Pharmacophore Search Research Articles

An RNA-dependent RNA polymerase inhibitor for tick-borne encephalitis virus

Tick-borne encephalitis virus (TBEV) is a medically important representative of the Flaviviridae family. The TBEV genome encodes a single polyprotein, which is co/post-translationally cleaved into three structural and seven non-structural proteins. Of the non-structural proteins, NS5, contains an RNA-dependent RNA polymerase (RdRp) domain that is highly conserved and is responsible for the genome replication. Screening for potential antivirals was done using a hybrid receptor and ligand-based pharmacophore search likely targeting the RdRp domain. For the identification of pharmacophores, a mixture of small probe molecules and nucleotide triphosphates were used. The ligand/receptor interaction screenings of structures from the ZINC database resulted in five compounds. Zinc 3677 and 7151 exhibited lower cytotoxicity and were tested for their antiviral effect against TBEV in vitro. Zinc 3677 inhibited TBEV at micromolar concentrations. The results indicate that Zinc 3677 represents a good target for structure-activity optimizations leading potentially to a discovery of effective TBEV antivirals.

Rapid Discovery of Aspartyl Protease Inhibitors Using an Anchoring Approach.

Pharmacophore searches that include anchors, fragments contributing above average to receptor binding, combined with one‐step syntheses are a powerful approach for the fast discovery of novel bioactive molecules. Here, we are presenting a pipeline for the rapid and efficient discovery of aspartyl protease inhibitors. First, we hypothesized that hydrazine could be a multi‐valent warhead to interact with the active site Asp carboxylic acids. We incorporated the hydrazine anchor in a multicomponent reaction and created a large virtual library of hydrazine derivatives synthetically accessible in one‐step. Next, we performed anchor‐based pharmacophore screening of the libraries and resynthesized top‐ranked compounds. The inhibitory potency of the molecules was finally assessed by an enzyme activity assay and the binding mode confirmed by several soaked crystal structures supporting the validity of the hypothesis and approach. The herein reported pipeline of tools will be of general value for the rapid generation of receptor binders beyond Asp proteases.

Discovery of direct inhibitor of KRAS oncogenic protein by natural products: a combination of pharmacophore search, molecular docking, and molecular dynamic studies.

Background and purpose:Aberrant signaling by oncogenic RAS proteins occurs in almost all human tumors. One of the promising strategies to overcome such cancers is the inhibition of KRAS protein, a subtype of RAS family involved in cell growth, differentiation, and apoptosis, through preventing its effector, SOS1, from being attached to the protein.Experimntal approach:Herein, a virtual screening process was performed using pharmacophore search, molecular docking, and molecular dynamic simulations. A pharmacophore model was created to indicate essential features for a KRAS inhibitor and used for screening the National Cancer Institution (NCI) database to retrieve similar compounds to the pharmacophore model with more than 70% similarity. Chosen compounds were then docked into KRAS and four compounds were selected based on the highest binding scores. Next, a similarity search was done in the whole PubChem database to increase the number of potential inhibitors. The filtered compounds were docked again into KRAS and three of them were selected for molecular dynamic simulation.Findings / Results:Compounds 1a, 2d, and 3a can inhibit SOS-iKRASG12D interaction due to the higher number of interactions with the protein. Moreover, they achieved the equilibrium faster than the approved inhibitor.Conclusion and implications:Auriculasin, a polyphenol flavonoid, can be considered as a potential inhibitor of SOS1-KRAS interaction. This compound seems to be a stronger anticancer than 9LI, a known inhibitor of KRAS, due to its better docking scores. Moreover, this compound can be an appropriate candidate to be formulated as an oral drug.

Combined Ligand-Based and Structure-Based Virtual Screening Approach for Identification of New Dipeptidyl Peptidase 4 Inhibitors.

Dipeptidyl Peptidase 4 (DPP 4) enzyme cleaves an incretin-based glucoregulatory hormone Glucagon Like Peptide -1 from N-terminal where penultimate amino acid is either alanine or proline. Several DPP 4 inhibitors, "gliptins", are approved for the management of Type 2 Diabetes or are under clinical trial. In the present study, combined pharmacophore and docking-based virtual screening protocol were used for the identification of new hits from the Specs Database, which would inhibit DPP 4. The entire computational studies were performed using the Discovery Studio v. 4.1 software package, Pipeline Pilot v. 9.2 (Accelrys Inc.) and FRED v. 2.2.5 (OpenEye Scientific Software). Common feature pharmacophore model was generated from known DPP 4 inhibitors and validated by Receiver Operating curve analysis and GH-scoring method. Database search of Specs commercial database was performed using validated pharmacophore. Hits obtained from pharmacophore search were further docked into the binding site of DPP 4. Based on the analysis of docked poses of hits, 10 compounds were selected for in- vitro DPP 4 enzyme inhibition assay. Based on docking studies, virtual hits were predicted to form interaction with essential amino acid residues of DPP 4 and have an almost similar binding orientation as that of the reference molecule. Three compounds having Specs database ID- AN-465/42837213, AP-064/42049348 and AN- 465/43369427 were found to inhibit DPP 4 enzyme moderately. The present study demonstrates a successful utilization of in-silico tools in the identification of new DPP 4 inhibitor, which can serve as a starting point for the development of novel DPP 4 inhibitors.

