Rational discovery of novel nuclear hormone receptor antagonists

Discovery of succinate dehydrogenase candidate fungicides via lead optimization for effective resistance management of Fusarium oxysporum f. sp. capsici

Fusarium wilt of chili caused by the fungus Fusarium oxysporum f. sp. capsici (FCO) severely reduces the production of chili worldwide. There is growing evidence of resistance to commercial fungicides targeting succinate dehydrogenase (Sdh) of FCO soliciting the development of new Sdh inhibitors (SdhIs). In the current work, optimized docking and virtual screening were used to mine twelve SdhIs from the ZINC database, followed by in vitro antifungal evaluation on spore and radial mycelium development. Four new promising SdhIs exhibiting a mean mycelium inhibition rate greater than 85.6% (F = 155.8, P = 0.001, P < 0.05) were observed on ten strains of virulent and resistant FCO. Importantly, three of the discovered molecules exhibited potent spore germination inhibition (≥ 80%, P = 0.01, P < 0.05) compared to the commonly used fungicide penthiopyrad. A significant positive correlation (r* ≥ 0.67, P < 0.05) between the activities of the newly discovered SdhIs compared to penthiopyrad against all tested FCO strains indicated a broad-spectrum fungicidal activity. The current findings indicate that the four SdhI's discovered could judiciously replace certain commercial SdhIs that some FCO displays resistance to.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03157-8.

In vitro assessment of pesticides capacity to act as agonists/antagonists of the thyroid hormone nuclear receptors

Chemicals acting as thyroid hormone disruptors (THDs) are of a particular concern for public health, considering the importance of this hormone in neurodevelopment and metabolic processes. They might either alter the circulating level of thyroid hormone (TH) or interfere with the cellular response to the hormonal stimulation. In order to assess this later possibility we selected 39 pesticides and combined several in vitro tests. Reporter assays respectively addressed the transactivation capacity of the full-length TH nuclear receptor TRα1, the transactivation capacity of its C-terminal ligand binding domain, or the ability of the hormone to destabilize the interaction between TRα1 and the transcriptional corepressor NcoR. Although some pesticides elicit a cellular response, which sometimes interferes with TH signaling, RNA-seq analysis provided no evidence that they can act as TRα1 agonists or antagonists. Their neurodevelopmental toxicity in mammals cannot be explained by an alteration of the response to TH.

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Pesticides were tested for their capacity to interfere with thyroid hormone receptors

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Three reporter assays were combined to identify possible agonists/antagonists

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The tested pesticides are not major disruptors of thyroid hormone signaling

Investigation of the reactivity indices for the formation of substituted dinitroanilines and correlations to their dockings on α-tubulin of Plasmodium falciparum

The local and global reactivity descriptors of substituted dinitroaniline analogues were investigated using M06-2X/6-31 + G(d,p) method. It was observed that NH₂ (m = 3.53 eV; p = 3.70 eV) substituent conveyed the highest nucleophilic character on the benzene ring system than the other groups under study. For the substrates 4-substituted-1-chloro-2,6-dinitrobenzenes, the condensed to atom electrophilicity ([Formula: see text]) increases in the order COOCH₃ > NO₂ > F > SO₃H > CN > Cl > Br. The para substituted groups with the halogens follow the order of increasing electronegativity, F > Cl > Br. However, the nucleophilicity of the halo substituents of the products increases in the order, F > Br > Cl. Molecular docking simulations using the homology model with the crystallographic structure of zinc-induced bovine tubulin heterodimer (1JFF) as one of the templates reveal that the interactions between the tubulins of Plasmodium falciparum and dinitroaniline analogues are due to H-bonding. In general, the binding interaction is with the following residues: Met137, ARG64, Lys60, Glu183, Val4, His28, Cys171, Tyr224, Asn206, 228, Ile235, and Leu238. The pK_as of the residue decrease as the ring activating power of the substituents increases from strongly activating to weakly activating groups. There is no evidence of intra or intermolecular H-bonding between Arg64 and Cys171. Electronegativity (χ) gives a better generic description of the dinitroanilines than any other parameters considered. Short-range hydrophobic interaction contributes to reduced binding affinities of the ligands. Graphical abstractReaction of substituted 2,6-dinitro chlorobenzene with diisopropylamine. Orbital interaction between the substrates and diisopropylamine in the formation of the dinitroanilines.

Structure-based design of Trifarotene (CD5789), a potent and selective RARγ agonist for the treatment of acne

Retinoids have a dominant role in topical acne therapy and to date, only RARβ and RARγ dual agonists have reached the market. Given the tissue distribution of RAR isoforms, it was hypothesized that developing RARγ -selective agonists could yield a new generation of topical acne treatments that would increase safety margins while maintaining the robust efficacy of previous drugs. Structural knowledge derived from the X-ray structure of known γ-selective CD437, suggested the design of a novel triaryl series of agonists which was optimized and ultimately led to the discovery of Trifarotene/CD5789.

PPARγ as a Novel Therapeutic Target in Lung Cancer

Lung cancer is the leading cause of cancer-related death, with more than half the patients having advanced-stage disease at the time of initial diagnosis and thus facing a poor prognosis. This dire situation poses a need for new approaches in prevention and treatment. Peroxisome proliferator-activated receptor γ (PPARγ) is a ligand-activated transcription factor belonging to the nuclear hormone receptor superfamily. Its involvement in adipocyte differentiation and glucose and lipid homeostasis is well-recognized, but accumulating evidence now suggests that PPARγ may also function as a tumor suppressor, inhibiting development of primary tumors and metastases in lung cancer and other malignancies. Besides having prodifferentiation, antiproliferative, and proapoptotic effects, PPARγ agonists have been shown to prevent cancer cells from acquiring the migratory and invasive capabilities essential for successful metastasis. Angiogenesis and secretion of certain matrix metalloproteinases and extracellular matrix proteins within the tumor microenvironment are also regulated by PPARγ. This review of the current literature highlights the potential of PPARγ agonists as novel therapeutic modalities in lung cancer, either as monotherapy or in combination with standard cytotoxic chemotherapy.

