The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation.

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q(2)cv=0.610, Nopt=7, SEPcv=0.505, r(2)pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development

Computational Studies and Design of PPARγ and GLUT1 Inhibitors

The peroxisome proliferator-activated receptor gamma (PPARγ) is a ligand-dependent transcription factor of the nuclear receptor superfamily that controls the expression of a variety of genes involved in fatty acid metabolism, adipogenesis, and insulin sensitivity. PPARγ is a target for insulin-sensitizing drugs, and it plays a significant function in prostate cancer. PPARγ antagonists have anti-proliferative effects in a broad range of hematopoietic and epithelial cell lines. The ligand binding domain (LBD) of PPARγ is large and has orthosteric and allosteric binding sites. Several PPARγ-ligand co-crystal structures show two bound molecules, one to the orthosteric pocket and a second to the allosteric site. We ran docking studies against the orthosteric and allosteric binding sites to determine the most favorable binding site for PPARγ antagonists. We found that Glide docking performed well in predicting PPARγ antagonist binding affinities, and that the allosteric site of PPARγ was the most favorable binding site for antagonists. We also investigated PPARγ ligand-protein interactions to better define a structural basis for the binding selectivity of PPARγ antagonists. We found that Phe282, Arg288, and Lys367 interact with antagonists more than with agonists and partial agonists. We then identified several potential PPARγ antagonists by virtual screening of the PPARγ allosteric pocket. The glucose transporter 1 (GLUT1) is a uniporter protein that facilitates the transport of glucose across the plasma membranes of mammalian cells. As GLUT1 is overexpressed in numerous tumors, this transporter is a potential target for cancer treatment. GLUT1 works through conformational switching from an outward-open (OOP) to an inward-open (IOP) conformation passing through an occluded conformation. We sought to determine which conformation is favored for ligand binding by molecular docking studies of known GLUT1 inhibitors with the different GLUT1 conformers. Our data revealed that the IOP is the preferred conformation and that residues Phe291, Phe379, Glu380, Trp388, and Trp412 may play important roles in ligand binding to GLUT1. To identify new chemotypes targeting GLUT1, we built a pharmacophore model and searched against an NCI compound database. Sixteen hit molecules with good docking scores were screened for GLUT1 inhibition and anti-proliferative activities. From these, we identified four compounds that inhibited cell viability in an HCT116 colon cancer cell line. We also determined that one of these, NSC295720, inhibited GLUT1 in a biochemical assay

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 PPARλ 3 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 PPARλ 3, 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 PPARλ 3 provides a rationale for the different activation of the ligand towards PPARα and PPARλ 3, suggesting a novel basis for ligand design

Virtual Screening as a Technique for PPAR Modulator Discovery

Virtual screening (VS) is a discovery technique to identify novel compounds with therapeutic and preventive efficacy against disease. Our current focus is on the in silico screening and discovery of novel peroxisome proliferator-activated receptor-gamma (PPARγ) agonists. It is well recognized that PPARγ agonists have therapeutic applications as insulin sensitizers in type 2 diabetes or as anti-inflammatories. VS is a cost- and time-effective means for identifying small molecules that have therapeutic potential. Our long-term goal is to devise computational approaches for testing the PPARγ-binding activity of extensive naturally occurring compound libraries prior to testing agonist activity using ligand-binding and reporter assays. This review summarizes the high potential for obtaining further fundamental understanding of PPARγ biology and development of novel therapies for treating chronic inflammatory diseases through evolution and implementation of computational screening processes for immunotherapeutics in conjunction with experimental methods for calibration and validation of results