An Improved Protocol for the Virtual Screening Discovery of Novel Histone Deacetylase Inhibitors.

Histone deacetylases (HDACs) are enzymes that play a key role in structural modification and gene expression. The overexpression of HDAC is associated with cancer, and thus inhibiting the enzyme could be an efficient cancer therapy. To discover new HDAC inhibitors (HDACis), we proposed an improved protocol combining a hierarchical pharmacophore search, molecular docking, and molecular dynamic simulations. The test results showed that the improved screening protocol effectively reduced the false-positive rates of drug-like chemicals. Based on the protocol, we obtained 16 hit compounds as potential HDACis from the Life Chemicals database. Enzyme inhibition experiments showed that two of the hit chemical compounds had HDAC-inhibitory effects. In vitro assays showed that Z165155756 could selectively inhibit the proliferation of cancer cells and specifically promoted apoptosis and induced G1/S phase arrest in A2780 cells. It may have potential therapeutic effects in ovarian cancer and is worthy of further investigation.

Design of pyrimidine-based scaffolds as potential anticancer agents for human DHFR: three-dimensional quantitative structure–activity relationship by docking derived grid-independent descriptors

Dihydrofolate reductase (DHFR) is one of the effective and attractive chemotherapeutic targets because of its importance in catalyzing the reduction of dihydrofolate to tetrahydrofolate, which has important function in the action of folate-dependent enzymes. In this study, a new hybrid docking alignment-independent three-dimensional quantitative structure–activity relationship (3D-QSAR) model was prepared and applied in order to predict biological activities in a series of pyrimidine-based scaffold inhibitors against the human DHFR target. Partial least squares (PLS) regression was implemented to correlate genetic algorithm selected-grind descriptors with biological activity. Several internal/external approaches were used to validate the model. Considering the good agreement of all calculated parameters with the acceptance criteria and the meaningful and interpretable results of the QSAR model (i.e., PLS coefficient), we have retrieved new hits using the Pharmit server. The most active compound in the data set was docked and imported to Pharmit server in order to screen the PubChem library through building a structure-based pharmacophore model. We applied some shape filters for protein and ligand as well as Lipinski’s rule of five, in order to filter the results generated by a virtual screening which was performed by pharmacophore search. Based on the virtual screening and molecular docking results, eventually, 44 compounds were retrieved. ADMET filtration was applied to validate the hit compounds. Based on the results obtained from the in silico prediction of ADMET properties, two hits were finally retrieved from the candidate compounds.

BRUSELAS: HPC Generic and Customizable Software Architecture for 3D Ligand-Based Virtual Screening of Large Molecular Databases.

BRUSELAS (balanced rapid and unrestricted server for extensive ligand-aimed screening) is a novel, highly efficient web software architecture for 3D shape and pharmacophore searches in off the cuff libraries. A wide panel of shape and pharmacophore similarity algorithms are combined to avoid unbiased results while yielding consensus scoring functions. To evaluate its reliability, BRUSELAS was tested against other similar servers (e.g., USR-VS, SwissSimilarity, ChemMapper) to search for potential antidiabetic drugs. A web tool is developed for users to customize their tasks and is accessible free of any charge or login at http://bio-hpc.eu/software/Bruselas . Source code is available on request.

Identification of a potential allosteric site of Golgi α-mannosidase II using computer-aided drug design.

Golgi α-mannosidase II (GMII) is a glycoside hydrolase playing a crucial role in the N-glycosylation pathway. In various tumour cell lines, the distribution of N-linked sugars on the cell surface is modified and correlates with the progression of tumour metastasis. GMII therefore is a possible molecular target for anticancer agents. Here, we describe the identification of a non-competitive GMII inhibitor using computer-aided drug design methods including identification of a possible allosteric binding site, pharmacophore search and virtual screening.

