A promising in silico protocol to develop novel PPARγ antagonists as potential anticancer agents: Design,synthesis and experimental validation via PPARγ protein activity and competitive binding assay

Peroxisome proliferator-activated receptor gamma (PPARγ), a member of the nuclear receptor superfamily is an excellent example of targets that orchestrates cancer, inflammation, lipid and glucose metabolism. We report a protocol for the development of novel PPARγ antagonists by employing 3D QSAR based virtual screening for the identification of ligands with anticancer properties. The models are generated based on a large and diverse set of PPARγ antagonist ligands by the HYPOGEN algorithm using Discovery Studio 2019 drug design software. Among the 10 hypotheses generated, Hypotheses 2 showed the highest correlation coefficient values of 0.95 with less RMS deviation of 1.193. Validation of the developed pharmacophore model was performed by Fischer’s randomization and screening against test and decoy set. The GH score or goodness score was found to be 0.81 indicating moderate to a good model. The selected pharmacophore model Hypo 2 was used as a query model for further screening of 11,145 compounds from the PubChem, sc-PDB structure database, and designed novel ligands. Based on fit values and ADMET filter, the final 10 compounds with the predicated activity of ≤ 3 nM were subjected for docking analysis. Docking analysis revealed the unique binding mode with hydrophobic amino acid that can cause destabilization of the H12 which is an important molecular mechanism to prove its antagonist action. Based on high CDocker scores, Cpd31 was synthesized, purified, analyzed and screened for PPARγ competitive binding by TR-FRET assay. The biochemical protein binding results matched the predicted results. Further, Cpd31 was screened against cancer cells and validated the results.     

Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores

The design of biologically active compounds from ligand-free protein structures using a structure-based approach is still a major challenge. In this paper, we present a fast knowledge-based approach (HS-Pharm) that allows the prioritization of cavity atoms that should be targeted for ligand binding, by training machine learning algorithms with atom-based fingerprints of known ligand-binding pockets. The knowledge of hot spots for ligand binding is here used for focusing structure-based pharmacophore models. Three targets of pharmacological interest (neuraminidase, beta2 adrenergic receptor, and cyclooxygenase-2) were used to test the evaluated methodology, and the derived structure-based pharmacophores were used in retrospective virtual screening studies. The current study shows that structure-based pharmacophore screening is a powerful technique for the fast identification of potential hits in a chemical library, and that it is a valid alternative to virtual screening by molecular docking.     

Three-dimensional pharmacophore modeling of liver-X receptor agonists

High cholesterol levels contribute to hyperlipidemia. Liver X receptors (LXRs) are the drug targets. LXRs regulate the cholesterol absorption, biosynthesis, transportation, and metabolism. Novel agonists of LXR, especially LXRβ, are attractive solutions for treating hyperlipidemia. In order to discover novel LXRβ agonists, a three-dimensional pharmacophore model was built based upon known LXRβ agonists. The model was validated with a test set, a virtual screening experiment, and the FlexX docking approach. Results show that the model is capable of predicting a LXRβ agonist activity. Ligand-based virtual screening results can be refined by cross-linking by structure-based approaches. This is because two ligands that are mapped in the same way to the same pharmacophore model may have significantly different binding behaviors in the receptor's binding pocket. This paper reports our approach to identify reliable pharmacophore models through combining both ligand- and structure-based approaches.     

Isoxazolyl-serine-based agonists of peroxisome proliferator-activated receptor: design, synthesis, and effects on cardiomyocyte differentiation

The peroxisome proliferator-activated receptors (PPARs) are important molecular targets for the development of drugs for the treatment of human metabolic diseases, inflammation, and cancer. They are known to be activated by a variety of structurally diverse compounds. Using a structure-based drug design approach, we designed and synthesized a series of novel isoxazolyl-serine-based PPAR ligands possessing moderate affinities. Some of the new PPAR ligands were able to stimulate cardiomyocyte differentiation from murine ES cells. Ligand 1a was the most active one tested at concentrations between 1.25 to 20 muM between days 2-6, coinciding with the period when mesodermal cells can be recruited to become cardiomyocytes. Notably, the known PPARalpha, gamma, and delta agonists tested, e.g., fenofibrate, rosiglitazone, and GW501516, were inactive in this assay.     

