Discovery of novel human acrosin inhibitors by virtual screening

Human acrosin is an attractive target for the discovery of male contraceptive drugs. For the first time, structure-based drug design was applied to discover structurally diverse human acrosin inhibitors. A parallel virtual screening strategy in combination with pharmacophore-based and docking-based techniques was used to screen the SPECS database. From 16 compounds selected by virtual screening, a total of 10 compounds were found to be human acrosin inhibitors. Compound 2 was found to be the most potent hit (IC₅₀ = 14 μM) and its binding mode was investigated by molecular dynamics simulations. The hit interacted with human acrosin mainly through hydrophobic and hydrogen-bonding interactions, which provided a good starting structure for further optimization studies.     

Virtual screening of B-Raf kinase inhibitors: A combination of pharmacophore modelling,molecular docking, 3D-QSAR model and binding free energy calculation studies

B-Raf kinase has been identified as an important target in recent cancer treatment. In order to discover structurally diverse and novel B-Raf inhibitors (BRIs), a virtual screening of BRIs against ZINC database was performed by using a combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy (ΔG_bind) calculation studies in this work. After the virtual screening, six promising hit compounds were obtained, which were then tested for inhibitory activities of A375 cell lines. In the result, five hit compounds show good biological activities (IC₅₀ < 50 μM). The present method of virtual screening can be applied to find structurally diverse inhibitors, and the obtained five structurally diverse compounds are expected to develop novel BRIs.     

Discovery of novel IDO1 inhibitors via structure-based virtual screening and biological assays

Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme that catalyzes the first and rate-limiting step in catabolism of tryptophan via the kynurenine pathway, which plays a pivotal role in the proliferation and differentiation of T cells. IDO1 has been proven to be an attractive target for many diseases, such as breast cancer, lung cancer, colon cancer, prostate cancer, etc. In this study, docking-based virtual screening and bioassays were conducted to identify novel inhibitors of IDO1. The cellular assay demonstrated that 24 compounds exhibited potent inhibitory activity against IDO1 at micromolar level, including 8 compounds with IC₅₀ values below 10 μM and the most potent one (compound 1) with IC₅₀ of 1.18?±?0.04 μM. Further lead optimization based on similarity searching strategy led to the discovery of compound 28 as an excellent inhibitor with IC₅₀ of 0.27?±?0.02 μM. Then, the structure–activity relationship of compounds 1, 2, 8 and 14 analogues is discussed. The interaction modes of two compounds against IDO1 were further explored through a Python Based Metal Center Parameter Builder (MCPB.py) molecular dynamics simulation, binding free energy calculation and electrostatic potential analysis. The novel IDO1 inhibitors of compound 1 and its analogues could be considered as promising scaffold for further development of IDO1 inhibitors.

     

Comparison of a homology model and the crystallographic structure of human 11β-hydroxysteroid dehydrogenase type 1 (11βHSD1) in a structure-based identification of inhibitors

Summary Human 11β-hydroxysteroid dehydrogenase type 1 (11βHSD1) catalyzes the interconversion of cortisone into active cortisol. 11βHSD1 inhibition is a tempting target for the treatment of a host of human disorders that might benefit from blockade of glucocorticoid action, such as obesity, metabolic syndrome, and diabetes type 2. Here, we report an in silico screening study aimed at identifying new selective inhibitors of human 11βHSD1 enzyme. In the first step, homology modeling was employed to build the 3D structure of 11βHSD1. Further, molecular docking was used to validate the predicted model by showing that it was able to discriminate between known 11βHSD1 inhibitors or substrates and non-inhibitors. The homology model was found to reproduce closely the crystal structure that became publicly available in the final stages of this work. Finally, we carried out structure-based virtual screening experiments on both the homology model and the crystallographic structure with a database of 114’000 natural molecules. Among these, 15 molecules were consistently selected as inhibitors based on both the model and crystal structures of the enzyme, implying a good quality for the homology model. Among these putative 11βHSD1 inhibitors, two were flavonone derivatives that have already been shown to be potent inhibitors of the enzyme.     

