Epstein-Barr virus-driven B cell lymphoma mediated by a direct LMP1-TRAF6 complex.

Epstein-Barr virus (EBV) latent membrane protein 1 (LMP1) drives viral B cell transformation and oncogenesis. LMP1's transforming activity depends on its C-terminal activation region 2 (CTAR2), which induces NF-κB and JNK by engaging TNF receptor-associated factor 6 (TRAF6). The mechanism of TRAF6 recruitment to LMP1 and its role in LMP1 signalling remains elusive. Here we demonstrate that TRAF6 interacts directly with a viral TRAF6 binding motif within CTAR2. Functional and NMR studies supported by molecular modeling provide insight into the architecture of the LMP1-TRAF6 complex, which differs from that of CD40-TRAF6. The direct recruitment of TRAF6 to LMP1 is essential for NF-κB activation by CTAR2 and the survival of LMP1-driven lymphoma. Disruption of the LMP1-TRAF6 complex by inhibitory peptides interferes with the survival of EBV-transformed B cells. In this work, we identify LMP1-TRAF6 as a critical virus-host interface and validate this interaction as a potential therapeutic target in EBV-associated cancer.

Reduced expression and activity of patient-derived SHIP1 phosphatase domain mutants.

The characterization of dysregulated proteins in cell signaling pathways is important for the development of therapeutic approaches. The PI3K/AKT/mTOR pathway is frequently upregulated in cancer cells and the SH2-containing inositol 5-phosphatase SHIP1 can act as a negative regulator of the PI3K/AKT pathway. In this study, we investigated different patient-derived mutations within the conserved phosphatase domain of SHIP1. We could demonstrate that 2 out of 7 SHIP1-phosphatase domain mutations (G585K and R673Q) possessed reduced protein expression and reduced enzymatic activity in comparison to SHIP1 wild type (WT) protein and two additional mutations (E452K, R551Q) possessed reduced enzymatic activity at a comparable expression level compared to SHIP1 WT in the cell line H1299. The investigated mutations resulted in protein expression levels that were up to 93% lower than those of the SHIP1 WT for SHIP1 mutant R673Q and the enzymatic activity was below the detection limit of the performed phosphatase assay. Whereas the protein level of the R673Q mutant was reduced in comparison to SHIP1 WT the mRNA level was comparable indicating a post-transcriptional regulation. SHIP1 R673Q was rapidly degraded, with a calculated half-life of l.5 h. In addition, SHIP1 R673Q levels were significantly increased by the treatment with the proteasome inhibitor MG-132 in comparison to the DMSO control. Therefore, SHIP1 was confirmed as the target of enhanced proteasomal degradation. Computational analysis of the wild type and mutant protein structures revealed that the loss of the positively charged arginine residue R673 is associated with the loss of two salt bridges to the negatively charged amino acids D617 and E634 leading to an intramolecular instability of the mutated SHIP1 R673Q protein. Six out of seven SHIP1 mutants significantly affected the PI3K/AKT/mTOR pathway in the three cancer cell lines H1299, Reh and Sem. Four out of seven SHIP1 mutants affected phosphorylation of AKT and its target GSK3ß positively compared to SHIP1 WT, whereas a negative effect on the phosphorylation of S6 was found in five out of seven mutants. In general, SHIP1 mutants impacting signal transduction were either associated with decreased SHIP1 activity or SHIP1 expression or both. Overall, the presented results indicate a regulation of the protein expression and activity of SHIP1 by patient-derived mutations in its phosphatase domain.

In Silico and In Vitro Approach to Assess Direct Allosteric AMPK Activators from Nature.

The 5'-adenosine monophosphate-activated protein kinase (AMPK) is an important metabolic regulator. Its allosteric drug and metabolite binding (ADaM) site was identified as an attractive target for direct AMPK activation and holds promise as a novel mechanism for the treatment of metabolic diseases. With the exception of lusianthridin and salicylic acid, no natural product (NP) is reported so far to directly target the ADaM site. For the streamlined assessment of direct AMPK activators from the pool of NPs, an integrated workflow using in silico and in vitro methods was applied. Virtual screening combining a 3D shape-based approach and docking identified 21 NPs and NP-like molecules that could potentially activate AMPK. The compounds were purchased and tested in an in vitro AMPK α 1 ß 1 γ 1 kinase assay. Two NP-like virtual hits were identified, which, at 30 µM concentration, caused a 1.65-fold (± 0.24) and a 1.58-fold (± 0.17) activation of AMPK, respectively. Intriguingly, using two different evaluation methods, we could not confirm the bioactivity of the supposed AMPK activator lusianthridin, which rebuts earlier reports.

