A combined ligand- and structure-based virtual screening protocol identifies submicromolar PPARγ partial agonists

Structural insights and molecular profiling of a large set of diverse compounds targeting PPARγ: from comprehensive cheminformatics approach to tool development

This study integrates a robust cheminformatics approach (including chemical space exploration, Bayesian model-based fingerprint analysis, and cluster-driven molecular profiling) to reveal the key structural features influencing peroxisome proliferator activated receptor-gamma (PPARγ) modulatory activity. The Bayesian classification model effectively differentiates between the beneficial and adverse structural characteristics of PPARγ modulators. Structural motifs such as substituted benzylamine, phenoxyphenyl groups, 5-phenyl-1,3-thiazolidine scaffolds, and indole rings have been identified as positive contributors, enhancing PPARγ activity. Conversely, features like substituted tertiary amines and sulphonamide groups were found to have detrimental effects, suggesting that these should be deprioritized in the design of future PPARγ modulators. Additionally, molecular clustering provided a means to categorize structurally similar compounds, facilitating scaffold analysis, diversity calculation, and lead optimization for PPARγ modulators. To extend these findings to the broader scientific community, we have developed an open-access online tool, 'Fasda_v1.0', (https://fasdav1web.streamlit.app/) designed for cluster-driven molecular profiling of any dataset, enabling further exploration and application of these methods. This study offers valuable guidance for designing and developing novel therapeutics targeting PPARγ, thereby contributing to advancements in treating type 2 diabetes mellitus and related diseases.

Discovery by Virtual Screening of an Inhibitor of CDK5-Mediated PPARγ Phosphorylation

Thiazolidinedione PPARγ agonists such as rosiglitazone and pioglitazone are effective antidiabetic drugs, but side effects have limited their use. It has been posited that their positive antidiabetic effects are mainly mediated by the inhibition of the CDK5-mediated Ser273 phosphorylation of PPARγ, whereas the side effects are linked to classical PPARγ agonism. Thus compounds that inhibit PPARγ Ser273 phosphorylation but lack classical PPARγ agonism have been sought as safer antidiabetic therapies. Herein we report the discovery by virtual screening of 10, which is a potent PPARγ binder and in vitro inhibitor of the CDK5-mediated phosphorylation of PPARγ Ser273 and displays negligible PPARγ agonism in a reporter gene assay. The pharmacokinetic properties of 10 are compatible with oral dosing, enabling preclinical in vivo testing, and a 7 day treatment demonstrated an improvement in insulin sensitivity in the ob/ob diabetic mouse model.

Computational identification of potential chemoprophylactic agents according to dynamic behavior of peroxisome proliferator-activated receptor gamma

Peroxisome proliferator-activated receptor gamma (PPARγ) is an attractive target for chemoprevention of lung carcinoma, however its highly dynamic nature has plagued drug development for decades, with difficulties in receptor modeling for structure-based design. In this work, an integrated receptor-based virtual screening (VS) strategy was applied to identify PPARγ agonists as chemoprophylactic agents by using extensive docking and conformational sampling methods. Our results showed that the conformational plasticity of PPARγ, especially the H2 & S245 loop, H2' & Ω loop and AF-2 surface, is markedly affected by binding of full/partial agonists. To fully take the dynamic behavior of PPARγ into account, the VS approach effectively sorts out five commercial agents with reported antineoplastic properties. Among them, ZINC03775146 (gusperimus) and ZINC14087743 (miltefosine) might be novel PPARγ agonists with the potential for chemoprophylaxis, that simultaneously take part in a flexible switch of the AF-2 surface and state change of the Ω loop. Furthermore, the dynamic structural coupling between the H2 & S245 and H2' & Ω loops offers enticing hope for PPARγ-targeted therapeutics, by blocking kinase accessibility to PPARγ. These results might aid the development of chemopreventive drugs, and the integrated VS strategy could be conducive to drug design for highly flexible biomacromolecules.

