In Silico Workflow for the Discovery of Natural Products Activating the G Protein-Coupled Bile Acid Receptor 1

Identification and Characterization of a Leoligin-Inspired Synthetic Lignan as a TGR5 Agonist

The G-protein coupled bile acid receptor 1 (GPBAR1 or TGR5) is the major cell membrane receptor for bile acids regulating metabolic and immunological functions. Its pharmacological modulation has been shown to alleviate inflammatory diseases, such as type 2 diabetes and atherosclerosis. The naturally occurring lignan leoligin and structural analogues have shown anti-inflammatory effects in vitro. However, the underlying molecular targets are still unknown. In this study, we identify the natural product-inspired synthetic structural analogue of leoligin, LT-188A (1), as a novel nonsteroidal TGR5 agonist. LT-188A (1) induced cyclic adenosine monophosphate (cAMP) accumulation and cAMP response element (CRE)-dependent luciferase activity in a concentration- and TGR5-dependent manner. Consistently, LT-188A (1) inhibited activation of the pro-inflammatory transcription factor nuclear factor κB (NFκB) only in TGR5 expressing cells. In macrophages, LT-188A (1) reduced the expression levels of pro-inflammatory cytokines and the production of nitric oxide (NO) as determined by qPCR and the Griess assay, respectively. We showed that LT-188A (1) decreased the levels of production of these inflammatory mediators in macrophages. In conclusion, we demonstrate that LT-188A (1) is a novel natural product-inspired TGR5 agonist with promising anti-inflammatory in vitro bioactivity in relevant cellular assays representing a promising tool compound with potential for further development.

TGR5 Activation by Dietary Bioactives and Related Improvement in Mitochondrial Function for Alleviating Diabetes and Associated Complications

Takeda G protein-coupled receptor 5 (TGR5), also known as G protein-coupled bile acid receptor 1 (GPBAR1), is a cell surface receptor involved in key physiological processes, including glucose homeostasis and energy metabolism. Recent research has focused on the role of TGR5 activation in preventing or treating diabetes while also highlighting its potential impact on the progression of diabetic complications. Functional foods and edible plants have emerged as valuable sources of natural compounds that can activate TGR5, offering potential therapeutic benefits for diabetes management. Despite growing interest, studies on the activation of TGR5 by dietary bioactive compounds remain scattered. This Review aims to provide a comprehensive analysis of how dietary bioactives act as potential agents for TGR5 activation in managing diabetes and its complications. It explores the mechanisms of TGR5 activation through both direct agonistic effects and indirect pathways via modulation of the gut microbiota and bile acid metabolism.

Mechanism of action of the bile acid receptor TGR5 in obesity

G protein-coupled receptors (GPCRs) are a large family of membrane protein receptors, and Takeda G protein-coupled receptor 5 (TGR5) is a member of this family. As a membrane receptor, TGR5 is widely distributed in different parts of the human body and plays a vital role in regulating metabolism, including the processes of energy consumption, weight loss and blood glucose homeostasis. Recent studies have shown that TGR5 plays an important role in glucose and lipid metabolism disorders such as fatty liver, obesity and diabetes. With the global obesity situation becoming more and more serious, a comprehensive explanation of the mechanism of TGR5 and filling the gaps in knowledge concerning clinical ligand drugs are urgently needed. In this review, we mainly explain the anti-obesity mechanism of TGR5 to promote the further study of this target, and show the electron microscope structure of TGR5 and review recent studies on TGR5 ligands to illustrate the specific binding between TGR5 receptor binding sites and ligands, which can effectively provide new ideas for ligand research and promote drug research.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

Mind the Gap-Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand-receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