Discovery of a New Drug-like Series of OGT Inhibitors by Virtual Screening

O-GlcNAcylation is an essential post-translational modification installed by the enzyme O-β-N-acetyl-d-glucosaminyl transferase (OGT). Modulating this enzyme would be extremely valuable to better understand its role in the development of serious human pathologies, such as diabetes and cancer. However, the limited availability of potent and selective inhibitors hinders the validation of this potential therapeutic target. To explore new chemotypes that target the active site of OGT, we performed virtual screening of a large library of commercially available compounds with drug-like properties. We purchased samples of the most promising virtual hits and used enzyme assays to identify authentic leads. Structure-activity relationships of the best identified OGT inhibitor were explored by generating a small library of derivatives. Our best hit displays a novel uridine mimetic scaffold and inhibited the recombinant enzyme with an IC₅₀ value of 7 µM. The current hit represents an excellent starting point for designing and developing a new set of OGT inhibitors that may prove useful for exploring the biology of OGT.     

Identification of Novel Covalent XPO1 Inhibitors Based on a Hybrid Virtual Screening Strategy

Nuclear export protein 1 (XPO1), a member of the nuclear export protein-p (Karyopherin-P) superfamily, regulates the transport of “cargo” proteins. To facilitate this important process, which is essential for cellular homeostasis, XPO1 must first recognize and bind the cargo proteins. To inhibit this process, small molecule inhibitors have been designed that inhibit XPO1 activity through covalent binding. However, the scaffolds for these inhibitors are very limited. While virtual screening may be used to expand the diversity of the XPO1 inhibitor skeleton, enormous computational resources would be required to accomplish this using traditional screening methods. In the present study, we report the development of a hybrid virtual screening workflow and its application in XPO1 covalent inhibitor screening. After screening, several promising XPO1 covalent molecules were obtained. Of these, compound 8 performed well in both tumor cell proliferation assays and a nuclear export inhibition assay. In addition, molecular dynamics simulations were performed to provide information on the mode of interaction of compound 8 with XPO1. This research has identified a promising new scaffold for XPO1 inhibitors, and it demonstrates an effective and resource-saving workflow for identifying new covalent inhibitors.     

Virtual screening using a conformationally flexible target protein: models for ligand binding to p38α MAPK

We have used virtual screening to develop models for the binding of aryl substituted heterocycles to p38α MAPK. Virtual screening was conducted on a number of p38α MAPK crystal structures using a library of 46 known p38α MAPK inhibitors containing a heterocyclic core substituted by pyridine and fluorophenyl rings (structurally related to SB203580) and a set of decoy compounds. Multiple protonation states and tautomers of active and decoy compounds were considered. Each docking model was evaluated using receiver operating characteristic (ROC) curves and enrichment factors. The two best performing single crystal structures were found to be 1BL7 and 2EWA, with enrichment factors of 14.1 and 13.0 at 2 % of the virtual screen respectively. Ensembles of up to four receptors of similar conformations were generated, generally giving good or very good performances with high ROC AUCs and good enrichment. The 1BL7-2EWA ensemble was able to outperform each of its constituent receptors and gave high enrichment factors of 17.3, 12.0, 8.0 at 2, 5 and 10 % respectively, of the virtual screen. A ROC AUC of 0.94 was obtained for this ensemble. This method may be applied to other proteins where there are a large number of inhibitor classes with different binding site conformations.     

Novel isoquinolone PDK1 inhibitors discovered through fragment-based lead discovery