To Probe Full and Partial Activation of Human Peroxisome Proliferator-Activated Receptors by Pan-Agonist Chiglitazar Using Molecular Dynamics Simulations

Chiglitazar is a promising new-generation insulin sensitizer with low reverse effects for the treatment of type II diabetes mellitus (T2DM) and has shown activity as a nonselective pan-agonist to the human peroxisome proliferator-activated receptors (PPARs) (i.e., full activation of PPARγ and a partial activation of PPARα and PPARβ/δ). Yet, it has no high-resolution complex structure with PPARs and its detailed interactions and activation mechanism remain unclear. In this study, we docked chiglitazar into three experimentally resolved crystal structures of hPPAR subtypes, PPARα, PPARβ/δ, and PPARγ, followed by 3 μs molecular dynamics simulations for each system. Our MM-GBSA binding energy calculation revealed that chiglitazar most favorably bound to hPPARγ (-144.6 kcal/mol), followed by hPPARα (-138.0 kcal/mol) and hPPARβ (-135.9 kcal/mol), and the order is consistent with the experimental data. Through the decomposition of the MM-GBSA binding energy by residue and the use of two-dimensional interaction diagrams, key residues involved in the binding of chiglitazar were identified and characterized for each complex system. Additionally, our detailed dynamics analyses support that the conformation and dynamics of helix 12 play a critical role in determining the activities of the different types of ligands (e.g., full agonist vs. partial agonist). Rather than being bent fully in the direction of the agonist versus antagonist conformation, a partial agonist can adopt a more linear conformation and have a lower degree of flexibility. Our finding may aid in further development of this new generation of medication.

Therapeutic potential of PPARγ natural agonists in liver diseases

Peroxisome proliferator‐activated receptor gamma (PPARγ) is a vital subtype of the PPAR family. The biological functions are complex and diverse. PPARγ plays a significant role in protecting the liver from inflammation, oxidation, fibrosis, fatty liver and tumours. Natural products are a promising pool for drug discovery, and enormous research effort has been invested in exploring the PPARγ‐activating potential of natural products. In this manuscript, we will review the research progress of PPARγ agonists from natural products in recent years and probe into the application potential and prospects of PPARγ natural agonists in the therapy of various liver diseases, including inflammation, hepatic fibrosis, non‐alcoholic fatty liver and liver cancer.

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.

Molecular determinants of PPARγ partial agonism and related in silico/in vivo studies of natural saponins as potential type 2 diabetes modulators

The metabolic syndrome, which includes hypertension, type 2 diabetes (T2D) and obesity, has reached an epidemic-like scale. Saponins and sapogenins are considered as valuable natural products for ameliorating this pathology, possibly through the nuclear receptor PPARγ activation. The aims of this study were: to look for in vivo antidiabetic effects of a purified saponins' mixture (PSM) from Astragalus corniculatus Bieb; to reveal by in silico methods the molecular determinants of PPARγ partial agonism, and to investigate the potential PPARγ participation in the PSM effects. In the in vivo experiments spontaneously hypertensive rats (SHRs) with induced T2D were treated with PSM or pioglitazone as a referent PPARγ full agonist, and pathology-relevant biochemical markers were analysed. The results provided details on the PSM modulation of the glucose homeostasis and its potential mechanism. The in silico studies focused on analysis of the protein-ligand interactions in crystal structures of human PPARγ-partial agonist complexes, pharmacophore modelling and molecular docking. They outlined key pharmacophoric features, typical for the PPARγ partial agonists, which were used for pharmacophore-based docking of the main PSM sapogenin. The in silico studies, strongly suggest possible involvement of PPARγ-mediated mechanisms in the in vivo antidiabetic and antioxidant effects of PSM from A. corniculatus.