Therapeutic Opportunities of GPBAR1 in Cholestatic Diseases

GPBAR1, a transmembrane G protein-coupled receptor for bile acids, is widely expressed in multiple tissues in humans and rodents. In recent years, GPBAR1 has been thought to play an important role in bile homeostasis, metabolism and inflammation. This review specifically focuses on the function of GPBAR1 in cholestatic liver disease and summarizes the various pathways through which GPBAR1 acts in cholestatic models. GPBAR1 mainly regulates cholestasis in a holistic system of liver-gallbladder-gut formation. In the state of cholestasis, the activation of GPBAR1 could regulate liver inflammation, induce cholangiocyte regeneration to maintain the integrity of the biliary tree, control the hydrophobicity of the bile acid pool and promote the secretion of bile HCO₃⁻. All these functions of GPBAR1 might be clear ways to protect against cholestatic diseases and liver injury. However, the characteristic of GPBAR1-mediated proliferation increases the risk of proliferation of cholangiocarcinoma in malignant transformed cholangiocytes. This dichotomous function of GPBAR1 limits its use in cholestasis. During disease treatment, simultaneous activation of GPBAR1 and FXR receptors often results in improved outcomes, and this strategy may become a crucial direction in the development of bile acid-activated receptors in the future.

Cheminformatics in Natural Product-based Drug Discovery

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

Cheminformatics to Characterize Pharmacologically Active Natural Products

Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. This review discusses a broad range of chemoinformatics applications to support natural product-based drug discovery. We emphasize profiling natural product data sets in terms of diversity; complexity; acid/base; absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties; and fragment analysis. Novel techniques for the visual representation of the chemical space are also discussed.

Effects of Farnesiferol B on Ischemia-Reperfusion-Induced Renal Damage, Inflammation, and NF-κB Signaling

Background: G-protein-coupled bile acid receptor (TGR5), a membrane bile acid receptor, regulates macrophage reactivity, and attenuates inflammation in different disease models. However, the regulatory effects of TGR5 in ischemia/reperfusion (I/R)-induced kidney injury and inflammation have not yet been extensively studied. Therefore, we hypothesize that Farnesiferol B, a natural TGR5 agonist, could alleviate renal I/R injury by reducing inflammation and macrophage migration through activating TGR5. Methods: Mice were treated with Farnesiferol B before I/R or sham procedures. Renal function, pathological analysis, and inflammatory mediators were examined. In vitro, the regulatory effects of Farnesiferol B on the Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway in macrophages were investigated. Results: After I/R, Farnesiferol B-treated mice displayed better renal function and less tubular damage. Farnesiferol B reduced renal oxidative stress and inflammation significantly. In vitro, Farnesiferol B treatment alleviated lipopolysaccharide (LPS)-induced macrophage migration and activation, as well as LPS-induced NF-κB activation through TGR5. Conclusions: Farnesiferol B could protect kidney function from I/R-induced damage by attenuating inflammation though activating TGR5 in macrophages. Farnesiferol B might be a potent TGR5 ligand for the treatment of I/R-induced renal inflammation.

Current computational methods for predicting protein interactions of natural products

Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.

Chemistry and Pharmacology of GPBAR1 and FXR Selective Agonists, Dual Agonists, and Antagonists

In the recent years, bile acid receptors FXR and GPBAR1 have attracted the interest of scientific community and companies, as they proved promising targets for the treatment of several diseases, ranging from liver cholestatic disorders to metabolic syndrome, inflammatory states, nonalcoholic steatohepatitis (NASH), and diabetes.Consequently, the development of dual FXR/GPBAR1 agonists, as well as selective targeting of one of these receptors, is considered a hopeful possibility in the treatment of these disorders. Because endogenous bile acids and steroidal ligands, which cover the same chemical space of bile acids, often target both receptor families, speculation on nonsteroidal ligands represents a promising and innovative strategy to selectively target GPBAR1 or FXR.In this review, we summarize the most recent acquisition on natural, semisynthetic, and synthetic steroidal and nonsteroidal ligands, able to interact with FXR and GPBAR1.

A Strength-Weaknesses-Opportunities-Threats (SWOT) Analysis of Cheminformatics in Natural Product Research

Cheminformatics-based techniques, such as molecular modeling, docking, virtual screening, and machine learning, are well accepted for their usefulness in drug discovery and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings. Combining information obtained from different sources, i.e., virtual predictions, traditional medicine, structural, biochemical, and biological data, and handling big data effectively will open up new possibilities, but also challenges in the future. Strategies and examples will be presented on how to integrate cheminformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines toward streamlining experimental efforts. While considering their limits and pitfalls and by exploiting their potential, computer-aided strategies should successfully guide future studies and thereby augment our knowledge of bioactive natural lead structures.