Orphan nuclear receptors as drug targets for the treatment of prostate and breast cancers

Nuclear receptors (NRs), a family of 48 transcriptional factors, have been studied intensively for their roles in cancer development and progression. The presence of distinctive ligand binding sites capable of interacting with small molecules has made NRs attractive targets for developing cancer therapeutics. In particular, a number of drugs have been developed over the years to target human androgen- and estrogen receptors for the treatment of prostate cancer and breast cancer. In contrast, orphan nuclear receptors (ONRs), which in many cases lack known biological functions or ligands, are still largely under investigated. This review is a summary on ONRs that have been implicated in prostate and breast cancers, specifically retinoic acid-receptor-related orphan receptors (RORs), liver X receptors (LXRs), chicken ovalbumin upstream promoter transcription factors (COUP-TFs), estrogen related receptors (ERRs), nerve growth factor 1B-like receptors, and ‘‘dosage-sensitive sex reversal, adrenal hypoplasia critical region, on chromosome X, gene 1’’ (DAX1). Discovery and development of small molecules that can bind at various functional sites on these ONRs will help determine their biological functions. In addition, these molecules have the potential to act as prototypes for future drug development. Ultimately, the therapeutic value of targeting the ONRs may go well beyond prostate and breast cancers.

Compound activity prediction using models of binding pockets or ligand properties in 3D

Transient interactions of endogenous and exogenous small molecules with flexible binding sites in proteins or macromolecular assemblies play a critical role in all biological processes. Current advances in high-resolution protein structure determination, database development, and docking methodology make it possible to design three-dimensional models for prediction of such interactions with increasing accuracy and specificity. Using the data collected in the Pocketome encyclopedia, we here provide an overview of two types of the three-dimensional ligand activity models, pocketbased and ligand property-based, for two important classes of proteins, nuclear and G-protein coupled receptors. For half the targets, the pocket models discriminate actives from property matched decoys with acceptable accuracy (the area under ROC curve, AUC, exceeding 84%) and for about one fifth of the targets with high accuracy (AUC > 95%). The 3D ligand property field models performed better than 95% in half of the cases. The high performance models can already become a basis of activity predictions for new chemicals. Family-wide benchmarking of the models highlights strengths of both approaches and helps identify their inherent bottlenecks and challenges.

NRLiSt BDB, the manually curated nuclear receptors ligands and structures benchmarking database

Nuclear receptors (NRs) constitute an important class of drug targets. We created the most exhaustive NR-focused benchmarking database to date, the NRLiSt BDB (NRs ligands and structures benchmarking database). The 9905 compounds and 339 structures of the NRLiSt BDB are ready for structure-based and ligand-based virtual screening. In the present study, we detail the protocol used to generate the NRLiSt BDB and its features. We also give some examples of the errors that we found in ChEMBL that convinced us to manually review all original papers. Since extensive and manually curated experimental data about NR ligands and structures are provided in the NRLiSt BDB, it should become a powerful tool to assess the performance of virtual screening methods on NRs, to assist the understanding of NR's function and modulation, and to support the discovery of new drugs targeting NRs. NRLiSt BDB is freely available online at http://nrlist.drugdesign.fr .

Discovery of novel ligands for mouse olfactory receptor MOR42-3 using an in silico screening approach and in vitro validation

The ligands for many olfactory receptors remain largely unknown despite successful heterologous expression of these receptors. Understanding the molecular receptive range of olfactory receptors and deciphering the olfactory recognition code are hampered by the huge number of odorants and large number of olfactory receptors, as well as the complexity of their combinatorial coding. Here, we present an in silico screening approach to find additional ligands for a mouse olfactory receptor that allows improved definition of its molecular receptive range. A virtual library of 574 odorants was screened against a mouse olfactory receptor MOR42-3. We selected the top 20 candidate ligands using two different scoring functions. These 40 odorant candidate ligands were then tested in vitro using the Xenopus oocyte heterologous expression system and two-electrode voltage clamp electrophysiology. We experimentally confirmed 22 of these ligands. The candidate ligands were screened for both agonist and antagonist activity. In summary, we validated 19 agonists and 3 antagonists. Two of the newly identified antagonists were of low potency. Several previously known ligands (mono- and dicarboxylic acids) are also confirmed in this study. However, some of the newly identified ligands were structurally dissimilar compounds with various functional groups belonging to aldehydes, phenyls, alkenes, esters and ethers. The high positive predictive value of our in silico approach is promising. We believe that this approach can be used for initial deorphanization of olfactory receptors as well as for future comprehensive studies of molecular receptive range of olfactory receptors.

Discovery of a new class of inhibitors for the protein arginine deiminase type 4 (PAD4) by structure-based virtual screening

BACKGROUND

Rheumatoid arthritis (RA) is an autoimmune disease with unknown etiology. Anticitrullinated protein autoantibody has been documented as a highly specific autoantibody associated with RA. Protein arginine deiminase type 4 (PAD4) is the enzyme responsible for catalyzing the conversion of peptidylarginine into peptidylcitrulline. PAD4 is a new therapeutic target for RA treatment. In order to search for inhibitors of PAD4, structure-based virtual screening was performed using LIDAEUS (Ligand discovery at Edinburgh university). Potential inhibitors were screened experimentally by inhibition assays.

RESULTS

Twenty two of the top-ranked water-soluble compounds were selected for inhibitory screening against PAD4. Three compounds showed significant inhibition of PAD4 and their IC50 values were investigated. The structures of the three compounds show no resemblance with previously discovered PAD4 inhibitors, nor with existing drugs for RA treatment.

CONCLUSION

Three compounds were discovered as potential inhibitors of PAD4 by virtual screening. The compounds are commercially available and can be used as scaffolds to design more potent inhibitors against PAD4.