Phosphoinositide-dependent kinase-1 (PDK1) is a critical enzyme in the PI3K/AKT pathway and to the activation of AGC family protein kinases, including S6K, SGK, and PKC. Dysregulation of this pathway plays a key role in cancer cell growth, survival and tumor angiogenesis. As such, inhibitors of PDK1 offer the promise of a new therapeutic modality for cancer treatment. Fragment based drug screening has recently become a viable entry point for hit identification. In this work, NMR spectroscopy fragment screening of PDK1 afforded novel chemotypes as orthogonal starting points from HTS screening hits. Compounds identified as hits by NMR spectroscopy were tested in a biochemical assay, and fragments with activity in both assays were clustered. The Pfizer compound file was mined via substructure and 2D similarity search, and the chemotypes were prioritized by ligand efficiency (LE), SAR mining, chemical attractiveness, and chemical enablement of promising vectors. From this effort, an isoquinolone fragment hit, 5 (IC₅₀ 870 μM, LE = 0.39), was identified as a novel, ligand efficient inhibitor of PDK1 and a suitable scaffold for further optimization. Initially in the absence of crystallographic data, a fragment growing approach efficiently explored four vectors of the isoquinolone scaffold via parallel synthesis to afford a compound with crystallographic data, 16 (IC₅₀ 41.4 μM, LE = 0.33). Subsequent lead optimization efforts provided 24 (IC₅₀ 1.8 μM, LE = 0.42), with greater than fivefold selectivity against other key pathway kinases.     

Structure-Based Discovery and Bioactivity Evaluation of Novel Aurora-A Kinase Inhibitors as Anticancer Agents via Docking-Based Comparative Intermolecular Contacts Analysis (dbCICA)

Aurora-A kinase plays a central role in mitosis, where aberrant activation contributes to cancer by promoting cell cycle progression, genomic instability, epithelial-mesenchymal transition, and cancer stemness. Aurora-A kinase inhibitors have shown encouraging results in clinical trials but have not gained Food and Drug Administration (FDA) approval. An innovative computational workflow named Docking-based Comparative Intermolecular Contacts Analysis (dbCICA) was applied—aiming to identify novel Aurora-A kinase inhibitors—using seventy-nine reported Aurora-A kinase inhibitors to specify the best possible docking settings needed to fit into the active-site binding pocket of Aurora-A kinase crystal structure, in a process that only potent ligands contact critical binding-site spots, distinct from those occupied by less-active ligands. Optimal dbCICA models were transformed into two corresponding pharmacophores. The optimal one, in capturing active hits and discarding inactive ones, validated by receiver operating characteristic analysis, was used as a virtual in-silico search query for screening new molecules from the National Cancer Institute database. A fluorescence resonance energy transfer (FRET)-based assay was used to assess the activity of captured molecules and five promising Aurora-A kinase inhibitors were identified. The activity was next validated using a cell culture anti-proliferative assay (MTT) and revealed a most potent lead 85(NCI 14040) molecule after 72 h of incubation, scoring IC₅₀ values of 3.5–11.0 μM against PANC1 (pancreas), PC-3 (prostate), T-47D and MDA-MB-231 (breast)cancer cells, and showing favorable safety profiles (27.5 μM IC₅₀ on fibroblasts). Our results provide new clues for further development of Aurora-A kinase inhibitors as anticancer molecules.     

Virtual and experimental high-throughput screening (HTS) in search of novel inosine 5′-monophosphate dehydrogenase II (IMPDH II) inhibitors

IMPDH (Inosine 5??-monophosphate dehydrogenase) catalyzes a rate-limiting step in the de novo biosynthesis of guanine nucleotides. IMPDH inhibition in sensitive cell types (e.g., lymphocytes) blocks proliferation (by blocking RNA and DNA synthesis as a result of decreased cellular levels of guanine nucleotides). This makes it an interesting target for cancer and autoimmune disorders. Currently available IMPDH inhibitors such as mycophenolic acid (MPA, uncompetitive inhibitor) and nucleoside analogs (e.g., ribavirin, competitive inhibitor after intracellular activation by phosphorylation) have unfavorable tolerability profiles which limit their use. Hence, the quest for novel IMPDH inhibitors continues. In the present study, a ligand-based virtual screening using IMPDH inhibitor pharmacophore models was performed on in-house compound collection. A total of 50,000 virtual hits were selected for primary screen using in vitro IMPDH II inhibition up to 10???M. The list of 2,500 hits (with >70?% inhibition) was further subjected to hit confirmation for the determination of IC₅₀ values. The hits obtained were further clustered using maximum common substructure based formalism resulting in 90 classes and 7 singletons. A thorough inspection of these yielded 7 interesting classes in terms of mini-SAR with IC₅₀ values ranging from 0.163???M to little over 25???M. The average ligand efficiency was found to be 0.3 for the best class. The classes thus discovered represent structurally novel chemotypes which can be taken up for further development.     

Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure–activity relationship-based artificial intelligence and molecular docking of hit compounds

Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 and DPP-9. We built a virtual screening workflow using a quantitative structure–activity relationship (QSAR) strategy based on artificial intelligence to allow faster screening of millions of molecules for the DPP-4 target relative to other screening methods. Five regression machine learning algorithms and four classification machine learning algorithms were applied to build virtual screening workflows, with the QSAR model applied using support vector regression (R²_pred 0.78) and the classification QSAR model using the random forest algorithm with 92.2% accuracy. Virtual screening results of > 10 million molecules obtained 2 716 hits compounds with a pIC₅₀ value of > 7.5. Additionally, molecular docking results of several potential hit compounds for DPP-4, DPP-8, and DPP-9 identified CH0002 as showing high inhibitory potential against DPP-4 and low inhibitory potential for DPP-8 and DPP-9 enzymes. These results demonstrated the effectiveness of this technique for identifying DPP-4-inhibitor hit compounds selective for DPP-4 and against DPP-8 and DPP-9 and suggest its potential efficacy for applications to discover hit compounds of other targets.     

Cell-active dual specificity phosphatase inhibitors identified by high-content screening

Phosphorylation of extracellular signal-regulated kinase (Erk) is tightly controlled by dual specificity phosphatases (DSPases), but few inhibitors of Erk dephosphorylation have been identified. Using a high-content, fluorescence-based cellular assay and the National Cancer Institute's 1990 agent Diversity Set, we identified ten compounds (0.5%) that significantly increased phospho-Erk cytonuclear differences in intact cells. Three of the ten positive compounds inhibited the mitogen-activated protein kinase phosphatase-3 (MKP-3/PYST-1) in vitro without affecting VHR or PTP1B phosphatases. The most potent inhibitor of MKP-3 had an IC(50) of <10 microM and inhibited MKP-3 in a novel, fluorescence-based multiparameter chemical complementation assay. These results suggest that the phospho-Erk nuclear accumulation assay may be a useful tool to discover DSPase inhibitors with biological activity.     

Structure-guided fragment-based<Emphasis Type="Italic"> in silico</Emphasis> drug design of dengue protease inhibitors

An in silico fragment-based drug design approach was devised and applied towards the identification of small molecule inhibitors of the dengue virus (DENV) NS2B-NS3 protease. Currently, no DENV protease co-crystal structure with bound inhibitor and fully formed substrate binding site is available. Therefore a homology model of DENV NS2B-NS3 protease was generated employing a multiple template spatial restraints method and used for structure-based design. A library of molecular fragments was derived from the ZINC screening database with help of the retrosynthetic combinatorial analysis procedure (RECAP). 150,000 molecular fragments were docked to the DENV protease homology model and the docking poses were rescored using a target-specific scoring function. High scoring fragments were assembled to small molecule candidates by an implicit linking cascade. The cascade included substructure searching and structural filters focusing on interactions with the S1 and S2 pockets of the protease. The chemical space adjacent to the promising candidates was further explored by neighborhood searching. A total of 23 compounds were tested experimentally and two compounds were discovered to inhibit dengue protease (IC₅₀ = 7.7 μM and 37.9 μM, respectively) and the related West Nile virus protease (IC₅₀ = 6.3 μM and 39.0 μM, respectively). This study demonstrates the successful application of a structure-guided fragment-based in silico drug design approach for dengue protease inhibitors providing straightforward hit generation using a combination of homology modeling, fragment docking, chemical similarity and structural filters.     