IN SILICO SCREENING OF POTENT PPARGAMMA AGONISTS AMONG NATURAL ANTICANCER COMPOUNDS OF INDIAN ORIGIN

ABSTRACTObjective: Naturally occurring anticancer compounds of Indian origin are well-known for potential therapeutic values. A better understanding ofthe intermolecular interactions of these compounds with peroxisome proliferator-activated receptor gamma (PPARγ) is essential, as its activity isreported in many of the cancers involving colon, breast, gastric, and lung. By this study, it is attempted to perform an in silico screening of naturalanticancer compounds of Indian origin with PPARγ ligand binding domain (LBD). The potential anticancer leads ranked in this study will also exertan additional advantage of PPARγ activity modulation. As PPARγ is also an important nuclear hormone receptor that modulates transcriptionalregulation of lipid and glucose homeostasis and also a key target for many of the anti-diabetic medications, the compounds ranked by this study willalso be utilized for other related therapeutic effects.Methods: This study features in silico screening of compounds from Indian Plant Anticancer compounds database against PPARγ LBD main performedSchrodinger glide virtual screening and docking module to delineate potential PPARγ agonists. Finally, the most potential lead was also subjected tomolecular dynamics simulation to infer the stability of complex formation.Results: The results reveal that majority of the top ranking compounds that interact with LBD was found to be flavonoids, and all these compoundswere found to interact with key residues involved in PPARγ agonist interactions.Conclusion: The leads from this study would be helpful in better understanding of the potential of naturally occurring anticancer compounds ofIndian origin toward targeting PPARγ.Keywords: Peroxisome proliferator-activated receptor-gamma, Agonists, Docking, Natural compounds, Anticancer.Â

PEROXISOME PROLIFERATOR-ACTIVATED RECEPTOR-GAMMA, AN EMERGING POTENTIAL TARGET TO COMBAT METABOLIC DISORDER

  Day-by-day metabolic disorder/syndrome (MS) falling in love with current lifestyle status of everyone especially after study age group of people. If we look carefully around us, we will see evidence is growing up of diabetic, obese, and hypertensive population regularly. In urgencies of above view extensive literature survey has been done prioritizing prevalence of metabolic disorder and peroxisome proliferator-activated receptor-gamma (PPAR-γ) as potential target protein. This review covered current status of MS emphasizing diabetes along with its management criteria. Special importance is given to PPAR-γ exploring its metabolic regulation and structural orientation for understanding ligand-protein interaction. Development of PPAR-γ agonist thiazolidinediones (TZDs) and other pharmacodynamic importance of this nuclear receptor also discussed. Being as nuclear receptor more genomics exploitation needs to be done emphasizing minimization of cardiac adverse effect. Selective PPAR modulator (SPPRM), TZDs are the master regulator of adipogenesis and angiogenesis which makes TZD more interesting topic to explore. Developmental hierarchy suggests that in a few years from now PPAR-γ won't be in the list of double edge sword

VIRTUAL SCREENING STUDIES OF SEAWEED METABOLITES FOR PREDICTING POTENTIAL PPARγ AGONISTS

Objective: Peroxisome Proliferator-Activated Receptor-gamma (PPARγ) is a crucial nuclear hormone receptor, which modulates the transcriptional regulation of lipid and glucose homeostasis. It plays a crucial role in many of the metabolic and inflammatory systems. It is a key target for many of the anti-diabetic medications. Perturbation of PPARγ activity is also observed in many of the cancers involving colon, breast, gastric and lung. Thus, it is considered to be the hub molecule for targeting many of these cellular disorders. Seaweed metabolites have been well documented to be novel structural entities with a broad spectrum of pharmacological values. However, it is yet to be utilized for screening PPARγ agonists.Methods: In this study, virtual screening of PPARγ Ligand Binding Domain (LBD) was performed against the datasets from SeaWeed Metabolite Database (SWMD) using Schrodinger Glide High Throughput Virtual Screening module to identify potential PPARγ agonists. Further, the most potential lead was also subjected to molecular dynamics simulation to infer the stability of complex formation.Results: The results have revealed that bromophenolic compounds from the genus Avrainvillea to interact with documented key residues of LBD involved in agonist interactions. Many other metabolites from the genus Rhodomela, Leathesia, Bifurcaria, Osmundaria, Cymopolia also showed significant interactions with LBD of PPARγ.Conclusion: The insights from this study will pave the way for further exploration of lead compounds from seaweed metabolites targeting PPARγ. Â