Design, Synthesis, and in vitro and in vivo Evaluations of (Z)-3,4,5-Trimethoxystyrylbenzenesulfonamides/sulfonates as Highly Potent Tubulin Polymerization Inhibitors

Newer therapeutics can be developed in drug discovery by adopting the strategy of scaffold hopping of the privileged scaffolds from known bioactive compounds. This strategy has been widely employed in drug-discovery processes. Structure-based docking studies illustrate the basic underlying concepts and reveal that interactions of the sulfonamide group and hydrophobic interactions are crucial. On the basis of this strategy, over 60 synthetic analogues were synthesized and evaluated for their cytotoxicity against the NCI panel of 60 human cancer cell lines; the majority of these compounds exhibited promising cytotoxicity with GI₅₀ values ranging between 18 and 50 nm. Among these compounds, (Z)-N-[2,3-dimethoxy-5-(3,4,5-trimethoxystyryl)phenyl]-4-methoxybenzenesulfonamide (7 a) and (Z)-N-[2-hydroxy-3-methoxy-6-(3,4,5-trimethoxystyryl)phenyl]-4-methoxybenzenesulfonamide (9 a) were found to be potent. Similar results were obtained against three human cancer cell lines with IC₅₀ values ranging between 0.04 and 3.0 μm. Studies aimed at elucidating the mechanism of action of these new analogues revealed that they inhibited the in vitro polymerization of tubulin and disorganized the assembly of microtubules in HeLa and MCF-7cancer cells. Lead compounds 7 a and 9 a displayed notable in vivo antitumor activity in a HeLa tumor xenograft model. Our studies have resulted in the identification of a scaffold that can target tubulin polymerization, which should have significant potential toward the development of new antitumor drugs.

Structural basis for PPAR partial or full activation revealed by a novel ligand binding mode

The peroxisome proliferator-activated receptors (PPARs) are nuclear receptors involved in the regulation of the metabolic homeostasis and therefore represent valuable therapeutic targets for the treatment of metabolic diseases. The development of more balanced drugs interacting with PPARs, devoid of the side-effects showed by the currently marketed PPARγ full agonists, is considered the major challenge for the pharmaceutical companies. Here we present a structure-based virtual screening approach that let us identify a novel PPAR pan-agonist with a very attractive activity profile and its crystal structure in the complex with PPARα and PPARγ, respectively. In PPARα this ligand occupies a new pocket whose filling is allowed by the ligand-induced switching of the F273 side chain from a closed to an open conformation. The comparison between this pocket and the corresponding cavity in PPARγ provides a rationale for the different activation of the ligand towards PPARα and PPARγ, suggesting a novel basis for ligand design.

PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation

Virtual screening has become an indispensable procedure in drug discovery. Virtual screening methods can be classified into two categories: ligand-based and structure-based. While the former have advantages, including being quick to compute, in general they are relatively weak at discovering novel active compounds because they use known actives as references. On the other hand, structure-based methods have higher potential to find novel compounds because they directly predict the binding affinity of a ligand in a target binding pocket, albeit with substantially lower speed than ligand-based methods. Here we report a novel structure-based virtual screening method, PL-PatchSurfer2. In PL-PatchSurfer2, protein and ligand surfaces are represented by a set of overlapping local patches, each of which is represented by three-dimensional Zernike descriptors (3DZDs). By means of 3DZDs, the shapes and physicochemical complementarities of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared with the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by the addition of two more features, atom-based hydrophobicity and hydrogen-bond acceptors and donors. Benchmark studies showed that PL-PatchSurfer2 performed better than or comparable to popular existing methods. Particularly, PL-PatchSurfer2 significantly outperformed existing methods when apo-form or template-based protein models were used for queries. The computational time of PL-PatchSurfer2 is about 20 times shorter than those of conventional structure-based methods. The PL-PatchSurfer2 program is available at http://www.kiharalab.org/plps2/ .

Identification of a New Type of Covalent PPARγ Agonist using a Ligand-Linking Strategy