Targeting AMPK signalling pathway with natural medicines for atherosclerosis therapy: an integration of in silico screening and in vitro assay

An integration of virtual screening and kinase assay was reported to identify AMPK kinase inhibitors from various natural medicines.The activation of AMP-activated protein kinase (AMPK) signalling pathway plays a central role in the pathologic progression of atherosclerosis (AS). Targeting the AMPK is thus considered as a potential therapeutics to attenuate AS. Here, we report the establishment of a synthetic pipeline that integrates in silico virtual screening and in vitro kinase assay to discover new lead compounds of AMPK inhibitors. The screening is performed against a large-size pool of structurally diverse natural products, from which a number of compounds are inferred as promising candidates, and few of them are further tested in vitro by using a standard kinase assay protocol to determine their inhibitory potency against AMPK. With this scheme we successfully identify five potent AMPK inhibitors with IC₅₀ values at micromolar level. We also examine the structural basis and molecular mechanism of nonbonded interaction network across the modelled complex interface of AMPK kinase domain with a newly identified natural medicine.     

A New Class of Uracil–DNA Glycosylase Inhibitors Active against Human and Vaccinia Virus Enzyme

Uracil–DNA glycosylases are enzymes that excise uracil bases appearing in DNA as a result of cytosine deamination or accidental dUMP incorporation from the dUTP pool. The activity of Family 1 uracil–DNA glycosylase (UNG) activity limits the efficiency of antimetabolite drugs and is essential for virulence in some bacterial and viral infections. Thus, UNG is regarded as a promising target for antitumor, antiviral, antibacterial, and antiprotozoal drugs. Most UNG inhibitors presently developed are based on the uracil base linked to various substituents, yet new pharmacophores are wanted to target a wide range of UNGs. We have conducted virtual screening of a 1,027,767-ligand library and biochemically screened the best hits for the inhibitory activity against human and vaccinia virus UNG enzymes. Although even the best inhibitors had IC₅₀ ≥ 100 μM, they were highly enriched in a common fragment, tetrahydro-2,4,6-trioxopyrimidinylidene (PyO3). In silico, PyO3 preferably docked into the enzyme’s active site, and in kinetic experiments, the inhibition was better consistent with the competitive mechanism. The toxicity of two best inhibitors for human cells was independent of the presence of methotrexate, which is consistent with the hypothesis that dUMP in genomic DNA is less toxic for the cell than strand breaks arising from the massive removal of uracil. We conclude that PyO3 may be a novel pharmacophore with the potential for development into UNG-targeting agents.     

Molecular dynamics simulation study of PTP1B with allosteric inhibitor and its application in receptor based pharmacophore modeling

Allosteric inhibition of protein tyrosine phosphatase 1B (PTP1B), has paved a new path to design specific inhibitors for PTP1B, which is an important drug target for the treatment of type II diabetes and obesity. The PTP1B_1–282-allosteric inhibitor complex crystal structure lacks α7 (287–298) and moreover there is no available 3D structure of PTP1B_1–298 in open form. As the interaction between α7 and α6–α3 helices plays a crucial role in allosteric inhibition, α7 was modeled to the PTP1B_1–282 in open form complexed with an allosteric inhibitor (compound-2) and a 5 ns MD simulation was performed to investigate the relative orientation of the α7–α6–α3 helices. The simulation conformational space was statistically sampled by clustering analyses. This approach was helpful to reveal certain clues on PTP1B allosteric inhibition. The simulation was also utilized in the generation of receptor based pharmacophore models to include the conformational flexibility of the protein-inhibitor complex. Three cluster representative structures of the highly populated clusters were selected for pharmacophore model generation. The three pharmacophore models were subsequently utilized for screening databases to retrieve molecules containing the features that complement the allosteric site. The retrieved hits were filtered based on certain drug-like properties and molecular docking simulations were performed in two different conformations of protein. Thus, performing MD simulation with α7 to investigate the changes at the allosteric site, then developing receptor based pharmacophore models and finally docking the retrieved hits into two distinct conformations will be a reliable methodology in identifying PTP1B allosteric inhibitors. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Risedronate and Methotrexate Are High-Affinity Inhibitors of New Delhi Metallo-β-Lactamase-1 (NDM-1): A Drug Repurposing Approach