A Discovery Funnel for Nucleic Acid Binding Drug Candidates

Computational approaches are becoming increasingly popular for the discovery of drug candidates against a target of interest. Proteins have historically been the primary targets of many virtual screening efforts. While in silico screens targeting proteins has proven successful, other classes of targets, in particular DNA, remain largely unexplored using virtual screening methods. With the realization of the functional importance of many non-cannonical DNA structures such as G-quadruplexes, increased efforts are underway to discover new small molecules that can bind selectively to DNA structures. Here, we describe efforts to build an integrated in silico and in vitro platform for discovering compounds that may bind to a chosen DNA target. Millions of compounds are initially screened in silico for selective binding to a particular structure and ranked to identify several hundred best hits. An important element of our strategy is the inclusion of an array of possible competing structures in the in silico screen. The best hundred or so hits are validated experimentally for binding to the actual target structure by a high-throughput 96-well thermal denaturation assay to yield the top ten candidates. Finally, these most promising candidates are thoroughly characterized for binding to their DNA target by rigorous biophysical methods, including isothermal titration calorimetry, differential scanning calorimetry, spectroscopy and competition dialysis.This platform was validated using quadruplex DNA as a target and a newly discovered quadruplex binding compound with possible anti-cancer activity was discovered. Some considerations when embarking on virtual screening and in silico experiments are also discussed.

Structural determinants of PERK inhibitor potency and selectivity

The unfolded protein response (UPR) is a coordinated program that promotes cell survival under conditions of endoplasmic reticulum stress and is required in tumor progression as well. To date, no specific small molecule inhibitor targeting this pathway has been identified. Pancreatic endoplasmic reticulum kinase (PERK), one of the UPR transducers, is an eIF2α kinase. Compromising PERK function inhibits tumor growth in mice, suggesting that PERK may be a cancer drug target, but identifying a specific inhibitor of any kinase is challenging. The goal of this study was to identify some pair-wise receptor-ligand atomic contacts that confer selective PERK inhibition. Compounds selectively inhibiting PERK-mediated phosphorylation in vitro were identified using an initial virtual library screen, followed by structure-activity hypothesis testing. The most potent PERK selective inhibitors utilize three specific kinase active site contacts that, when absent from chemically similar compounds, abrogates the inhibition: (i) a strong van der Waals contact with PERK residue Met7, (ii) interactions with the N-terminal portion of the activation loop, and (iii) groups providing electrostatic complementarity to Asp144. Interestingly, the activation loop contact is required for PERK selectivity to emerge. Understanding these structure-activity relationships may accelerate rational PERK inhibitor design.

Nuclear receptor engineering based on novel structure activity relationships revealed by farnesyl pyrophosphate

Nuclear receptors (NRs) comprise the second largest protein family targeted by currently available drugs, acting via specific ligand interactions within the ligand binding domain (LBD). Recently, farnesyl pyrophosphate (FPP) was shown to be a unique promiscuous NR ligand, activating a subset of NR family members and inhibiting wound healing in skin. The current study aimed at visualizing the unique basis of FPP interaction with multiple receptors in order to identify general structure-activity relationships that operate across the NR family. Docking of FPP to the 3D structures of the LBDs of a diverse set of NRs consistently revealed an electrostatic FPP pyrophosphate contact with an NR arginine conserved in the NR family, a hydrophobic farnesyl contact with NR helix-12 and a ligand binding pocket volume between 300 and 430 Å(3) as the minimal requirements for FPP activation of any NR. Lack of any of these structural features appears to render a given NR resistant to FPP activation. We used these structure-activity relationships to rationally design and successfully engineer several mutant human estrogen receptors that retain responsiveness to estradiol but no longer respond to FPP.

Enrichment of ligands with molecular dockings and subsequent characterization for human alcohol dehydrogenase 3

Alcohol dehydrogenase 3 (ADH3) has been assigned a role in nitric oxide homeostasis due to its function as an S-nitrosoglutathione reductase. As altered S-nitrosoglutathione levels are often associated with disease, compounds that modulate ADH3 activity might be of therapeutic interest. We performed a virtual screening with molecular dockings of more than 40,000 compounds into the active site of human ADH3. A novel knowledge-based scoring method was used to rank compounds, and several compounds that were not known to interact with ADH3 were tested in vitro. Two of these showed substrate activity (9-decen-1-ol and dodecyltetraglycol), where calculated binding scoring energies correlated well with the logarithm of the k (cat)/K (m) values for the substrates. Two compounds showed inhibition capacity (deoxycholic acid and doxorubicin), and with these data three different lines for specific inhibitors for ADH3 are suggested: fatty acids, glutathione analogs, and cholic acids.

Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles

Nuclear receptors (NRs) are ligand dependent transcriptional factors and play a key role in reproduction, development, and homeostasis of organism. NRs are potential targets for treatment of cancer and other diseases such as inflammatory diseases, and diabetes. In this study, we present a comprehensive library of pocket conformational ensembles of thirteen human nuclear receptors (NRs), and test the ability of these ensembles to recognize their ligands in virtual screening, as well as predict their binding geometry, functional type, and relative binding affinity. 157 known NR modulators and 66 structures were used as a benchmark. Our pocket ensemble library correctly predicted the ligand binding poses in 94% of the cases. The models were also highly selective for the active ligands in virtual screening, with the areas under the ROC curves ranging from 82 to a remarkable 99%. Using the computationally determined receptor-specific binding energy offsets, we showed that the ensembles can be used for predicting selectivity profiles of NR ligands. Our results evaluate and demonstrate the advantages of using receptor ensembles for compound docking, screening, and profiling.

Discovery of potent thermolysin inhibitors using structure based virtual screening and binding assays

In the present work, 22 compounds of the U.S. NCI compound library (size 273K) were identified as putative thermolysin binders by structure based virtual screening with the ICM software (ICM-VLS). In vitro competitive binding assays confirmed that 12 were thermolysin binders. Thermolysin binding modes of the 12 compounds were studied by docking using ICM and Molegro Virtual Docker (MVD). The most potent inhibitor had an IC(50) value of 6.4 x 10(-8) mM (NSC250686, 1 beta-D-arabinofuranosyl-N(4)-lauroylcytosine). The structure of this compound is quite different from the other 11 compounds. Nine out of the 12 compounds contained a similar chemical skeleton (3-nitrobenzamide derivatives) and have IC(50) values ranging from 697.48 to 0.047 mM. The ICM-VLS score and the activity profiles (pIC(50) values) were compared and found to be somewhat linearly correlated (R(2) = 0.78). Kinetic studies showed that, except for NSC285166 (oxyquinoline), the compounds are competitive thermolysin inhibitors.