Identification of potential Tpx inhibitors against pathogen-host interactions

Yersinia organisms cause many infectious diseases by invading human cells and delivering their virulence factors via the type three secretion system (T3SS). One alternative strategy in the fight against these pathogenic organisms is to interfere with their T3SS. Previous studies demonstrated that thiol peroxidase, Tpx is functional in the assembly of T3SS and its inhibition by salicylidene acylhydrazides prevents the secretion of pathogenic effectors. In this study, the aim was to identify potential inhibitors of Tpx using an integrated approach starting with high throughput virtual screening and ending with molecular dynamics simulations of selected ligands. Virtual screening of ZINC database of 500,000 compounds via ligand-based and structure-based pharmacophore models retrieved 10,000 hits. The structure-based pharmacophore model was validated using high-throughput virtual screening (HTVS). After multistep docking (SP and XP), common scaffolds were used to find common substructures and the ligand binding poses were optimized using induced fit docking. The stability of the protein–ligand complex was examined with molecular dynamics simulations and the binding free energy of the complex was calculated. As a final outcome eight compounds with different chemotypes were proposed as potential inhibitors for Tpx. The eight ligands identified by a detailed virtual screening protocol can serve as leads in future drug design efforts against the destructive actions of pathogenic bacteria.     

Parallel screening and activity profiling with HIV protease inhibitor pharmacophore models

Parallel Screening has been introduced as an in silico method to predict the potential biological activities of compounds by screening them with a multitude of pharmacophore models. This study presents an early application example employing a Pipeline Pilot-based screening platform for automatic large-scale virtual activity profiling. An extensive set of HIV protease inhibitor pharmacophore models was used to screen a selection of active and inactive compounds. Furthermore, we aimed to address the usually critically eyed point, whether it is possible in a parallel screening system to differentiate between similar molecules/molecules acting on closely related proteins, and therefore we incorporated a collection of other protease inhibitors including aspartic protease inhibitors. The results of the screening experiments show a clear trend toward most extensive retrieval of known active ligands, followed by the general protease inhibitors and lowest recovery of inactive compounds.     

Discovery of novel cathepsin S inhibitors by pharmacophore-based virtual high-throughput screening

The cysteine protease cathepsin S (CatS) is involved in the pathogenesis of autoimmune disorders, atherosclerosis, and obesity. Therefore, it represents a promising pharmacological target for drug development. We generated ligand-based and structure-based pharmacophore models for noncovalent and covalent CatS inhibitors to perform virtual high-throughput screening of chemical databases in order to discover novel scaffolds for CatS inhibitors. An in vitro evaluation of the resulting 15 structures revealed seven CatS inhibitors with kinetic constants in the low micromolar range. These compounds can be subjected to further chemical modifications to obtain drugs for the treatment of autoimmune disorders and atherosclerosis.     

Construction and Evaluation of Merged Pharmacophore Based on Peroxisome Proliferator Receptor-Alpha Agonists

Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore (LBP) and structure-based pharmacophore (SBP). LBP can be utilized to identify active compounds usual with lower accuracy, and SBP is able to use for distinguishing active compounds from inactive compounds with frequently higher missing rates. Merged pharmacophore (MP) is presented to integrate advantages and avoid shortcomings of LBP and SBP. In this work, LBP and SBP models were constructed for the study of peroxisome proliferator receptor-alpha (PPARα) agonists. According to the comparison of the two types of pharmacophore models, mainly and secondarily pharmacological features were identified. The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model. Then, the reliability and screening efficiency of the best MP model were validated by three databases. The best MP model was utilized to compute PPARα activity of compounds from traditional Chinese medicine. The screening efficiency of MP model outperformed individual LBP or SBP model for PPARα agonists, and was similar to combinatorial screening of LBP and SBP. However, MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP, which would be beneficial to guide drug design and optimization.     