De novo drug design, pharmacophore search and molecular docking for inhibitors to treat TB and HIV co-infection

Novel molecules were designed as possible inhibitors of ATP synthase through de novo drug design, but were not drug-like molecules. Hence, ZINC database was searched for drug-like molecules from the common pharmacophore of the designed molecules. A total of 472 hits were obtained, among them, ZINC39552534, ZINC39371747, and ZINC38959526 produced better docking than the standard drug Bedaquiline. The vulnerability of TB and HIV co-infection has necessitated the search for inhibitors effective against both the diseases. Hence, the hits obtained were further screened for possible interaction with HIV reverse transcriptase. ZINC63941671, ZINC05858010, and ZINC05857787 were found better over the standard drug Rilpivirine, but their interaction was least against ATP synthase. Further, ZINC38959526 (lead against ATP synthase) and ZINC05858010 (lead against reverse transcriptase) share some common chemical features and based on this, new hybrid molecules were designed to inhibit both the targets. The possibility of hERG toxicity was also checked to eliminate unwanted cardiotoxicity.

MedPServer: A database for identification of therapeutic targets and novel leads pertaining to natural products.

Natural products have been the source of treatment for various human diseases from time immemorial. Interests in natural product-based scaffolds for the discovery of modern drugs have grown in recent years. However, research on exploring the traditional medicinal systems for modern therapeutics is severely limited due to our incomplete understanding of the therapeutic mechanism of action. One possible solution is to develop computational approaches, based on ligand- and structure-based screening tools, for fast and plausible target identification, leading to elucidation of the therapeutic mechanism. In the present work, we present two methods based on shape-based and pharmacophore search to predict targets of natural products and elucidate their mechanism, and to identify natural product-based leads. These methods were tested on an in-house developed database of medicinal plants that include information from a largely unexplored North-East region of India, known as one of the twelve mega biodiversity regions. However, depending on the choice of the lead molecules, any existing databases can be used for screening. MedPServer is an open access resource available at http://bif.uohyd.ac.in/medserver/.

Discovery of Non-Peptidic Compounds against Chagas Disease Applying Pharmacophore Guided Molecular Modelling Approaches.

Chagas disease is one of the primary causes of heart diseases accounting to 50,000 lives annually and is listed as the neglected tropical disease. Because the currently available therapies have greater toxic effects with higher resistance, there is a dire need to develop new drugs to combat the disease. In this pursuit, the 3D QSAR ligand-pharmacophore (pharm 1) and receptor-based pharmacophore (pharm 2) search was initiated to retrieve the candidate compounds from universal natural compounds database. The validated models were allowed to map the universal natural compounds database. The obtained lead candidates were subjected to molecular docking against cysteine protease (PDB code: 1ME3) employing -Cdocker available on the discovery studio. Subsequently, two Hits have satisfied the selection criteria and were escalated to molecular dynamics simulation and binding free energy calculations. These Hits have demonstrated higher dock scores, displayed interactions with the key residues portraying an ideal binding mode complemented by mapping to all the features of pharm 1 and pharm 2. Additionally, they have rendered stable root mean square deviation (RMSD) and potential energy profiles illuminating their potentiality as the prospective antichagastic agents. The study further demonstrates the mechanism of inhibition by tetrad residues compromising of Gly23 and Asn70 holding the ligand at each ends and the residues Gly65 and Gly160 clamping the Hits at the center. The notable feature is that the Hits lie in close proximity with the residues Glu66 and Leu67, accommodating within the S1, S2 and S3 subsites. Considering these findings, the study suggests that the Hits may be regarded as effective therapeutics against Chagas disease.

Structure-based discovery of selective BRPF1 bromodomain inhibitors

Bromodomain and plant homeodomain (PHD) finger containing protein 1 (BRPF1) is a member of subfamily IV of the human bromodomains. Experimental evidence suggests that BRPF1 is involved in leukemia. In a previous high-throughput docking campaign we identified several chemotypes targeting the BRPF1 bromodomain. Here, pharmacophore searches using the binding modes of two of these chemotypes resulted in two new series of ligands of the BRPF1 bromodomain. The 2,3-dioxo-quinoxaline 21 exhibits a 2-μM affinity for the BRPF1 bromodomain in two different competition binding assays, and more than 100-fold selectivity for BRPF1 against other members of subfamily IV and representatives of other subfamilies. Cellular activity is confirmed by a viability assay in a leukemia cell line. Isothermal titration calorimetry measurements reveal enthalpy-driven binding for compounds 21, 26 (KD = 3 μM), and the 2,4-dimethyl-oxazole derivative 42 (KD = 10 μM). Multiple molecular dynamics simulations and a dozen co-crystal structures at high resolution provide useful information for further optimization of affinity for the BRPF1 bromodomain.