Peroxisome proliferator-activated receptor γ (PPARγ) is a ligand-activated transcription factor that plays an important role in adipogenesis and glucose metabolism. The ligand-binding pocket (LBP) of PPARγ has a large Y-shaped cavity with multiple subpockets where multiple ligands can simultaneously bind and cooperatively activate PPARγ. Focusing on this unique property of the PPARγ LBP, we describe a novel two-step cell-based strategy to develop PPARγ ligands. First, a combination of ligands that cooperatively activates PPARγ was identified using a luciferase reporter assay. Second, hybrid ligands were designed and synthesized. For proof of concept, we focused on covalent agonists, which activate PPARγ through a unique activation mechanism regulated by a covalent linkage with the Cys285 residue in the PPARγ LBP. Despite their biological significance and pharmacological potential, few covalent PPARγ agonists are known except for endogenous fatty acid metabolites. With our strategy, we determined that plant-derived cinnamic acid derivatives cooperatively activated PPARγ by combining with GW9662, an irreversible antagonist. GW9662 covalently reacts with the Cys285 residue. A docking study predicted that a cinnamic acid derivative can bind to the open cavity in GW9662-bound PPARγ LBP. On the basis of the putative binding mode, structures of both ligands were linked successfully to create a potent PPARγ agonist, which enhanced the transactivation potential of PPARγ at submicromolar levels through covalent modification of Cys285. Our approach could lead to the discovery of novel high-potency PPARγ agonists.

Post-HTS case report and structural alert: Promiscuous 4-aroyl-1,5-disubstituted-3-hydroxy-2H-pyrrol-2-one actives verified by ALARM NMR

Despite its wide use, not every high-throughput screen (HTS) yields chemical matter suitable for drug development campaigns, and seldom are 'go/no-go' decisions in drug discovery described in detail. This case report describes the follow-up of a 4-aroyl-1,5-disubstituted-3-hydroxy-2H-pyrrol-2-one active from a cell-free HTS to identify small-molecule inhibitors of Rtt109-catalyzed histone acetylation. While this compound and structural analogs inhibited Rtt109-catalyzed histone acetylation in vitro, further work on this series was halted after several risk mitigation strategies were performed. Compounds with this chemotype had a poor structure-activity relationship, exhibited poor selectivity among other histone acetyltransferases, and tested positive in a β-lactamase counter-screen for chemical aggregates. Furthermore, ALARM NMR demonstrated compounds with this chemotype grossly perturbed the conformation of the La protein. In retrospect, this chemotype was flagged as a 'frequent hitter' in an analysis of a large corporate screening deck, yet similar compounds have been published as screening actives or chemical probes versus unrelated biological targets. This report-including the decision-making process behind the 'no-go' decision-should be informative for groups engaged in post-HTS triage and highlight the importance of considering physicochemical properties in early drug discovery.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

Probing the S1' site for the identification of non-zinc-binding MMP-2 inhibitors

Matrix metalloproteinases (MMPs) are zinc-dependent enzymes involved in several pathological states. Among them, MMP-2 is a relevant therapeutic target because of its role in cancer development and progression. Many MMP inhibitors (MMPIs) have been discovered over the last 30 years, and the majority of them contain a functional group that binds the zinc ion (zinc-binding group; ZBG). Unfortunately, no MMPIs have reached the market yet, owing to toxic effects due to unselective interactions of the ZBG. The new generation of MMPIs that do not bind the zinc ion could overcome problems of selectivity and toxicity, but have so far been developed only for MMP-8, -12, and -13. In this work, a virtual screening protocol was established by combining ligand- and structure-based methods to identify non-zinc-binding MMP-2 inhibitors using a new-generation MMP-8 inhibitor as a probe to find unexplored interactions in the MMP-2 S1' site. The screening allowed the identification of micromolar MMP-2 inhibitors that putatively avoid binding the zinc ion, as demonstrated by docking calculations. The LIA model, built to correlate predicted and experimental binding energies of the identified non-zinc-binding MMP-2 hits, underpins the reliability of the predicted docking poses.

