Patrick Markt

Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection--what can we learn from earlier mistakes?

Within the last few years a considerable amount of evaluative studies has been published that investigate the performance of 3D virtual screening approaches. Thereby, in particular assessments of protein–ligand docking are facing remarkable interest in the scientific community. However, comparing virtual screening approaches is a non-trivial task. Several publications, especially in the field of molecular docking, suffer from shortcomings that are likely to affect the significance of the results considerably. These quality issues often arise from poor study design, biasing, by using improper or inexpressive enrichment descriptors, and from errors in interpretation of the data output. In this review we analyze recent literature evaluating 3D virtual screening methods, with focus on molecular docking. We highlight problematic issues and provide guidelines on how to improve the quality of computational studies. Since 3D virtual screening protocols are in general assessed by their ability to discriminate between active and inactive compounds, we summarize the impact of the composition and preparation of test sets on the outcome of evaluations. Moreover, we investigate the significance of both classic enrichment parameters and advanced descriptors for the performance of 3D virtual screening methods. Furthermore, we review the significance and suitability of RMSD as a measure for the accuracy of protein–ligand docking algorithms and of conformational space sub sampling algorithms.

In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens

The identification of targets whose interaction is likely to result in the successful treatment of a disease is of growing interest for natural product scientists. In the current study we performed an exemplary application of a virtual parallel screening approach to identify potential targets for 16 secondary metabolites isolated and identified from the aerial parts of the medicinal plant RUTA GRAVEOLENS L. Low energy conformers of the isolated constituents were simultaneously screened against a set of 2208 pharmacophore models generated in-house for the IN SILICO prediction of putative biological targets, i. e., target fishing. Based on the predicted ligand-target interactions, we focused on three biological targets, namely acetylcholinesterase (AChE), the human rhinovirus (HRV) coat protein and the cannabinoid receptor type-2 (CB (2)). For a critical evaluation of the applied parallel screening approach, virtual hits and non-hits were assayed on the respective targets. For AChE the highest scoring virtual hit, arborinine, showed the best inhibitory IN VITRO activity on AChE (IC (50) 34.7 muM). Determination of the anti-HRV-2 effect revealed 6,7,8-trimethoxycoumarin and arborinine to be the most active antiviral constituents with IC (50) values of 11.98 muM and 3.19 muM, respectively. Of these, arborinine was predicted virtually. Of all the molecules subjected to parallel screening, one virtual CB (2) ligand was obtained, i. e., rutamarin. Interestingly, in experimental studies only this compound showed a selective activity to the CB (2) receptor ( Ki of 7.4 muM) by using a radioligand displacement assay. The applied parallel screening paradigm with constituents of R. GRAVEOLENS on three different proteins has shown promise as an IN SILICO tool for rational target fishing and pharmacological profiling of extracts and single chemical entities in natural product research.

Computer-Aided Discovery, Validation, and Mechanistic Characterization of Novel Neolignan Activators of Peroxisome Proliferator-Activated Receptor gamma

Peroxisome proliferator-activated receptor gamma (PPARγ) agonists are used for the treatment of type 2 diabetes and metabolic syndrome. However, the currently used PPARγ agonists display serious side effects, which has led to a great interest in the discovery of novel ligands with favorable properties. The aim of our study was to identify new PPARγ agonists by a PPARγ pharmacophore–based virtual screening of 3D natural product libraries. This in silico approach led to the identification of several neolignans predicted to bind the receptor ligand binding domain (LBD). To confirm this prediction, the neolignans dieugenol, tetrahydrodieugenol, and magnolol were isolated from the respective natural source or synthesized and subsequently tested for PPARγ receptor binding. The neolignans bound to the PPARγ LBD with EC50 values in the nanomolar range, exhibiting a binding pattern highly similar to the clinically used agonist pioglitazone. In intact cells, dieugenol and tetrahydrodieugenol selectively activated human PPARγ-mediated, but not human PPARα- or -β/δ-mediated luciferase reporter expression, with a pattern suggesting partial PPARγ agonism. The coactivator recruitment study also demonstrated partial agonism of the tested neolignans. Dieugenol, tetrahydrodieugenol, and magnolol but not the structurally related eugenol induced 3T3-L1 preadipocyte differentiation, confirming effectiveness in a cell model with endogenous PPARγ expression. In conclusion, we identified neolignans as novel ligands for PPARγ, which exhibited interesting activation profiles, recommending them as potential pharmaceutical leads or dietary supplements.