Mechanism of BIP-4 mediated inhibition of InsP3Kinase-A.

Overexpression of the neuronal InsP3kinase-A increases malignancy of different tumor types. Since InsP3kinase-A highly selectively binds Ins(1,4,5)P3, small molecules competing with Ins(1,4,5)P3 provide a promising approach for the therapeutic targeting of InsP3kinase-A. Based on this consideration, we analyzed the binding mechanism of BIP-4 (2-[3,5-dimethyl-1-(4-nitrophenyl)-1H-pyrazol-4-yl]-5, 8-dinitro-1H-benzo[de]isoquinoline-1,3(2H)-dione), a known competitive small-molecule inhibitor of Ins(1,4,5)P3. We tested a total of 80 BIP-4 related compounds in biochemical assays. The results of these experiments revealed that neither the nitrophenyl nor the benzisochinoline group inhibited InsP3kinase-A activity. Moreover, none of the BIP-4 related compounds competed for Ins(1,4,5)P3, demonstrating the high selectivity of BIP-4. To analyze the inhibition mechanism of BIP-4, mutagenesis experiments were performed. The results of these experiments suggest that the nitro groups attached to the benzisochinoline ring compete for binding of Ins(1,4,5)P3 while the nitrophenyl group is associated with amino acids of the ATP-binding pocket. Our results now offer the possibility to optimize BIP-4 to design specific InsP3Kinase-A inhibitors suitable for therapeutic targeting of the enzyme.

AKT1 and PTEN show the highest affinities among phosphoinositide binding proteins for the second messengers PtdIns(3,4,5)P3 and PtdIns(3,4)P2.

The phosphoinositides phosphatidylinositol-3,4,5-trisphosphate [PtdIns(3,4,5)P3] and phosphatidylinositol-3,4-bisphosphate [PtdIns(3,4)P2] function as second messengers and have been implicated in cancerogenesis. The signalling events downstream of PtdIns(3,4,5)P3 and PtdIns(3,4)P2 are mediated through a complex network of phosphoinositide binding effector proteins and phosphatases. In this study, we compared the phosphoinositide effector proteins AKT1, TAPP1, TAPP2, VAV1 and P-REX1 and the phosphoinositide phosphatases PTEN, SHIP1 and INPP4B for their binding affinities to PtdIns(3,4,5)P3 and/or PtdIns(3,4)P2 using Surface Plasmon Resonance. Our results demonstrate that all measured proteins except P-REX1 and VAV1 showed high affinity phosphoinositide binding with KD values in the nM to sub-nM range. Within the effector proteins, AKT1 showed the highest affinity for both PtdIns(3,4,5)P3 and PtdIns(3,4)P2. Of the phosphoinositide phosphatases PTEN displayed the highest affinity towards PtdIns(3,4,5)P3 and PtdIns(3,4)P2. The SHIP1 mutant E452K detected in carcinoma patients had a 100-fold increased affinity to PtdIns(3,4)P2 but not to PtdIns(3,4,5)P3 compared to SHIP1 WT. Distinct mutations in phosphoinositide binding proteins like the patient-derived SHIP1E452K mutant may be involved in the upregulation of PI(3,4)P2 -mediated signalling in tumor cells due to phosphoinositide trapping. Our results add further information to the complex hierarchy of phosphoinositide binding proteins helping to elucidate their functional role in cellular signal transduction.

In Silico Models to Predict the Perturbation of Molecular Initiating Events Related to Thyroid Hormone Homeostasis.

Disturbance of the thyroid hormone homeostasis has been associated with adverse health effects such as goiters and impaired mental development in humans and thyroid tumors in rats. In vitro and in silico methods for predicting the effects of small molecules on thyroid hormone homeostasis are currently being explored as alternatives to animal experiments, but are still in an early stage of development. The aim of this work was the development of a battery of in silico models for a set of targets involved in molecular initiating events of thyroid hormone homeostasis: deiodinases 1, 2, and 3, thyroid peroxidase (TPO), thyroid hormone receptor (TR), sodium/iodide symporter, thyrotropin-releasing hormone receptor, and thyroid-stimulating hormone receptor. The training data sets were compiled from the ToxCast database and related scientific literature. Classical statistical approaches as well as several machine learning methods (including random forest, support vector machine, and neural networks) were explored in combination with three data balancing techniques. The models were trained on molecular descriptors and fingerprints and evaluated on holdout data. Furthermore, multi-task neural networks combining several end points were investigated as a possible way to improve the performance of models for which the experimental data available for model training are limited. Classifiers for TPO and TR performed particularly well, with F1 scores of 0.83 and 0.81 on the holdout data set, respectively. Models for the other studied targets yielded F1 scores of up to 0.77. An in-depth analysis of the reliability of predictions was performed for the most relevant models. All data sets used in this work for model development and validation are available in the Supporting Information.

HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors.

Histone deacetylase 3 (HDAC3) is a potential drug target for treatment of human diseases such as cancer, chronic inflammation, neurodegenerative diseases and diabetes. Machine learning (ML) as an essential cheminformatics approach has been widely used for QSAR modeling. However, none of them has been applied to HDAC3. To this end, we carefully compiled a set of 1098 compounds from the ChEMBL database that have been assayed against HDAC3 and calculated three different sets of molecular features for each compound, i. e. two-dimensional Mordred descriptors, MACCS keys (166 bits) and Morgan2 fingerprints (1024 bits). Five ML classifiers, i. e. k-Nearest Neighbour (KNN), Support Vector Machine (SVM), Random forest (RF), eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) were trained on each feature set and optimized for classification. A total of 15 models were generated and carefully compared, among which the best-performing one was the XGBoost model based on the Morgan2 fingerprints, i. e. XGBoost_morgan2. Evaluated on a well-curated benchmarking set named MUBD-HDAC3, this model achieved a high early ROC enrichment (ROCE0.5 %: 41.02). A further retrospective screening of an annotated chemical library in PubChem demonstrated that the best model could identify 8 novel-scaffold HDAC3 inhibitors while assaying only 1 % of the compounds. To make this model accessible for the scientific community, we developed a python GUI application named HDAC3i-Finder to facilitate prospective screening for HDAC3 inhibitors. The source code of HDAC3i-Finder is available at https://github.com/jwxia2014/HDAC3i-Finder.

N-thiadiazole-4-hydroxy-2-quinolone-3-carboxamides bearing heteroaromatic rings as novel antibacterial agents: Design, synthesis, biological evaluation and target identification.

Due to the occurrence of antibiotic resistance, bacterial infectious diseases have become a serious threat to public health. To overcome antibiotic resistance, novel antibiotics are urgently needed. N-thiadiazole-4-hydroxy-2-quinolone-3-carboxamides are a potential new class of antibacterial agents, as one of its derivatives was identified as an antibacterial agent against S. aureus. However, no potency-directed structural optimization has been performed. In this study, we designed and synthesized 37 derivatives, and evaluated their antibacterial activity against S. aureus ATCC29213, which led to the identification of ten potent antibacterial agents with minimum inhibitory concentration (MIC) values below 1 µg/mL. Next, we performed bacterial growth inhibition assays against a panel of drug-resistant clinical isolates, including methicillin-resistant S. aureus, and cytotoxicity assays with HepG2 and HUVEC cells. One of the tested compounds named 1-ethyl-4-hydroxy-2-oxo-N-(5-(thiazol-2-yl)-1,3,4-thiadiazol-2-yl)-1,2-dihydroquinoline-3-carboxamide (g37) showed 2 to 128-times improvement compared with vancomycin in term of antibacterial potency against the tested strains (MICs: 0.25-1 µg/mL vs. 1-64 µg/mL) and an optimal selective toxicity (HepG2/MRSA, 110.6 to 221.2; HUVEC/MRSA, 77.6-155.2). Further, comprehensive evaluation indicated that g37 did not induce resistance development of MRSA over 20 passages, and it has been confirmed as a bactericidal, metabolically stable, orally active antibacterial agent. More importantly, we have identified the S. aureus DNA gyrase B as its potential target and proposed a potential binding mode by molecular docking. Taken together, the present work reports the most potent derivative of this chemical series (g37) and uncovers its potential target, which lays a solid foundation for further lead optimization facilitated by the structure-based drug design technique.

Validation strategies for target prediction methods.

Computational methods for target prediction, based on molecular similarity and network-based approaches, machine learning, docking and others, have evolved as valuable and powerful tools to aid the challenging task of mode of action identification for bioactive small molecules such as drugs and drug-like compounds. Critical to discerning the scope and limitations of a target prediction method is understanding how its performance was evaluated and reported. Ideally, large-scale prospective experiments are conducted to validate the performance of a model; however, this expensive and time-consuming endeavor is often not feasible. Therefore, to estimate the predictive power of a method, statistical validation based on retrospective knowledge is commonly used. There are multiple statistical validation techniques that vary in rigor. In this review we discuss the validation strategies employed, highlighting the usefulness and constraints of the validation schemes and metrics that are employed to measure and describe performance. We address the limitations of measuring only generalized performance, given that the underlying bioactivity and structural data are biased towards certain small-molecule scaffolds and target families, and suggest additional aspects of performance to consider in order to produce more detailed and realistic estimates of predictive power. Finally, we describe the validation strategies that were employed by some of the most thoroughly validated and accessible target prediction methods.