Discovery of substituted sulfonamides and thiazolidin-4-one derivatives as agonists of human constitutive androstane receptor

The constitutive androstane receptor (CAR; NR1I3) is a nuclear receptor responsible for the recognition of potentially toxic endo- and exogenous compounds whose elimination from the body is accelerated by the CAR-mediated inducible expression of metabolizing enzymes and transporters. Despite the importance of CAR, few human agonists are known so far. Following a sequential virtual screening procedure using a 3D pharmacophore and molecular docking approach, we identified 17 novel agonists that could activate human CAR in vitro and enhance its association with the nuclear receptor co-activator SRC1. Selected agonists also increased the expression of the human CAR target CYP2B6 mRNA in primary hepatocytes. Composed of substituted sulfonamides and thiazolidin-4-one derivatives, these agonists represent two novel chemotypes capable of human CAR activation, thus broadening the agonist spectrum of CAR.

Design of ligand binding to an engineered protein cavity using virtual screening and thermal up-shift evaluation

Proteins could be used to carry and deliver small compounds. As a tool for designing ligand binding sites in protein cores, a three-step virtual screening method is presented that has been optimised using existing data on T4 lysozyme complexes and tested in a newly engineered cavity in flavodoxin. The method can pinpoint, in large databases, ligands of specific protein cavities. In the first step, physico-chemical filters are used to screen the library and discard a majority of compounds. In the second step, a flexible, fast docking procedure is used to score and select a smaller number of compounds as potential binders. In the third step, a finer method is used to dock promising molecules of the hit list into the protein cavity, and an optimised free energy function allows discarding the few false positives by calculating the affinity of the modelled complexes. To demonstrate the portability of the method, several cavities have been designed and engineered in the flavodoxin from Anabaena PCC 7119, and the W66F/L44A double mutant has been selected as a suitable host protein. The NCI database has then been screened for potential binders, and the binding to the engineered cavity of five promising compounds and three tentative non-binders has been experimentally tested by thermal up-shift assays and spectroscopic titrations. The five tentative binders (some apolar and some polar), unlike the three tentative non-binders, are shown to bind to the host mutant and, importantly, not to bind to the wild type protein. The three-step virtual screening method developed can thus be used to identify ligands of buried protein cavities. We anticipate that the method could also be used, in a reverse manner, to identify natural or engineerable protein cavities for the hosting of ligands of interest.

Computational discovery of novel low micromolar human pregnane X receptor antagonists

Very few antagonists have been identified for the human pregnane X receptor (PXR). These molecules may be of use for modulating the effects of therapeutic drugs, which are potent agonists for this receptor (e.g., some anticancer compounds and macrolide antibiotics), with subsequent effects on transcriptional regulation of xenobiotic metabolism and transporter genes. A recent novel pharmacophore for PXR antagonists was developed using three azoles and consisted of two hydrogen bond acceptor regions and two hydrophobic features. This pharmacophore also suggested an overall small binding site that was identified on the outer surface of the receptor at the AF-2 site and validated by docking studies. Using computational approaches to search libraries of known drugs or commercially available molecules is preferred over random screening. We have now described several new smaller antagonists of PXR discovered with the antagonist pharmacophore with in vitro activity in the low micromolar range [S-p-tolyl 3',5-dimethyl-3,5'-biisoxazole-4'-carbothioate (SPB03255) (IC(50), 6.3 microM) and 4-(3-chlorophenyl)-5-(2,4-dichlorobenzylthio)-4H-1,2,4-triazol-3-ol (SPB00574) (IC(50), 24.8 microM)]. We have also used our computational pharmacophore and docking tools to suggest that most of the known PXR antagonists, such as coumestrol and sulforaphane, could also interact on the outer surface of PXR at the AF-2 domain. The involvement of this domain was also suggested by further site-directed mutagenesis work. We have additionally described an FDA approved prodrug, leflunomide (IC(50), 6.8 microM), that seems to be a PXR antagonist in vitro. These observations are important for predicting whether further molecules may interact with PXR as antagonists in vivo with potential therapeutic applications.

Potential therapeutic applications of thyroid hormone analogs

Thyroid hormone (T3 and T4) has many beneficial effects including enhancing cardiac function, promoting weight loss and reducing serum cholesterol. Excess thyroid hormone is, however, associated with unwanted effects on the heart, bone and skeletal muscle. We therefore need analogs that harness the beneficial effects of thyroid hormone without the untoward effects. Such work is largely based on understanding the cellular mechanisms of thyroid hormone action, specifically the crystal structure of the nuclear receptor proteins. In clinical studies, use of naturally occurring thyroid hormone analogs can suppress TSH levels in patients with thyroid cancer without producing tachycardia. Many thyromimetic compounds have been tested in animal models and shown to increase total body oxygen consumption, and to lower weight and serum cholesterol and triglyceride levels while having minor effects on heart rate. Alternatively, analogs that specifically enhance both systolic and diastolic function are potentially useful in the treatment of chronic congestive heart failure. In addition to analogs that are thyroid hormone receptor agonists, several compounds that are thyroid hormone receptor antagonists have been identified and tested. This Review discusses the potential application of thyroid hormone analogs (both agonists and antagonists) in a variety of human disease states.

Future directions for drug transporter modelling

Since the late 1980s computational methods such as quantitative structure-activity relationship (QSAR) and pharmacophore approaches have become more widely applied to assess interactions between drug-like molecules and transporters, starting with P-glycoprotein (P-gp). Identifying molecules that interact with P-gp and other transporters is important for drug discovery, but it is normally ascertained using laborious in vitro and in vivo studies. Computational QSAR and pharmacophore models can be used to screen commercial databases of molecules rapidly and suggest those likely to bind as substrates or inhibitors for transporters. These predictions can then be readily verified in vitro, thus representing a more efficient route to screening. Recently, the application of this approach has seen the identification of new substrates and inhibitors for several transporters. The successful application of computational models and pharmacophore models in particular to predict transporter binding accurately represents a way to anticipate drug-drug interactions of novel molecules from molecular structure. These models may also see incorporation in future pharmacokinetic-pharmacodynamic models to improve predictions of in vivo drug effects in patients. The implications of early assessment of transporter activity, current advances in QSAR, and other computational methods for future development of these and systems-based approaches will be discussed.