Targeting natural compounds against HER2 kinase domain as potential anticancer drugs applying pharmacophore based molecular modelling approaches

Human epidermal growth factor receptors are implicated in several types of cancers characterized by aberrant signal transduction. This family comprises of EGFR (ErbB1), HER2 (ErbB2, HER2/neu), HER3 (ErbB3), and HER4 (ErbB4). Amongst them, HER2 is associated with breast cancer and is one of the most valuable targets in addressing the breast cancer incidences. For the current investigation, we have performed 3D-QSAR based pharmacophore search for the identification of potential inhibitors against the kinase domain of HER2 protein. Correspondingly, a pharmacophore model, Hypo1, with four features was generated and was validated employing Fischer’s randomization, test set method and the decoy test method. The validated pharmacophore was allowed to screen the colossal natural compounds database (UNPD). Subsequently, the identified 33 compounds were docked into the proteins active site along with the reference after subjecting them to ADMET and Lipinski’s Rule of Five (RoF) employing the CDOCKER implemented on the Discovery Studio. The compounds that have displayed higher dock scores than the reference compound were scrutinized for interactions with the key residues and were escalated to MD simulations. Additionally, molecular dynamics simulations performed by GROMACS have rendered stable root mean square deviation values, radius of gyration and potential energy values. Eventually, based upon the molecular dock score, interactions between the ligands and the active site residues and the stable MD results, the number of Hits was culled to two identifying Hit1 and Hit2 has potential leads against HER2 breast cancers.

Structure-based virtual screening and ADME/T-based prediction analysis for the discovery of novel antifungal CYP51 inhibitors.

With the increasing incidence of pathogenic fungi and drug-resistant fungi in clinic, it has become very important to develop the novel rate-limiting enzyme 14α-demethylase (CYP51) as an antifungal inhibitor. In this study, a method involving structure-based virtual screening was employed. First, a publicly available database was obtained from the Dow Chemical Company, and the database was screened by the designed pharmacophore model of CYP51 inhibitors. Then, the pharmacophore search hits were docked into the CYP51 crystal structure. Finally, sixteen compounds were selected for in vitro antifungal inhibition assay, and most of the compounds showed a certain degree of antifungal activity. In particular, compounds 3, 4, and 9 exhibited significant antifungal and anti-drug resistance activities by blocking the synthesis of ergosterol. The molecular docking and ADME/T properties of the compounds 3, 4, and 9 were further predicted, and the results indicated that they can form hydrophobic and coordination interactions with the active sites of CYP51. At the same time, compounds 4 and 9 showed promising drug-like properties. This study reveals that the compounds can be further optimized and developed as lead compounds.

Identification of new potential HIV-1 reverse transcriptase inhibitors by QSAR modeling and structure-based virtual screening

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have gained a definitive place due to their unique antiviral potency, high specificity and low toxicity in antiretroviral combination therapies which are used to treat HIV. To design more specific HIV-1 inhibitors, 218 diverse non-nucleoside reverse transcriptase inhibitors with their EC50 values were collected. Then, different types of molecular descriptors were calculated. Also, genetic algorithm (GA) and enhanced replacement methods (ERM) were used as the variable selection approaches to choose more relevant features. Based on selected descriptors, a classification support vector machine (SVM) model was constructed to categorize compounds into two groups of active and inactive ones. The most active compound in the set was docked and was used as the input to the Pharmit server to screen the Molport and PubChem libraries by constructing a structure-based pharmacophore model. Shape filters for the protein and ligand as well as Lipinski’s rule of five have been applied to filter out the output of virtual screening from pharmacophore search. Three hundred and thirty-four compounds were finally retrieved from the virtual screening and were fed to the previously constructed SVM model. Among them, the SVM model rendered seven active compounds and they were also analyzed by docking calculations and ADME/Tox parameters.

Pharmadoop: a tool for pharmacophore searching using Hadoop framework

The term pharmacophore is used to define the important features of one or more molecules having the same biological activity. Pharmacophores are selected based on several common features, such as the type of functional groups present, the distance between each atom or group of atoms and the angle between such groups or an individual atom. In this paper, we present the design and implementation of a pharmacophore searching tool, Pharmadoop, using the Hadoop framework. Due to its Hadoop implementation, Pharmadoop is a faster approach as compared to the existing standalone pharmacophore search tools. It utilizes the MapReduce algorithm to support the comparison of millions of conformers in a short time span. We further demonstrated and compared the utility of Pharmadoop on ten distinct chemical datasets of ligand molecules by running common substructure searching job on standalone and multi-system Hadoop platforms. These results were further used to perform pharmacophore searching applications on standalone and multi-node Hadoop distributions. The performance, speed and accuracy of the tool were evaluated through time-scale analysis and receiver operating curve. The Pharmadoop tool can be accessed at http://bioserver.iiita.ac.in/Pharmadoop/ .