Analysis of the FLVR motif of SHIP1 and its importance for the protein stability of SH2 containing signaling proteins.

Binding of proteins with SH2 domains to tyrosine-phosphorylated signaling proteins is a key mechanism for transmission of biological signals within the cell. Characterization of dysregulated proteins in cell signaling pathways is important for the development of therapeutic approaches. The AKT pathway is a frequently upregulated pathway in most cancer cells and the SH2-containing inositol 5-phosphatase SHIP1 is a negative regulator of the AKT pathway. In this study we investigated different mutations of the conserved FLVR motif of the SH2 domain and putative phosphorylation sites of SHIP1 which are located in close proximity to its FLVR motif. We demonstrate that patient-derived SHIP1-FLVR motif mutations e.g. F28L, and L29F possess reduced protein expression and increased phospho-AKT-S473 levels in comparison to SHIP1 wildtype. The estimated half-life of SHIP1-F28L protein was reduced from 23.2 h to 0.89 h in TF-1 cells and from 4.7 h to 0.6 h in Jurkat cells. These data indicate that the phenylalanine residue at position 28 of SHIP1 is important for its stability. Replacement of F28 with other aromatic residues like tyrosine and tryptophan preserves protein stability while replacement with non-aromatic amino acids like leucine, isoleucine, valine or alanine severely affects the stability of SHIP1. In consequence, a SHIP1-mutant with an aromatic amino acid at position 28 i.e. F28W can rescue the inhibitory function of wild type SHIP1, whereas SHIP1-mutants with non-aromatic amino acids i.e. F28V do not inhibit cell growth anymore. A detailed structural analysis revealed that F28 forms hydrophobic surface contacts in particular with W5, I83, L97 and P100 which can be maintained by tyrosine and tryptophan residues, but not by non-aromatic residues at position 28. In line with this model of mutation-induced instability of SHIP1-F28L, treatment of cells with proteasomal inhibitor MG132 was able to rescue expression of SHIP1-F28L. In addition, mutation of putative phosphorylation sites S27 and S33 adjacent to the FLVR motif of SHIP1 have an influence on its protein stability. These results further support a functional role of SHIP1 as tumor suppressor protein and indicate a regulation of protein expression of SH2 domain containing proteins via the FLVR motif.

INH14, a Small-Molecule Urea Derivative, Inhibits the IKKα/ß-Dependent TLR Inflammatory Response.

N-(4-Ethylphenyl)-N'-phenylurea (INH14) is a fragment-like compound that inhibits the toll-like receptor 2 (TLR2)-mediated inflammatory activity and other inflammatory pathways (i.e., TLR4, TNF-R and IL-1R). In this study, we determined the molecular target of INH14. Overexpression of proteins that are part of the TLR2 pathway in cells treated with INH14 indicated that the target lay downstream of the complex TAK1/TAB1. Immunoblot assays showed that INH14 decreased IkBα degradation in cells activated by lipopeptide (TLR2 ligand). These data indicated the kinases IKKα and/or IKKß as the targets of INH14, which was confirmed with kinase assays (IC50 IKKα=8.97 µm; IC50 IKKß=3.59 µm). Furthermore, in vivo experiments showed that INH14 decreased TNFα formed after lipopeptide-induced inflammation, and treatment of ovarian cancer cells with INH14 led to a reduction of NF-kB constitutive activity and a reduction in the wound-closing ability of these cells. These results demonstrate that INH14 decreases NF-kB activation through the inhibition of IKKs. Optimization of INH14 could lead to potent inhibitors of IKKs that might be used as antiinflammatory drugs.

Anti-cancer drug development: computational strategies to identify and target proteins involved in cancer metabolism.

Cancer remains a fundamental burden to public health despite substantial efforts aimed at developing effective chemotherapeutics and significant advances in chemotherapeutic regimens. The major challenge in anti-cancer drug design is to selectively target cancer cells with high specificity. Research into treating malignancies by targeting altered metabolism in cancer cells is supported by computational approaches, which can take a leading role in identifying candidate targets for anti-cancer therapy as well as assist in the discovery and optimisation of anti-cancer agents. Natural products appear to have privileged structures for anti-cancer drug development and the bulk of this particularly valuable chemical space still remains to be explored. In this review we aim to provide a comprehensive overview of current strategies for computer-guided anti-cancer drug development. We start with a discussion of state-of-the art bioinformatics methods applied to the identification of novel anti-cancer targets, including machine learning techniques, the Connectivity Map and biological network analysis. This is followed by an extensive survey of molecular modelling and cheminformatics techniques employed to develop agents targeting proteins involved in the glycolytic, lipid, NAD+, mitochondrial (TCA cycle), amino acid and nucleic acid metabolism of cancer cells. A dedicated section highlights the most promising strategies to develop anti-cancer therapeutics from natural products and the role of metabolism and some of the many targets which are under investigation are reviewed. Recent success stories are reported for all the areas covered in this review. We conclude with a brief summary of the most interesting strategies identified and with an outlook on future directions in anti-cancer drug development.