Farnesyl Pyrophosphate Is a Novel Transcriptional Activator for a Subset of Nuclear Hormone Receptors

In silico docking of a chemical library with the ligand-binding domain of thyroid hormone nuclear receptor-beta (TRbeta) suggested that farnesyl pyrophosphate (FPP), a key intermediate in cholesterol synthesis and protein farnesylation, might function as an agonist. Surprisingly, addition of FPP to cells activated TR as well as the classical steroid hormone receptors but not peroxisome proliferative-activating receptors, farnesoid X receptor, liver X receptor, or several orphan nuclear receptors the ligands of which are unknown. FPP enhanced receptor-coactivator binding in vitro and in vivo, and elevation of FPP levels in cells by squalene synthetase or farnesyl transferase inhibitors leads to activation. The FPP effect was blocked by selective receptor antagonists, and in silico docking with 143 nuclear receptor ligand-binding domain structures revealed that FPP only docked with the agonist conformation of those receptors activated by FPP. Our results suggest that certain nuclear receptors maintain a common structural feature that may reflect an action of FPP on an ancient nuclear receptor or that FPP could function as a ligand for one of the many orphan nuclear receptors the ligands of which have not yet been identified. This finding also has potential interesting implications that may, in part, explain the pleotropic effects of statins as well as certain actions of farnesylation inhibitors in cells.

Hot spots in prion protein for pathogenic conversion

Prion proteins are key molecules in transmissible spongiform encephalopathies (TSEs), but the precise mechanism of the conversion from the cellular form (PrP(C)) to the scrapie form (PrP(Sc)) is still unknown. Here we discovered a chemical chaperone to stabilize the PrP(C) conformation and identified the hot spots to stop the pathogenic conversion. We conducted in silico screening to find compounds that fitted into a "pocket" created by residues undergoing the conformational rearrangements between the native and the sparsely populated high-energy states (PrP*) and that directly bind to those residues. Forty-four selected compounds were tested in a TSE-infected cell culture model, among which one, 2-pyrrolidin-1-yl-N-[4-[4-(2-pyrrolidin-1-yl-acetylamino)-benzyl]-phenyl]-acetamide, termed GN8, efficiently reduced PrP(Sc). Subsequently, administration of GN8 was found to prolong the survival of TSE-infected mice. Heteronuclear NMR and computer simulation showed that the specific binding sites are the A-S2 loop (N159) and the region from helix B (V189, T192, and K194) to B-C loop (E196), indicating that the intercalation of these distant regions (hot spots) hampers the pathogenic conversion process. Dynamics-based drug discovery strategy, demonstrated here focusing on the hot spots of PrP(C), will open the way to the development of novel anti-prion drugs.

Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs

Finding good drug leads de novo from large chemical libraries, real or virtual, is not an easy task. High-throughput screening is often plagued by low hit rates and many leads that are toxic or exhibit poor bioavailability. Exploiting the secondary activity of marketed drugs, on the other hand, may help in generating drug leads that can be optimized for the observed side-effect target, while maintaining acceptable bioavailability and toxicity profiles. Here, we describe an efficient computational methodology to discover leads to a protein target from safe marketed drugs. We applied an in silico "drug repurposing" procedure for identification of nonsteroidal antagonists against the human androgen receptor (AR), using multiple predicted models of an antagonist-bound receptor. The library of marketed oral drugs was then docked into the best-performing models, and the 11 selected compounds with the highest docking score were tested in vitro for AR binding and antagonism of dihydrotestosterone-induced AR transactivation. The phenothiazine derivatives acetophenazine, fluphenazine, and periciazine, used clinically as antipsychotic drugs, were identified as weak AR antagonists. This in vitro biological activity correlated well with endocrine side effects observed in individuals taking these medications. Further computational optimization of phenothiazines, combined with in vitro screening, led to the identification of a nonsteroidal antiandrogen with improved AR antagonism and marked reduction in affinity for dopaminergic and serotonergic receptors that are the primary target of phenothiazine antipsychotics.

In silico pharmacology for drug discovery: applications to targets and beyond

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

Orphan nuclear receptors in drug discovery

Orphan nuclear receptors provide a unique resource for uncovering novel regulatory systems that impact human health and also provide drug targets for a variety of human diseases. Ligands of nuclear receptors have been used in several important therapeutic areas, such as breast cancers, skin disorders and diabetes. Orphan nuclear receptors, therefore, represent a tremendous opportunity in understanding and treating human diseases. Here, I highlight recent advances in the use of orphan nuclear receptors and their potential as targets for drug discovery in diabetes, obesity, neurodegenerative diseases and other related disorders.

Critical evaluation of methods to incorporate entropy loss upon binding in high-throughput docking

Proper accounting of the positional/orientational/conformational entropy loss associated with protein-ligand binding is important to obtain reliable predictions of binding affinity. Herein, we critically examine two simplified statistical mechanics-based approaches, namely a constant penalty per rotor method, and a more rigorous method, referred to here as the partition function-based scoring (PFS) method, to account for such entropy losses in high-throughput docking calculations. Our results on the estrogen receptor beta and dihydrofolate reductase proteins demonstrate that, while the constant penalty method over-penalizes molecules for their conformational flexibility, the PFS method behaves in a more "DeltaG-like" manner by penalizing different rotors differently depending on their residual entropy in the bound state. Furthermore, in contrast to no entropic penalty or the constant penalty approximation, the PFS method does not exhibit any bias towards either rigid or flexible molecules in the hit list. Preliminary enrichment studies using a lead-like random molecular database suggest that an accurate representation of the "true" energy landscape of the protein-ligand complex is critical for reliable predictions of relative binding affinities by the PFS method.

Methods for Predicting Human Drug Metabolism

Drug metabolism information is a necessary component of drug discovery and development. The key issues in drug metabolism include identifying: the enzyme(s) involved, the site(s) of metabolism, the resulting metabolite(s), and the rate of metabolism. Methods for predicting human drug metabolism from in vitro and computational methodologies and determining relationships between the structure and metabolic activity of molecules are also critically important for understanding potential drug interactions and toxicity. There are numerous experimental and computational approaches that have been developed in order to predict human metabolism which have their own limitations. It is apparent that few of the computational tools for metabolism prediction alone provide the major integrated functions needed to assist in drug discovery. Similarly the different in vitro methods for human drug metabolism themselves have implicit limitations. The utilization of these methods for pharmaceutical and other applications as well as their integration is discussed as it is likely that hybrid methods will provide the most success.