Kern-basierte Lernverfahren für das virtuelle Screening

We investigate the utility of modern kernel-based machine learning methods for ligand-based virtual screening. In particular, we introduce a new graph kernel based on iterative graph similarity and optimal assignments, apply kernel principle component analysis to projection error-based novelty detection, and discover a new selective agonist of the peroxisome proliferator-activated receptor gamma using Gaussian process regression. Virtual screening, the computational ranking of compounds with respect to a predicted property, is a cheminformatics problem relevant to the hit generation phase of drug development. Its ligand-based variant relies on the similarity principle, which states that (structurally) similar compounds tend to have similar properties. We describe the kernel-based machine learning approach to ligand-based virtual screening; in this, we stress the role of molecular representations, including the (dis)similarity measures defined on them, investigate effects in high-dimensional chemical descriptor spaces and their consequences for similarity-based approaches, review literature recommendations on retrospective virtual screening, and present an example workflow. Graph kernels are formal similarity measures that are defined directly on graphs, such as the annotated molecular structure graph, and correspond to inner products. We review graph kernels, in particular those based on random walks, subgraphs, and optimal vertex assignments. Combining the latter with an iterative graph similarity scheme, we develop the iterative similarity optimal assignment graph kernel, give an iterative algorithm for its computation, prove convergence of the algorithm and the uniqueness of the solution, and provide an upper bound on the number of iterations necessary to achieve a desired precision. In a retrospective virtual screening study, our kernel consistently improved performance over chemical descriptors as well as other optimal assignment graph kernels. Chemical data sets often lie on manifolds of lower dimensionality than the embedding chemical descriptor space. Dimensionality reduction methods try to identify these manifolds, effectively providing descriptive models of the data. For spectral methods based on kernel principle component analysis, the projection error is a quantitative measure of how well new samples are described by such models. This can be used for the identification of compounds structurally dissimilar to the training samples, leading to projection error-based novelty detection for virtual screening using only positive samples. We provide proof of principle by using principle component analysis to learn the concept of fatty acids. The peroxisome proliferator-activated receptor (PPAR) is a nuclear transcription factor that regulates lipid and glucose metabolism, playing a crucial role in the development of type 2 diabetes and dyslipidemia. We establish a Gaussian process regression model for PPAR gamma agonists using a combination of chemical descriptors and the iterative similarity optimal assignment kernel via multiple kernel learning. Screening of a vendor library and subsequent testing of 15 selected compounds in a cell-based transactivation assay resulted in 4 active compounds. One compound, a natural product with cyclobutane scaffold, is a full selective PPAR gamma agonist (EC50 = 10 +/- 0.2 muM, inactive on PPAR alpha and PPAR beta/delta at 10 muM). The study delivered a novel PPAR gamma agonist, de-orphanized a natural bioactive product, and, hints at the natural product origins of pharmacophore patterns in synthetic ligands.Wir untersuchen moderne Kern-basierte maschinelle Lernverfahren für das Liganden-basierte virtuelle Screening. Insbesondere entwickeln wir einen neuen Graphkern auf Basis iterativer Graphähnlichkeit und optimaler Knotenzuordnungen, setzen die Kernhauptkomponentenanalyse für Projektionsfehler-basiertes Novelty Detection ein, und beschreiben die Entdeckung eines neuen selektiven Agonisten des Peroxisom-Proliferator-aktivierten Rezeptors gamma mit Hilfe von Gauß-Prozess-Regression. Virtuelles Screening ist die rechnergestützte