Novel approach to structure-based pharmacophore search using computational geometry and shape matching techniques

Computationally efficient structure-based virtual screening methods have recently been reported that seek to find effective means to utilize experimental structure information without employing detailed molecular docking calculations. These tools can be coupled with efficient experimental screening technologies to improve the probability of identifying hits and leads for drug discovery research. Commercial software ROCS (rapid overlay of chemical structures) from Open Eye Scientific is such an example, which is a shape-based virtual screening method using the 3D structure of a ligand, typically from a bound X-ray costructure, as the query. We report here the development of a new structure-based pharmacophore search method (called Shape4) for virtual screening. This method adopts a variant of the ROCS shape technology and expands its use to work with an empty crystal structure. It employs a rigorous computational geometry method and a deterministic geometric casting algorithm to derive the negative image (i.e., pseudoligand) of a target binding site. Once the negative image (or pseudoligand) is generated, an efficient shape comparison algorithm in the commercial OE SHAPE Toolkit is adopted to compare and match small organic molecules with the shape of the pseudoligand. We report the detailed computational protocol and its computational validation using known biologically active compounds extracted from the WOMBAT database. Models derived for five selected targets were used to perform the virtual screening experiments to obtain the enrichment data for various virtual screening methods. It was found that our approach afforded similar or better enrichment ratios than other related methods, often with better diversity among the top ranking computational hits.     

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.     

Molecular docking and pharmacophore filtering in the discovery of dual-inhibitors for human leukotriene A4 hydrolase and leukotriene C4 synthase

Combination of drugs for multiple targets has been a standard treatment in treating various diseases. A single chemical entity that acts upon multiple targets is emerging nowadays because of their predictable pharmacokinetic and pharmacodynamic properties. We have employed a computer-aided methodology combining molecular docking and pharmacophore filtering to identify chemical compounds that can simultaneously inhibit the human leukotriene hydrolase (hLTA4H) and the human leukotriene C4 synthase (hLTC4S) enzymes. These enzymes are the members of arachidonic acid pathway and act upon the same substrate, LTA4, producing different inflammatory products. A huge set of 4966 druglike compounds from the Maybridge database were docked into the active site of hLTA4H using the GOLD program. Common feature pharmacophore models were developed from the known inhibitors of both the targets using Accelrys Discovery Studio 2.5. The hits from the hLTA4H docking were filtered to match the chemical features of both the pharmacophore models. The compounds that resulted from the pharmacophore filtering were docked into the active site of hLTC4S and the hits those bind well at both the active sites and matched the pharmacophore models were identified as possible dual inhibitors for hLTA4H and hLTC4S enzymes. Reverse validation was performed to ensure the results of the study.     

Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments

Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.     

Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring

Shape-based methods for aligning and scoring ligands have proven to be valuable in the field of computer-aided drug design. Here, we describe a new shape-based flexible ligand superposition and virtual screening method, Phase Shape, which is shown to rapidly produce accurate 3D ligand alignments and efficiently enrich actives in virtual screening. We describe the methodology, which is based on the principle of atom distribution triplets to rapidly define trial alignments, followed by refinement of top alignments to maximize the volume overlap. The method can be run in a shape-only mode or it can include atom types or pharmacophore feature encoding, the latter consistently producing the best results for database screening. We apply Phase Shape to flexibly align molecules that bind to the same target and show that the method consistently produces correct alignments when compared with crystal structures. We then illustrate the effectiveness of the method for identifying active compounds in virtual screening of eleven diverse targets. Multiple parameters are explored, including atom typing, query structure conformation, and the database conformer generation protocol. We show that Phase Shape performs well in database screening calculations when compared with other shape-based methods using a common set of actives and decoys from the literature.     