Application of data mining approaches to drug delivery

Computational approaches play a key role in all areas of the pharmaceutical industry from data mining, experimental and clinical data capture to pharmacoeconomics and adverse events monitoring. They will likely continue to be indispensable assets along with a growing library of software applications. This is primarily due to the increasingly massive amount of biology, chemistry and clinical data, which is now entering the public domain mainly as a result of NIH and commercially funded projects. We are therefore in need of new methods for mining this mountain of data in order to enable new hypothesis generation. The computational approaches include, but are not limited to, database compilation, quantitative structure activity relationships (QSAR), pharmacophores, network visualization models, decision trees, machine learning algorithms and multidimensional data visualization software that could be used to improve drug delivery after mining public and/or proprietary data. We will discuss some areas of unmet needs in the area of data mining for drug delivery that can be addressed with new software tools or databases of relevance to future pharmaceutical projects.

Collection and preparation of molecular databases for virtual screening

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.

Evaluation of binary QSAR models derived from LUDI and MOE scoring functions for structure based virtual screening

In today's world of high-throughput in silico screening, the development of virtual screening methodologies to prioritize small molecules as new chemical entities (NCEs) for synthesis is of current interest. Among several approaches to virtual screening, structure-based virtual screening has been considered the most effective. However the problems associated with the ranking of potential solutions in terms of scoring functions remains one of the major bottlenecks in structure-based virtual screening technology. It has been suggested that scoring functions may be used as filters for distinguishing binders from nonbinders instead of accurately predicting their binding free energies. Subsequently, several improvements have been made in this area, which include the use of multiple rather than single scoring functions and application of either consensus or multivariate statistical methods or both to improve the discrimination between binders and nonbinders. In view of it, the discriminative ability (distinguishing binders from nonbinders) of binary QSAR models derived using LUDI and MOE scoring functions has been compared with the models derived by Jacobbsson et al. on five data sets viz. estrogen receptor alphamimics (ERalpha_mimics), estrogen receptor alphatoxins (ERalpha_toxins), matrix metalloprotease 3 inhibitors (MMP-3), factor Xa inhibitors (fXa), and acetylcholine esterase inhibitors (AChE). The overall analyses reveal that binary QSAR is comparable to the PLS discriminant analysis, rule-based, and Bayesian classification methods used by Jacobsson et al. Further the scoring functions implemented in LUDI and MOE can score a wide range of protein-ligand interactions and are comparable to the scoring functions implemented in ICM and Cscore. Thus the binary QSAR models derived using LUDI and MOE scoring functions may be useful as a preliminary screening layer in a multilayered virtual screening paradigm.

Protein structure similarity clustering and natural product structure as guiding principles for chemical genomics

The majority of all proteins are modularly built from a limited set of approximately 1,000 structural domains. The knowledge of a common protein fold topology in the ligand-sensing cores of protein domains can be exploited for the design of small-molecule libraries in the development of inhibitors and ligands. Thus, a novel strategy of clustering protein domain cores based exclusively on structure similarity considerations (protein structure similarity clustering, PSSC) has been successfully applied to the development of small-molecule inhibitors of acetylcholinesterase and the 11beta-hydroxysteroid dehydrogenases based on the structure of a naturally occurring Cdc25 inhibitor. The efficiency of making use of the scaffolds of natural products as biologically prevalidated starting points for the design of compound libraries is further highlighted by the development of benzopyran-based FXR ligands.

Utility of homology models in the drug discovery process

Advances in bioinformatics and protein modeling algorithms, in addition to the enormous increase in experimental protein structure information, have aided in the generation of databases that comprise homology models of a significant portion of known genomic protein sequences. Currently, 3D structure information can be generated for up to 56% of all known proteins. However, there is considerable controversy concerning the real value of homology models for drug design. This review provides an overview of the latest developments in this area and includes selected examples of successful applications of the homology modeling technique to pharmaceutically relevant questions. In addition, the strengths and limitations of the application of homology models during all phases of the drug discovery process are discussed.

Hammett Analysis of Selective Thyroid Hormone Receptor Modulators Reveals Structural and Electronic Requirements for Hormone Antagonists

Selective thyroid hormone modulators that function as isoform-selective agonists or antagonists of the thyroid hormone receptors (TRs) might be therapeutically useful in diseases associated with aberrant hormone signaling. The most potent thyroid hormone antagonist reported to date is NH-3. To explore the significance of the 5'-p-nitroaryl moiety of NH-3 and understand what chemical features are important to confer antagonism, we sought to expand the structure-activity relationship data for the class of 5'-phenylethynyl GC-1 derivatives. Herein, we describe an improved synthetic route utilizing palladium-catalyzed chemistry for efficient access to a series of 5'-phenylethynyl compounds with varying size and electronic properties. We prepared and tested sixteen analogues for TR binding and transactivation activity. Substitution at the 5'-position decreased binding affinity, but retained TRbeta-selectivity. In transactivation assays, the analogues displayed a spectrum of agonist, antagonist, and mixed agonist/antagonist activity that correlated with electronic character in a Hammett analysis between sigma substituent value and TR modulation. Analogues NH-5, NH-7, NH-9, NH-11, and NH-23 displayed full antagonist activity with reduced potency compared to NH-3, indicating the nitro group is not required for antagonism. However, para-substitution with strong electron withdrawing properties on the 5'-aryl extension is important for antagonist activity, and antagonist potency-but not ligand receptor binding-was found to correlate linearly with the sigma values for the electron withdrawing substituents.