Priorisierung von Molekülen bezüglich einer vorhergesagten Eigenschaft. Es handelt sich um ein Problem der Chemieinformatik, das in der Trefferfindungsphase der Medikamentenentwicklung auftritt. Seine Liganden-basierte Variante beruht auf dem Ähnlichkeitsprinzip, nach dem (strukturell) ähnliche Moleküle tendenziell ähnliche Eigenschaften haben. In unserer Beschreibung des Lösungsansatzes mit Kern-basierten Lernverfahren betonen wir die Bedeutung molekularer Repräsentationen, einschließlich der auf ihnen definierten (Un)ähnlichkeitsmaße. Wir untersuchen Effekte in hochdimensionalen chemischen Deskriptorräumen, ihre Auswirkungen auf Ähnlichkeits-basierte Verfahren und geben einen Literaturüberblick zu Empfehlungen zur retrospektiven Validierung, einschließlich eines Beispiel-Workflows. Graphkerne sind formale Ähnlichkeitsmaße, die inneren Produkten entsprechen und direkt auf Graphen, z.B. annotierten molekularen Strukturgraphen, definiert werden. Wir geben einen Literaturüberblick über Graphkerne, insbesondere solche, die auf zufälligen Irrfahrten, Subgraphen und optimalen Knotenzuordnungen beruhen. Indem wir letztere mit einem Ansatz zur iterativen Graphähnlichkeit kombinieren, entwickeln wir den iterative similarity optimal assignment Graphkern. Wir beschreiben einen iterativen Algorithmus, zeigen dessen Konvergenz sowie die Eindeutigkeit der Lösung, und geben eine obere Schranke für die Anzahl der benötigten Iterationen an. In einer retrospektiven Studie zeigte unser Graphkern konsistent bessere Ergebnisse als chemische Deskriptoren und andere, auf optimalen Knotenzuordnungen basierende Graphkerne. Chemische Datensätze liegen oft auf Mannigfaltigkeiten niedrigerer Dimensionalität als der umgebende chemische Deskriptorraum. Dimensionsreduktionsmethoden erlauben die Identifikation dieser Mannigfaltigkeiten und stellen dadurch deskriptive Modelle der Daten zur Verfügung. Für spektrale Methoden auf Basis der Kern-Hauptkomponentenanalyse ist der Projektionsfehler ein quantitatives Maß dafür, wie gut neue Daten von solchen Modellen beschrieben werden. Dies kann zur Identifikation von Molekülen verwendet werden, die strukturell unähnlich zu den Trainingsdaten sind, und erlaubt so Projektionsfehler-basiertes Novelty Detection für virtuelles Screening mit ausschließlich positiven Beispielen. Wir führen eine Machbarkeitsstudie zur Lernbarkeit des Konzepts von Fettsäuren durch die Hauptkomponentenanalyse durch. Der Peroxisom-Proliferator-aktivierte Rezeptor (PPAR) ist ein im Zellkern vorkommender Rezeptor, der den Fett- und Zuckerstoffwechsel reguliert. Er spielt eine wichtige Rolle in der Entwicklung von Krankheiten wie Typ-2-Diabetes und Dyslipidämie. Wir etablieren ein Gauß-Prozess-Regressionsmodell für PPAR gamma-Agonisten mit chemischen Deskriptoren und unserem Graphkern durch gleichzeitiges Lernen mehrerer Kerne. Das Screening einer kommerziellen Substanzbibliothek und die anschließende Testung 15 ausgewählter Substanzen in einem Zell-basierten Transaktivierungsassay ergab vier aktive Substanzen. Eine davon, ein Naturstoff mit Cyclobutan-Grundgerüst, ist ein voller selektiver PPAR gamma-Agonist (EC50 = 10 +/- 0,2 muM, inaktiv auf PPAR alpha und PPAR beta/delta bei 10 muM). Unsere Studie liefert einen neuen PPAR gamma-Agonisten, legt den Wirkmechanismus eines bioaktiven Naturstoffs offen, und erlaubt Rückschlüsse auf die Naturstoffursprünge von Pharmakophormustern in synthetischen Liganden

Graph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix

Prediction of protein-ligand complexes for flexible proteins remains still a challenging problem in computational structural biology and drug design. Here we present two novel deep neural network approaches with significant improvement in efficiency and accuracy of binding mode prediction on a large and diverse set of protein systems compared to standard docking. Whereas the first graph convolutional network is used for re-ranking poses the second approach aims to generate and rank poses independent of standard docking approaches. This novel approach relies on the prediction of distance matrices between ligand atoms and protein C_alpha atoms thus incorporating side-chain flexibility implicitly