Cognitive enhancement with rosiglitazone links the hippocampal PPARγ and ERK MAPK signaling pathways

We previously reported that the peroxisome proliferator-activated receptor γ (PPARγ) agonist rosiglitazone (RSG) improved hippocampus-dependent cognition in the Alzheimer's disease (AD) mouse model, Tg2576. RSG had no effect on wild-type littermate cognitive performance. Since extracellular signal-regulated protein kinase mitogen-activated protein kinase (ERK MAPK) is required for many forms of learning and memory that are affected in AD, and since both PPARγ and ERK MAPK are key mediators of insulin signaling, the current study tested the hypothesis that RSG-mediated cognitive improvement induces a hippocampal PPARγ pattern of gene and protein expression that converges with the ERK MAPK signaling axis in Tg2576 AD mice. In the hippocampal PPARγ transcriptome, we found significant overlap between peroxisome proliferator response element-containing PPARγ target genes and ERK-regulated, cAMP response element-containing target genes. Within the Tg2576 dentate gyrus proteome, RSG induced proteins with structural, energy, biosynthesis and plasticity functions. Several of these proteins are known to be important for cognitive function and are also regulated by ERK MAPK. In addition, we found the RSG-mediated augmentation of PPARγ and ERK2 activity during Tg2576 cognitive enhancement was reversed when hippocampal PPARγ was pharmacologically antagonized, revealing a coordinate relationship between PPARγ transcriptional competency and phosphorylated ERK that is reciprocally affected in response to chronic activation, compared with acute inhibition, of PPARγ. We conclude that the hippocampal transcriptome and proteome induced by cognitive enhancement with RSG harnesses a dysregulated ERK MAPK signal transduction pathway to overcome AD-like cognitive deficits in Tg2576 mice. Thus, PPARγ represents a signaling system that is not crucial for normal cognition yet can intercede to restore neural networks compromised by AD.

Identification of PPARgamma partial agonists of natural origin (I): development of a virtual screening procedure and in vitro validation

Background

Although there are successful examples of the discovery of new PPARγ agonists, it has recently been of great interest to identify new PPARγ partial agonists that do not present the adverse side effects caused by PPARγ full agonists. Consequently, the goal of this work was to design, apply and validate a virtual screening workflow to identify novel PPARγ partial agonists among natural products.

Methodology/Principal Findings

We have developed a virtual screening procedure based on structure-based pharmacophore construction, protein-ligand docking and electrostatic/shape similarity to discover novel scaffolds of PPARγ partial agonists. From an initial set of 89,165 natural products and natural product derivatives, 135 compounds were identified as potential PPARγ partial agonists with good ADME properties. Ten compounds that represent ten new chemical scaffolds for PPARγ partial agonists were selected for in vitro biological testing, but two of them were not assayed due to solubility problems. Five out of the remaining eight compounds were confirmed as PPARγ partial agonists: they bind to PPARγ, do not or only moderately stimulate the transactivation activity of PPARγ, do not induce adipogenesis of preadipocyte cells and stimulate the insulin-induced glucose uptake of adipocytes.

Conclusions/Significance

We have demonstrated that our virtual screening protocol was successful in identifying novel scaffolds for PPARγ partial agonists.

Analysis of structure-based virtual screening studies and characterization of identified active compounds

Structure-based virtual screening makes explicit or implicit use of 3D target structure information to detect novel active compounds. Results of nearly 300 currently available original applications have been analyzed to characterize the state-of-the-art in this field. Compound selection from docking calculations is much influenced by subjective criteria. Although submicromolar compounds are identified, the majority of docking hits are only weakly potent. However, only a small percentage of docking hits can be reproduced by ligand-based methods. When docking calculations identify potent hits, they often originate from specialized compound sources (e.g., pharmaceutical compound decks or target-focused libraries) and also display a notable bias towards kinase targets. Structure-based virtual screening is the dominant approach to computational hit identification. Docking calculations frequently identify active compounds. Limited accuracy of compound scoring and ranking currently presents a major caveat of the approach that is often compensated for by chemical intuition and knowledge.