Pharmacophore modeling and parallel screening for PPAR ligands

We describe the generation and validation of pharmacophore models for PPARs, as well as a large scale validation of the parallel screening approach by screening PPAR ligands against a large database of structure-based models. A large test set of 357 PPAR ligands was screened against 48 PPAR models to determine the best models for agonists of PPAR-α, PPAR-δ, and PPAR-γ. Afterwards, a parallel screen was performed using the 357 PPAR ligands and 47 structure-based models for PPARs, which were integrated into a 1537 models comprising in-house pharmacophore database, to assess the enrichment of PPAR ligands within the PPAR hypotheses. For these purposes, we categorized the 1537 database models into 181 protein targets and developed a score that ranks the retrieved targets for each ligand. Thus, we tried to find out if the concept of parallel screening is able to predict the correct pharmacological target for a set of compounds. The PPAR target was ranked first more often than any other target. This confirms the ability of parallel screening to forecast the pharmacological active target for a set of compounds.

Fast and efficient in silico 3D screening: toward maximum computational efficiency of pharmacophore-based and shape-based approaches.

In continuation of our recent studies on the quality of conformational models generated with CATALYST and OMEGA we present a large-scale survey focusing on the impact of conformational model quality and several screening parameters on pharmacophore-based and shape-based virtual high throughput screening (vHTS). Therefore, we collected known active compounds of CDK2, p38 MAPK, PPAR-gamma, and factor Xa and built a set of druglike decoys using ilib:diverse. Subsequently, we generated 3D structures using CORINA and also calculated conformational models for all compounds using CAESAR, CATALYST FAST, and OMEGA. A widespread set of 103 structure-based pharmacophore models was developed with LigandScout for virtual screening with CATALYST. The performance of both database search modes (FAST and BEST flexible database search) as well as the fit value calculation procedures (FAST and BEST fit) available in CATALYST were analyzed in terms of their ability to discriminate between active and inactive compounds and in terms of efficiency. Moreover, these results are put in direct comparison to the performance of the shape-based virtual screening platform ROCS. Our results prove that high enrichment rates are not necessarily in conflict with efficient vHTS settings: In most of the experiments, we obtained the highest yield of actives in the hit list when parameter sets for the fastest search algorithm were used.

Discovery of Novel PPAR Ligands by a Virtual Screening Approach Based on Pharmacophore Modeling, 3D Shape, and Electrostatic Similarity Screening

Peroxisome proliferator-activated receptors (PPARs) are important targets for drugs used in the treatment of atherosclerosis, dyslipidaemia, obesity, type 2 diabetes, and other diseases caused by abnormal regulation of the glucose and lipid metabolism. We applied a virtual screening workflow based on a combination of pharmacophore modeling with 3D shape and electrostatic similarity screening techniques to discover novel scaffolds for PPAR ligands. From the resulting 10 virtual screening hits, five tested positive in human PPAR ligand-binding domain (hPPAR-LBD) transactivation assays and showed affinities for PPAR in a competitive binding assay. Compounds 5, 7, and 8 were identified as PPAR-alpha agonists, whereas compounds 2 and 9 showed agonistic activity for hPPAR-gamma. Moreover, compound 9 was identified as a PPAR-delta antagonist. These results demonstrate that our virtual screening protocol is able to enrich novel scaffolds for PPAR ligands that could be useful for drug development in the area of atherosclerosis, dyslipidaemia, and type 2 diabetes.