AnchorQuery: Rapid online virtual screening for small-molecule protein-protein interaction inhibitors.

AnchorQuery (http://anchorquery.csb.pitt.edu) is a web application for rational structure-based design of protein-protein interaction (PPI) inhibitors. A specialized variant of pharmacophore search is used to rapidly screen libraries consisting of more than 31 million synthesizable compounds biased by design to preferentially target PPIs. Every library compound is accessible through one-step multi-component reaction (MCR) chemistry and contains an anchor motif that is bioisosteric to an amino acid residue. The inclusion of this anchor not only biases the compounds to interact with proteins, it also enables a rapid, sublinear time pharmacophore search algorithm. AnchorQuery provides all the tools necessary for users to perform online interactive virtual screens of millions of compounds, including pharmacophore elucidation and search, and enrichment analysis. Accessibility: AnchorQuery is freely accessible at http://anchorquery.csb.pitt.edu.

Discovery of Small Molecules Targeting the Synergy of Cardiac Transcription Factors GATA4 and NKX2-5

Transcription factors are pivotal regulators of gene transcription, and many diseases are associated with the deregulation of transcriptional networks. In the heart, the transcription factors GATA4 and NKX2-5 are required for cardiogenesis. GATA4 and NKX2-5 interact physically, and the activation of GATA4, in cooperation with NKX2-5, is essential for stretch-induced cardiomyocyte hypertrophy. Here, we report the identification of four small molecule families that either inhibit or enhance the GATA4-NKX2-5 transcriptional synergy. A fragment-based screening, reporter gene assay, and pharmacophore search were utilized for the small molecule screening, identification, and optimization. The compounds modulated the hypertrophic agonist-induced cardiac gene expression. The most potent hit compound, N-[4-(diethylamino)phenyl]-5-methyl-3-phenylisoxazole-4-carboxamide (3, IC50 = 3 μM), exhibited no activity on the protein kinases involved in the regulation of GATA4 phosphorylation. The identified and chemically and biologically characterized active compound, and its derivatives may provide a novel class of small molecules for modulating heart regeneration.

VSPrep: A General KNIME Workflow for the Preparation of Molecules for Virtual Screening.

Over the past decades, virtual screening has proved itself to be a valuable asset to identify new bioactive compounds. The vast majority of commonly used techniques can be described in three steps: pre-processing the dataset i. e. small (ligands) and eventually larger (receptors) molecules, execute the method and finally analyse the results. Hence, the preparation of ligands is a critical step for success of commonly used virtual screening approaches such as protein-ligand docking, similarity or pharmacophore search. We present here a new workflow, VSPrep, for the pre-processing of small molecules; it is based on freely accessible tools for academics and is integrated within the KNIME platform. It can be used to perform several chemoinformatics tasks such as molecular database cleaning, tautomer and stereoisomer enumeration, focused library design and conformer generation. Additionally, graphical reports of the results are provided to the user as a convenient analysis tool.

In Silico Prediction of hPXR Activators Using Structure-Based Pharmacophore Modeling

The activation of pregnane X receptor (PXR), a member of the nuclear receptor superfamily, can mediate potential drug-drug interactions by regulating the expression of several drug-mediated enzymes and transporters, resulting in reduced therapeutic efficacy or increased toxicity by producing reactive metabolites. Therefore, in the early stage of drug development, it is important to predict these risks using an in silico approach. We constructed a human PXR (hPXR) pharmacophore model based on known structural information of compounds that activate PXR. We evaluated the prediction accuracy of the model using data sets generated on 68 original synthetic compounds from the Mitsubishi Tanabe Pharma Corporation and over 2500 drugs from the National Institutes of Health Chemical Genomics Center Pharmaceutical Collection for their ability to activate hPXR. The prediction accuracies of the PXR pharmacophore model were 0.78 and 0.86 for the Mitsubishi Tanabe Pharma Corporation and National Institutes of Health Chemical Genomics Center Pharmaceutical Collection, respectively. The compounds resulting in the smallest root-mean square deviation hit by pharmacophore search were the well-known PXR inducers such as Bosentan. These results suggest that using the in silico approach developed in this study is useful to identify potential hPXR activators and modify the drug design during the early stage of drug development.