A scoring function for docking ligands to low-resolution protein structures

We present a docking method that uses a scoring function for protein-ligand docking that is designed to maximize the docking success rate for low-resolution protein structures. We find that the resulting scoring function parameters are very different depending on whether they were optimized for high- or low-resolution protein structures. We show that this docking method can be successfully applied to predict the ligand-binding site of low-resolution structures. For a set of 25 protein-ligand complexes, in 76% of the cases, more than 50% of ligand-contacting residues are correctly predicted (using receptor crystal structures where the binding site is unspecified). Using decoys of the receptor structures having a 4 A RMSD from the native structure, for the same set of complexes, in 72% of the cases, we obtain at least one correctly predicted ligand-contacting residue. Furthermore, using an 81-protein-ligand set described by Jain, in 76 (93.8%) cases, the algorithm correctly predicts more than 50% of the ligand-contacting residues when native protein structures are used. Using 3 A RMSD from native decoys, in all but two cases (97.5%), the algorithm predicts at least one ligand-binding residue correctly. Finally, compared to the previously published Dolores method, for 298 protein-ligand pairs, the number of cases in which at least half of the specific contacts are correctly predicted is more than four times greater.

Ligand-mediated conformational changes of the VDR are required for gene transactivation

The central element of the molecular switch of nuclear 1alpha,25-dihydroxyvitamin D(3) (1alpha,25(OH)(2)D(3)) signaling is the ligand-binding domain (LBD) of the Vitamin D receptor (VDR), which can be stabilized by 1alpha,25(OH)(2)D(3) or its analogues in to agonistic, antagonistic or inverse agonistic conformations. The positioning of helix 12 of the LBD is of most critical importance for these conformations, because it determines the distance between the charge clamp amino acids K246 and E420 that are essential for VDR-coactivator (CoA) interaction. Most VDR ligands have been identified as agonists and only a few (e.g., ZK168281 and TEI-9647) as pure or partial antagonists. Antagonists induce corepressor (CoR) dissociation from the VDR but prevent completely or partially CoA interaction and thus transactivation. Gemini is a 1alpha,25(OH)(2)D(3) analogue with two identical side chains that despite its significantly increased volume binds to the VDR and acts under most conditions as an agonist. Interestingly, supramolar CoR concentrations shift Gemini from an agonist to an inverse agonist, which actively recruits CoR to the VDR and thus mediates repression of 1alpha,25(OH)(2)D(3) target genes. Gemini is the first described (conditional) inverse agonist to an endocrine nuclear receptor (NR) and may function as a sensor for cell-specific CoA/CoR ratios.

Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: Binding of FK506 to FKBP

Most current docking methods to identify possible ligands and putative binding sites on a receptor molecule assume a rigid receptor structure to allow virtual screening of large ligand databases. However, binding of a ligand can lead to changes in the receptor protein conformation that are sterically necessary to accommodate a bound ligand. An approach is presented that allows relaxation of the protein conformation in precalculated soft flexible degrees of freedom during ligand-receptor docking. For the immunosuppressant FK506-binding protein FKBP, the soft flexible modes are extracted as principal components of motion from a molecular dynamics simulation. A simple penalty function for deformations in the soft flexible mode is used to limit receptor protein deformations during docking that avoids a costly recalculation of the receptor energy by summing over all receptor atom pairs at each step. Rigid docking of the FK506 ligand binding to an unbound FKBP conformation failed to identify a geometry close to experiment as favorable binding site. In contrast, inclusion of the flexible soft modes during systematic docking runs selected a binding geometry close to experiment as lowest energy conformation. This has been achieved at a modest increase of computational cost compared to rigid docking. The approach could provide a computationally efficient way to approximately account for receptor flexibility during docking of large numbers of putative ligands and putative docking geometries.

Improving structure-based virtual screening by multivariate analysis of scoring data

Three different multivariate statistical methods, PLS discriminant analysis, rule-based methods, and Bayesian classification, have been applied to multidimensional scoring data from four different target proteins: estrogen receptor alpha (ERalpha), matrix metalloprotease 3 (MMP3), factor Xa (fXa), and acetylcholine esterase (AChE). The purpose was to build classifiers able to discriminate between active and inactive compounds, given a structure-based virtual screen. Seven different scoring functions were used to generate the scoring matrices. The classifiers were compared to classical consensus scoring and single scoring functions. The classifiers show a superior performance, with rule-based methods being most effective. The precision of correctly predicting an active compound is about 90% for three of the targets and about 25% for acetylcholine esterase. On the basis of these results, a new two-stage approach is suggested for structure-based virtual screening where limited activity information is available.

Current understanding of the function of the nuclear vitamin D receptor in response to its natural and synthetic ligands

The vitamin D receptor (VDR), the high affinity receptor for 1alpha,25-dihydroxyvitamin D3 (1alpha,25(OH)2D3), is a member of the nuclear receptor superfamily. VDR preferentially forms a heterodimeric complex with the retinoid X receptor (RXR) and binds to 1alpha,25(OH)2D3 response elements (VDREs) that consist of two hexameric motifs in a directly repeated (DR) or inverted palindromic (IP) arrangement. DNA-complexed VDR acts as a molecular switch of nuclear 1alpha,25(OH)2D3 signaling by transmitting its activation status to different chromatin loci containing the 1alpha,25(OH)2D3 target genes. Approximately 0.5% of the human genome (about 200 genes) are estimated to be primary targets of 1alpha,25(OH)2D3, but via various mechanisms the VDR appears to interfere in the regulation of even more genes. The molecular basis of the regulatory actions of 1alpha,25(OH)2D3 and its synthetic analogs are ligand-triggered protein-protein interactions of the ligand-binding domain (LBD) of the VDR with coactivator (CoA), corepressor (CoR) and other nuclear proteins. Most analogs have been identified as agonists, a few as antagonists (ZK159222 and TEI-9647) and only Gemini and some of its variations as nonagonists. The positioning of helix 12 of the LBD is of critical importance for the agonistic, antagonistic and nonagonistic conformation of the VDR. In each of the three conformations, the VDR performs different protein-protein interactions, which then result in a characteristic functional profile. The functional profile of some 1alpha,25(OH)2D3 analogs, such as EB1089 and Gemini, can be modulated by protein and DNA interaction partners of the VDR. This provides them with some selectivity for DNA-dependent and -independent signaling pathways and VDRE structures.

