estrogen‐related receptor alpha (ERRα) has attracted increasing concerns. ERRα, orphan nuclear receptor, plays important roles in energy metabolism. Therefore, small molecule agonists of ERRα could be a potential therapeutic strategy in the treatment of metabolic diseases such as diabetes. Recently, Wei et al. identified cholesterol as the endogenous agonist of ERRα. However, the detailed molecular mechanism of cholesterol bound with ERRα remains ambiguous. Thus, in this study molecular docking and molecular dynamics (MD) simulations were performed to characterize how cholesterol affects the behavior of ERRα. Based on the results, we found that a proven residue Phe232 and others including Leu228, Glu235, Arg276, and Phe399 were key residues to ligand binding. A hydrogen‐bonding interaction between cholesterol and Glu235 ensured the orientation of the ligand in the binding pocket, while hydrophobic interactions between cholesterol and the above‐mentioned residues promoted the stability of ERRα‐cholesterol complex. In the presence of the proliferator‐activated receptor γ coactivator 1α (PGC‐1α), the cholesterol‐ERRα interaction became more stable. Interestingly, we observed that cholesterol facilitated the binding of ERRα with its coactivator PGC‐1α via stabilizing the conformation of helix 12 and the interaction surface of ERRα/PGC‐1α. Overall, these findings would be valuable for the future rational design of novel ERRα agonists.
In recent years, drug-induced nephrotoxicity has been one of the main reasons for the failure of drug development. Early prediction of the nephrotoxicity for drug candidates is critical to the success of clinical trials. Therefore, it is very important to construct an effective model that can predict the potential nephrotoxicity of compounds. Machine learning methods have been widely used to predict the physico-
Free fatty acid receptor 1 (FFAR1) is a potential therapeutic target for the treatment of type 2 diabetes (T2D). It has been validated that agonists targeting FFAR1 can achieve the initial therapeutic endpoints of T2D, and the epimer agonists ( R , S ) AM-8596 can activate FFAR1 differently, with one acting as a partial agonist and the other as a full agonist. Up to now, the origin of the stereoselectivity of FFAR1 agonists remains elusive. In this work, we used molecular simulation methods to elucidate the mechanism of the stereoselectivity of the FFAR1 agonists ( R )-AM-8596 and ( S )-AM-8596. We found that the full agonist ( R )-AM-8596 disrupts the residue interaction network around the receptor binding pocket and promotes the opening of the binding site for the G-protein, thereby resulting in the full activation of FFAR1. In contrast, the partial agonist ( S )-AM-8596 forms stable electrostatic interactions with FFAR1, which stabilizes the residue network and hinders the conformational transition of the receptor. Our work thus clarifies the selectivity and underlying molecular activation mechanism of FFAR1 agonists.
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