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.

Novel pharmacological chaperones that correct phenylketonuria in mice.

Phenylketonuria (PKU) is caused by inherited phenylalanine-hydroxylase (PAH) deficiency and, in many genotypes, it is associated with protein misfolding. The natural cofactor of PAH, tetrahydrobiopterin (BH(4)), can act as a pharmacological chaperone (PC) that rescues enzyme function. However, BH(4) shows limited efficacy in some PKU genotypes and its chemical synthesis is very costly. Taking an integrated drug discovery approach which has not been applied to this target before, we identified alternative PCs for the treatment of PKU. Shape-focused virtual screening of the National Cancer Institute's chemical library identified 84 candidate molecules with potential to bind to the active site of PAH. An in vitro evaluation of these yielded six compounds that restored the enzymatic activity of the unstable PAHV106A variant and increased its stability in cell-based assays against proteolytic degradation. During a 3-day treatment study, two compounds (benzylhydantoin and 6-amino-5-(benzylamino)-uracil) substantially improved the in vivo Phe oxidation and blood Phe concentrations of PKU mice (Pah(enu1)). Notably, benzylhydantoin was twice as effective as tetrahydrobiopterin. In conclusion, we identified two PCs with high in vivo efficacy that may be further developed into a more effective drug treatment of PKU.

From in silico target prediction to multi-target drug design: current databases, methods and applications.

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.

Development of anti-viral agents using molecular modeling and virtual screening techniques.

Computational chemistry has always played a key role in anti-viral drug development. The challenges and the quickly rising public interest when a virus is becoming a threat has significantly influenced computational drug discovery. The most obvious example is anti-AIDS research, where HIV protease and reverse transcriptase have triggered enormous efforts in developing and improving computational methods. Methods applied to anti-viral research include (i) ligand-based approaches that rely on known active compounds to extrapolate biological activity, such as machine learning techniques or classical QSAR, (ii) structure-based methods that rely on an experimentally determined 3D structure of the targets, such as molecular docking or molecular dynamics, and (iii) universal approaches that can be applied in a structure- or ligand-based way, such as 3D QSAR or 3D pharmacophore elucidation. In this review we summarize these molecular modeling approaches as they were applied to fight anti-viral diseases and highlight their importance for anti-viral research. We discuss the role of computational chemistry in the development of small molecules as agents against HIV integrase, HIV-1 protease, HIV-1 reverse transcriptase, the influenza virus M2 channel protein, influenza virus neuraminidase, the SARS coronavirus main proteinase and spike protein, thymidine kinases of herpes viruses, hepatitis c virus proteins and other flaviviruses as well as human rhinovirus coat protein and proteases, and other picornaviridae. We highlight how computational approaches have helped in discovering anti-viral activities of natural products and give an overview on polypharmacology approaches that help to optimize drugs against several viruses or help to optimize the metabolic profile of and anti-viral drug.

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.

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.

Discovery of nonsteroidal 17beta-hydroxysteroid dehydrogenase 1 inhibitors by pharmacophore-based screening of virtual compound libraries.

17Beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) plays a pivotal role in the local synthesis of the most potent estrogen estradiol. Its expression is a prognostic marker for the outcome of patients with breast cancer and inhibition of 17beta-HSD1 is currently under consideration for breast cancer prevention and treatment. We aimed to identify nonsteroidal 17beta-HSD1 inhibitor scaffolds by virtual screening with pharmacophore models built from crystal structures containing steroidal compounds. The most promising model was validated by comparing predicted and experimentally determined inhibitory activities of several flavonoids. Subsequently, a virtual library of nonsteroidal compounds was screened against the 3D pharmacophore. Analysis of 14 selected compounds yielded four that inhibited the activity of human 17beta-HSD1 (IC 50 below 50 microM). Specificity assessment of identified 17beta-HSD1 inhibitors emphasized the importance of including related short-chain dehydrogenase/reductase (SDR) members to analyze off-target effects. Compound 29 displayed at least 10-fold selectivity over the related SDR enzymes tested.