Ligand-independent activation of estrogen receptor alpha by XBP-1

The estrogen receptor (ER) is a member of a large superfamily of nuclear receptors that regulates the transcription of estrogen-responsive genes. Several recent studies have demonstrated that XBP-1 mRNA expression is associated with ERalpha status in breast tumors. However, the role of XBP-1 in ERalpha signaling remains to be elucidated. More recently, two forms of XBP-1 were identified due to its unconventional splicing. We refer to the spliced and unspliced forms of XBP-1 as XBP-1S and XBP-1U, respectively. Here, we report that XBP-1S and XBP-1U enhanced ERalpha-dependent transcriptional activity in a ligand-independent manner. XBP-1S had stronger activity than XBP-1U. The maximal effects of XBP-1S and XBP-1U on ERalpha transactivation were observed when they were co-expressed with full-length ERalpha. SRC-1, the p160 steroid receptor coactivator family member, synergized with XBP-1S or XBP-1U to potentiate ERalpha activity. XBP-1S and XBP-1U bound to the ERalpha both in vitro and in vivo in a ligand-independent fashion. XBP-1S and XBP-1U interacted with the ERalpha region containing the DNA-binding domain. The ERalpha-interacting regions on XBP-1S and XBP-1U have been mapped to two regions, including the N-terminal basic region leucine zipper domain (bZIP) and the C-terminal activation domain. The bZIP-deleted mutants of XBP-1S and XBP-1U completely abolished ERalpha transactivation by XBP-1S and XBP-1U. These findings suggest that XBP-1S and XBP-1U may directly modulate ERalpha signaling in both the absence and presence of estrogen and, therefore, may play important roles in the proliferation of normal and malignant estrogen-regulated tissues.

Analysis and optimization of structure-based virtual screening protocols (1): exploration of ligand conformational sampling techniques

Ligand conformational flexibility has long been recognized as an important issue in virtual screening (VS). To this end, a number of different methodologies have been adapted to tackle the problem. Many of said techniques were originally designed for ligand derived pharmacophore screens, but have subsequently been fashioned for application within structure-based virtual screening (SVS). A popular adaptation is the pre-calculation of diverse ligand conformations for subsequent docking in target active sites. In this paper, we study a number of the software programs currently being used in conformer generation, analyzing their ability to regenerate known ligand binding conformations. The implications of these studies are discussed, from the perspective of VS in general and SVS in particular.

Design and synthesis of potent vitamin D receptor antagonists with A-ring modifications: remarkable effects of 2alpha-methyl introduction on antagonistic activity

Novel A-ring analogues of the vitamin D receptor (VDR) antagonist (3a), ZK-159222, and its 24-epimer (3b) were convergently synthesized. Preparation of the CD-ring portions with the side chains of 3a,b, followed by palladium-catalyzed cross-coupling with the A-ring enyne precursors (15a,b), (3S,4S,5R)- and (3S,4S,5S)-bis[(tert-butyldimethylsilyl)oxy]-4-methyloct-1-en-7-yne, afforded the 2alpha-methyl-introduced analogues (4a,b) and their 3-epimers (5a,b). The biological profiles of the hybrid analogues were assessed in terms of affinity for VDR, and antagonistic activity to inhibit HL-60 cell differentiation induced by the natural hormone, 1alpha,25-dihydroxyvitamin D(3). The analogue 4a showed an approximately fivefold higher antagonistic activity compared with 3a. The 2alpha-methyl introduction into 3a increased the receptor affinity, thereby enhancing VDR antagonism. This approach to design potent antagonists based on hybridization of structural motifs in the A-ring and in the side chain may prove to be valuable.

In vitro antimalarial activity of retinoids and the influence of selective retinoic acid receptor antagonists

Retinol (vitamin A alcohol) may have a beneficial role in the host response to malaria in humans and previously published data have suggested that it has a direct inhibitory effect on the growth of Plasmodium falciparum in vitro. To further investigate the role of retinoids as potential antimalarial agents, we assessed the effect of all-trans-retinoic acid (RA), 9-cis-RA and 13-cis-RA, as well as retinol itself and its ester, retinyl palmitate, on 3H-hypoxanthine uptake by the laboratory-adapted strains of P. falciparum 3D7 and K1. In addition, we examined the influence of three specific RA receptor antagonists, ER 27191, Ro 415253 and AGN 194301, on retinoid-induced growth inhibition of 3D7. All-trans-RA, 9-cis-RA and 13-cis-RA in concentrations ranging from 1 x 10(-4) to 5 x 10(-10) M each had antimalarial activity, but at IC50 values (5.9 x 10(-5) to 7.9 x 10(-5) M) that were less than those of retinol (2.5 x 10(-5) to 3.2 x 10(-5) M). Retinyl palmitate had minimal effect on 3H-hypoxanthine uptake. Each of the three specific antagonists inhibited growth of 3D7 (IC50 range 1.2 x 10(-5) to 3.0 x 10(-5) M) but, in isobolographic analysis, were antagonistic to retinol (dose factor potentiation, DFP 0.46-0.79) and, in the case of Ro 415253, to all-trans-RA (DFP=0.39). Although we did not attempt to quantify losses of retinoids from the system, these data suggest that retinol has greater antimalarial activity than its RA metabolites and especially retinyl palmitate. The specific RA receptor antagonists showed paradoxical antimalarial activity but consistently antagonised the effect of retinol and all-trans-RA in isobolographic experiments. We conclude that RA metabolites may be less suitable than retinol per se as antimalarial agents and that P. falciparum might possess or acquire a RA receptor-like moiety.

Nuclear hormone receptor targeted virtual screening

Virtual library screening (VLS) is emerging as a valuable drug lead discovery tool. ICM-VLS implementation of this technology was evaluated on a benchmark set of nuclear hormone receptors (NRs), an important therapeutic target family. Over 5000 structurally diverse compounds, including 78 known NR ligands, were screened against 18 crystal structures and one computer model of 10 NR ligand binding domains in their active or inactive states. The results confirm the ability of the VLS method to generate highly focused subsets of the input chemical library, enriched 33- to 100-fold for all but one receptor studied. However, receptor flexibility remains to be fully addressed, and the choice of the specific conformation used for screening may determine the success of the exercise. We observe that for a particular ligand VLS can often identify the correct target within the receptor family, although the technology is unable to reliably discriminate between the closely related receptor isoforms. Additionally, our results suggest that VLS may be applied successfully without an experimental structure of the receptor by using a homology model. These data represent a realistic snapshot of the state-of-the-art of NR-targeted VLS and define the recent progress and the remaining limitations of the technology.