Integrated virtual screening for the identification of novel and selective peroxisome proliferator-activated receptor (PPAR) scaffolds

We describe a fully customizable and integrated target-specific "tiered" virtual screening approach tailored to identifying and characterizing novel peroxisome proliferator activated receptor γ (PPARγ) scaffolds. Built on structure- and ligand-based computational techniques, a consensus protocol was developed for use in the virtual screening of chemical databases, focused toward retrieval of novel bioactive chemical scaffolds for PPARγ. Consequent from application, three novel PPAR scaffolds displaying distinct chemotypes have been identified, namely, 5-(4-(benzyloxy)-3-chlorobenzylidene)dihydro-2-thioxopyrimidine-4,6(1H,5H)-dione (MDG 548), 3-((4-bromophenoxy)methyl)-N-(4-nitro-1H-pyrazol-1-yl)benzamide (MDG 559), and ethyl 2-[3-hydroxy-5-(5-methyl-2-furyl)-2-oxo-4-(2-thienylcarbonyl)-2,5-dihydro-1H-pyrrol-1-yl]-4-methyl-1,3-thiazole-5-carboxylate (MDG 582). Fluorescence polarization(FP) and time resolved fluorescence resonance energy transfer (TR-FRET) show that these compounds display high affinity competitive binding to the PPARγ-LBD (EC(50) of 215 nM to 5.45 μM). Consequent characterization by a TR-FRET activation reporter assay demonstrated agonism of PPARγ by all three compounds (EC(50) of 467-594 nM). Additionally, differential PPAR isotype specificity was demonstrated through assay against PPARα and PPARδ subtypes. This work showcases the ability of target specific "tiered screen" protocols to successfully identify novel scaffolds of individual receptor subtypes with greater efficacy than isolated screening methods.

IVSPlat 1.0: an integrated virtual screening platform with a molecular graphical interface

BACKGROUND

The virtual screening (VS) of lead compounds using molecular docking and pharmacophore detection is now an important tool in drug discovery. VS tasks typically require a combination of several software tools and a molecular graphics system. Thus, the integration of all the requisite tools in a single operating environment could reduce the complexity of running VS experiments. However, only a few freely available integrated software platforms have been developed.

RESULTS

A free open-source platform, IVSPlat 1.0, was developed in this study for the management and automation of VS tasks. We integrated several VS-related programs into a molecular graphics system to provide a comprehensive platform for the solution of VS tasks based on molecular docking, pharmacophore detection, and a combination of both methods. This tool can be used to visualize intermediate and final results of the VS execution, while also providing a clustering tool for the analysis of VS results. A case study was conducted to demonstrate the applicability of this platform.

CONCLUSIONS

IVSPlat 1.0 provides a plug-in-based solution for the management, automation, and visualization of VS tasks. IVSPlat 1.0 is an open framework that allows the integration of extra software to extend its functionality and modified versions can be freely distributed. The open source code and documentation are available at http://kyc.nenu.edu.cn/IVSPlat/.

A nuclear-receptor-dependent phosphatidylcholine pathway with antidiabetic effects

Nuclear hormone receptors regulate diverse metabolic pathways and the orphan nuclear receptor LRH-1 (also known as NR5A2) regulates bile acid biosynthesis. Structural studies have identified phospholipids as potential LRH-1 ligands, but their functional relevance is unclear. Here we show that an unusual phosphatidylcholine species with two saturated 12 carbon fatty acid acyl side chains (dilauroyl phosphatidylcholine (DLPC)) is an LRH-1 agonist ligand in vitro. DLPC treatment induces bile acid biosynthetic enzymes in mouse liver, increases bile acid levels, and lowers hepatic triglycerides and serum glucose. DLPC treatment also decreases hepatic steatosis and improves glucose homeostasis in two mouse models of insulin resistance. Both the antidiabetic and lipotropic effects are lost in liver-specific Lrh-1 knockouts. These findings identify an LRH-1 dependent phosphatidylcholine signalling pathway that regulates bile acid metabolism and glucose homeostasis.

The role of computational methods in the identification of bioactive compounds

Computational methods play an ever increasing role in lead finding. A vast repertoire of molecular design and virtual screening methods emerged in the past two decades and are today routinely used. There is increasing awareness that there is no single best computational protocol and correspondingly there is a shift recommending the combination of complementary methods. A promising trend for the application of computational methods in lead finding is to take advantage of the vast amounts of HTS (High Throughput Screening) data to allow lead assessment by detailed systems-based data analysis, especially for phenotypic screens where the identification of compound-target pairs is the primary goal. Herein, we review trends and provide examples of successful applications of computational methods in lead finding.