Effective virtual screening protocol for CYP2C9 ligands using a screening site constructed from flurbiprofen and S-warfarin pockets

An effective virtual screening protocol was developed against an extended active site of CYP2C9, which was derived from X-ray structures complexed with flubiprofen and S-warfarin. Virtual screening has been effectively supported by our structure-based pharmacophore model. Importance of hot residues identified by mutation data and structural analysis was first estimated in an enrichment study. Key role of Arg108 and Phe114 in ligand binding was also underlined. Our screening protocol successfully identified 76% of known CYP2C9 ligands in the top 1% of the ranked database resulting 76-fold enrichment relative to random situation. Relevance of the protocol was further confirmed in selectivity studies, when 89% of CYP2C9 ligands were retrieved from a mixture of CYP2C9 and CYP2C8 ligands, while only 22% of CYP2C8 ligands were found applying the structure-based pharmacophore constraints. Moderate discrimination of CYP2C9 ligands from CYP2C18 and CYP2C19 ligands could also be achieved extending the application domain of our virtual screening protocol for the entire CYP2C family. Our findings further demonstrate the existence of an active site comprising of at least two binding pockets and strengthens the need of involvement of protein flexibility in virtual screening.     

Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example

Virtual screening of large libraries of organic compounds combined with pharmacological high throughput screening is widely used for drug discovery in the pharmaceutical industry. Our aim was to explore the efficiency of using a biased 3D database comprising secondary metabolites from antiinflammatory medicinal plants as a source for the virtual screening. For this study pharmacophore models of cyclooxygenase I and II (COX-1, COX-2), key enzymes in the inflammation process, were generated with structure-based as well as common feature based modeling, resulting in three COX hypotheses. Four different multiconfomational 3D databases limited in molecular weight between 300 and 700 Da were applied to the screening in order to compare and analyze the obtained hit rates. Two of them were created in-house (DIOS, NPD). The database DIOS consists of 2752 compounds from phytochemical reports of antiinflammatory medicinal plants described by the ethnopharmacological source 'De material medica' of Pedanius Dioscorides, whereas NPD contains almost 80,000 compounds gathered arbitrarily from natural sources. In addition, two available multiconformational 3D libraries comprising marketed and development drug substances (DWI and NCI), mainly originating from synthesis, were used for comparison. As a test of the pharmacophore models' capability in natural sources, the models were used to search for known COX inhibitory natural products. This was achieved with some exceptions, which are discussed in the paper. Depending on the hypothesis used, DWI and NCI library searches produced hit rates in the range of 6.6% to 13.7%. A slight increase of the number of molecules assessed for binding was achieved with the database of natural products (NPD). Using the biased 3D database DIOS, however, the average increase of efficiency reached 77% to 133% compared to the hit rates resulting from WDI and NCI. The statistical benefit of a combination of an ethnopharmacological approach with the potential of computer aided drug discovery by in silico screening was demonstrated exemplified on the applied targets COX-1 and COX-2.     

Protein flexibility and species specificity in structure-based drug discovery: dihydrofolate reductase as a test system

In structure-based drug discovery, researchers would like to identify all possible scaffolds for a given target. However, techniques that push the boundaries of chemical space could lead to many false positives or inhibitors that lack specificity for the target. Is it possible to broadly identify the appropriate chemical space for the inhibitors and yet maintain target specificity? To address this question, we have turned to dihydrofolate reductase (DHFR), a well-studied metabolic enzyme of pharmacological relevance. We have extended our multiple protein structure (MPS) method for receptor-based pharmacophore models to use multiple X-ray crystallographic structures. Models were created for DHFR from human and Pneumocystis carinii. These models incorporate a fair degree of protein flexibility and are highly selective for known DHFR inhibitors over drug-like non-inhibitors. Despite sharing a highly conserved active site, the pharmacophore models reflect subtle differences between the human and P. carinii forms, which identify species-specific, high-affinity inhibitors. We also use structures of DHFR from Candida albicans as a counter example. The available crystal structures show little flexibility, and the resulting models give poorer performance in identifying species-specific inhibitors. Therapeutic success for this system may depend on achieving species specificity between the related human host and these key fungal targets. The MPS technique is a promising advance for structure-based drug discovery for DHFR and other proteins of biomedical interest.