Bacteria expressing New Delhi metallo-β-lactamase-1 (NDM-1) can hydrolyze β-lactam antibiotics (penicillins, cephalosporins, and carbapenems) and, thus, mediate multidrug resistance. The worldwide dissemination of NDM-1 poses a serious threat to public health, imposing a huge economic burden in the development of new antibiotics. Thus, there is an urgent need for the identification of novel NDM-1 inhibitors from a pool of already-known drug molecules. Here, we screened a library of FDA-approved drugs to identify novel non-β-lactam ring-containing inhibitors of NDM-1 by applying computational as well as in vitro experimental approaches. Different steps of high-throughput virtual screening, molecular docking, molecular dynamics simulation, and enzyme kinetics were performed to identify risedronate and methotrexate as the inhibitors with the most potential. The molecular mechanics/generalized Born surface area (MM/GBSA) and molecular dynamics (MD) simulations showed that both of the compounds (risedronate and methotrexate) formed a stable complex with NDM-1. Furthermore, analyses of the binding pose revealed that risedronate formed two hydrogen bonds and three electrostatic interactions with the catalytic residues of NDM-1. Similarly, methotrexate formed four hydrogen bonds and one electrostatic interaction with NDM-1’s active site residues. The docking scores of risedronate and methotrexate for NDM-1 were –10.543 kcal mol⁻¹ and −10.189 kcal mol⁻¹, respectively. Steady-state enzyme kinetics in the presence of risedronate and methotrexate showed a decreased catalytic efficiency (i.e., kcat/Km) of NDM-1 on various antibiotics, owing to poor catalytic proficiency and affinity. The results were further validated by determining the MICs of imipenem and meropenem in the presence of risedronate and methotrexate. The IC₅₀ values of the identified inhibitors were in the micromolar range. The findings of this study should be helpful in further characterizing the potential of risedronate and methotrexate to treat bacterial infections.     

Drug Repurposing for Influenza Virus Polymerase Acidic (PA) Endonuclease Inhibitor

Drug repurposing can quickly and effectively identify novel drug repurposing opportunities. The PA endonuclease catalytic site has recently become regarded as an attractive target for the screening of anti-influenza drugs. PA N-terminal (PA_N) inhibitor can inhibit the entire PA endonuclease activity. In this study, we screened the effectivity of PA_N inhibitors from the FDA database through in silico methods and in vitro experiments. PA_N and mutant PA_N-I38T were chosen as virtual screening targets for overcoming drug resistance. Gel-based PA endonuclease analysis determined that the drug lifitegrast can effectively inhibit PA_N and PA_N-I38T, when the IC₅₀ is 32.82 ± 1.34 μM and 26.81 ± 1.2 μM, respectively. Molecular docking calculation showed that lifitegrast interacted with the residues around PA or PA-I38 T’s active site, occupying the catalytic site pocket. Both PA_N/PA_N-I38T and lifitegrast can acquire good equilibrium in 100 ns molecular dynamic simulation. Because of these properties, lifitegrast, which can effectively inhibit PA endonuclease activity, was screened through in silico and in vitro research. This new research will be of significance in developing more effective and selective drugs for anti-influenza therapy.     

Characterization of dipeptidylcarboxypeptidase of <Emphasis Type="Italic">Leishmania donovani</Emphasis>: a molecular model for structure based design of antileishmanials

Leishmania donovani dipeptidylcarboxypeptidsae (LdDCP), an angiotensin converting enzyme (ACE) related metallopeptidase has been identified and characterized as a putative drug target for antileishmanial chemotherapy. The kinetic parameters for LdDCP with substrate, Hip-His-Leu were determined as, Km, 4 mM and Vmax, 1.173 μmole/ml/min. Inhibition studies revealed that known ACE inhibitors (captopril and bradykinin potentiating peptide; BPP1) were weak inhibitors for LdDCP as compared to human testicular ACE (htACE) with Ki values of 35.8 nM and 3.9 μM, respectively. Three dimensional model of LdDCP was generated based on crystal structure of Escherichia coli DCP (EcDCP) by means of comparative modeling and assessed using PROSAII, PROCHECK and WHATIF. Captopril docking with htACE, LdDCP and EcDCP and analysis of molecular electrostatic potentials (MEP) suggested that the active site domain of three enzymes has several minor but potentially important structural differences. These differences could be exploited for designing selective inhibitor of LdDCP thereby antileishmanial compounds either by denovo drug design or virtual screening of small molecule databases.