Pharmacophore Modeling and Virtual Screening for Novel Acidic Inhibitors of Microsomal Prostaglandin E2 Synthase-1 (mPGES-1)

Microsomal prostaglandin E2 synthase-1 (mPGES-1) catalyzes prostaglandin E2 formation and is considered as a potential anti-inflammatory pharmacological target. To identify novel chemical scaffolds active on this enzyme, two pharmacophore models for acidic mPGES-1 inhibitors were developed and theoretically validated using information on mPGES-1 inhibitors from literature. The models were used to screen chemical databases supplied from the National Cancer Institute (NCI) and the Specs. Out of 29 compounds selected for biological evaluation, nine chemically diverse compounds caused concentration-dependent inhibition of mPGES-1 activity in a cell-free assay with IC50 values between 0.4 and 7.9 μM, respectively. Further pharmacological characterization revealed that also 5-lipoxygenase (5-LO) was inhibited by most of these active compounds in cell-free and cell-based assays with IC50 values in the low micromolar range. Together, nine novel chemical scaffolds inhibiting mPGES-1 are presented that may possess anti-inflammatory properties based on the interference with eicosanoid biosynthesis.

Identification of HIV-1 reverse transcriptase dual inhibitors by a combined shape-, 2D-fingerprint- and pharmacophore-based virtual screening approach.

We report the first application of ligand-based virtual screening (VS) methods for discovering new compounds able to inhibit both human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT)-associated functions, DNA polymerase and ribonuclease H (RNase H) activities. The overall VS campaign consisted of two consecutive screening processes. In the first, the VS platform Rapid Overlay of Chemical Structures (ROCS) was used to perform in silico shape-based similarity screening on the NCI compounds database in which a hydrazone derivative, previously shown to inhibit the HIV-1 RT, was chosen. As a result, 34 hit molecules were selected and assayed on both RT-associated functions. In the second, the 4 most potent RT inhibitors identified were selected as queries for parallel VS performed by combining shape-based, 2D-fingerprint and 3D-pharmacophore VS methods. Overall, a set of molecules characterized by new different scaffolds were identified as novel inhibitors of both HIV-1 RT-associated activities in the low micromolar range.

Discovery of a novel IKK-β inhibitor by ligand-based virtual screening techniques

Graphical abstract

Epidermis-Type Lipoxygenase 3 Regulates Adipocyte Differentiation and Peroxisome Proliferator-Activated Receptor γ Activity

ABSTRACT The nuclear receptor peroxisome proliferator-activated receptor γ (PPARγ) is essential for adipogenesis. Although several fatty acids and their derivatives are known to bind and activate PPARγ, the nature of the endogenous ligand(s) promoting the early stages of adipocyte differentiation has remained enigmatic. Previously, we showed that lipoxygenase (LOX) activity is involved in activation of PPARγ during the early stages of adipocyte differentiation. Of the seven known murine LOXs, only the unconventional LOX epidermis-type lipoxygenase 3 (eLOX3) is expressed in 3T3-L1 preadipocytes. Here, we show that forced expression of eLOX3 or addition of eLOX3 products stimulated adipogenesis under conditions that normally require an exogenous PPARγ ligand for differentiation. Hepoxilins, a group of oxidized arachidonic acid derivatives produced by eLOX3, bound to and activated PPARγ. Production of hepoxilins was increased transiently during the initial stages of adipogenesis. Furthermore, small interfering RNA-mediated or retroviral short hairpin RNA-mediated knockdown of eLOX3 expression abolished differentiation of 3T3-L1 preadipocytes. Finally, we demonstrate that xanthine oxidoreductase (XOR) and eLOX3 synergistically enhanced PPARγ-mediated transactivation. Collectively, our results indicate that hepoxilins produced by the concerted action of XOR and eLOX3 may function as PPARγ activators capable of promoting the early PPARγ-dependent steps in the conversion of preadipocytes into adipocytes.