Structure-based virtual screening to identify inhibitors against Staphylococcus aureus MurD enzyme

Structural Chemistry - The MurD enzyme of Staphylococcus aureus is an attractive drug target as it is essential and ubiquitous in bacteria but absent in mammalian cells. In the present study, we...     

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.     

Receptor-based virtual ligand screening for the identification of novel CDC25 phosphatase inhibitors

CDC25 phosphatases play critical roles in cell cycle regulation and are attractive targets for anticancer therapies. Several small non-peptide molecules are known to inhibit CDC25, but many of them appear to form a covalent bond with the enzyme or act through oxidation of the thiolate group of the catalytic cysteine. Structure-based virtual ligand screening computations were performed with FRED, Surflex, and LigandFit, a compound collection of over 310,000 druglike molecules and the crystal structure of CDC25B in order to identify novel classes of ligands. In vitro experiments carried out on a selected list of 1500 molecules led to the discovery of 99 compounds able to inhibit CDC25B activity at 100 microM. Further docking computations were applied, allowing us to propose a binding mode for the most potent molecule (IC50 = 13 microM). Our best compounds represent promising new classes of CDC25 inhibitors that also exhibit antiproliferative properties.     

Discovery of novel wee1 inhibitors via structure-based virtual screening and biological evaluation

Wee1 plays a critical role in the arrest of G2/M cell cycle for DNA repair before entering mitosis. Many cancer cells have been identified as overexpression of Wee1. In this research, pharmacophore modeling, molecular docking and molecular dynamics simulation approaches were constructed to identify novel potential Wee1 inhibitors. A compound 8 was found to have a novel skeleton against Wee1 with an IC₅₀ value of 22.32 µM and a K_i value of 13.11 µM. Kinetic assays were employed to evaluate the compound 8 as a competitive inhibitor. Compound 8 was tested against A-549 tumor cell lines with IC₅₀ value of 17.8 µM. To investigate the intermolecular interaction of Wee1 and compound 8, further molecular dynamics simulations were performed. It indicates that the binding mode of compound 8 and reference ligand is similar. The active core scaffold of compound 8 could represent a promising lead compound for studying Wee1 and be used for further structural optimization to design more potent Wee1 inhibitors.     

A fluorescence polarization based screening assay for identification of small molecule inhibitors of the PICK1 PDZ domain

PDZ (PSD-95/Discs-large/ZO-1 homology) domains represent putative targets in several diseases including cancer, stroke, addiction and neuropathic pain. Here we describe the application of a simple and fast screening assay based on fluorescence polarization (FP) to identify inhibitors of the PDZ domain in PICK1 (protein interacting with C kinase 1). We screened 43,380 compounds for their ability to inhibit binding of an Oregon Green labeled C-terminal dopamine transporter peptide (OrG-DAT C13) to purified PICK1 in solution. The assay was highly reliable with excellent screening assay parameters (Z'≈0.7 and Z≈0.6). Out of ~200 compounds that reduced FP to less than 80% of the control wells, six compounds were further characterized. The apparent affinities of the compounds were determined in FP competition binding experiments and ranged from ~5.0 μM to ~193 μM. Binding to the PICK1 PDZ domain was confirmed for five of the compounds (CSC-03, CSC-04, CSC-43, FSC-231 and FSC-240) in a non-fluorescence based assay by their ability to inhibit pull-down of PICK1 by a C-terminal DAT GST fusion protein. CSC-03 displayed the highest apparent affinity (5.0 μM) in the FP assay, and was according to fluorescence resonance energy transfer (FRET) experiments capable of inhibiting the interaction between the C-terminus of the GluR2 subunit of the AMPA-type glutamate receptor and PICK1 in live cells. Additional experiments suggested that CSC-03 most likely is an irreversible inhibitor but with specificity for PICK1 since it did not bind three different PDZ domains of PSD-95. Summarized, our data suggest that FP based screening assays might be a widely applicable tool in the search for small molecule inhibitors of PDZ domain interactions.     

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.

     

Pharmacophore and docking-based virtual screening approach for the design of new dual inhibitors of Janus kinase 1 and Janus kinase 2

Janus kinase 1 and 2, non-receptor protein tyrosine kinases, are implicated in various cancerous diseases. Involvement of these two enzymes in the pathways that stimulate cell proliferation in cancerous conditions makes them potential therapeutic targets for designing new dual-targeted agents for the treatment of cancer. In the present study, two separate pharmacophore models were developed and the best models for JAK1 (AAADH.25) and JAK2 (ADRR.92) were selected on the basis of their external predictive ability. Both models were subjected to a systematic virtual screening (VS) protocol using a PHASE database of 1.5 million molecules. The hits retrieved in VS were investigated for ADME properties to avoid selection of molecules with a poor pharmacokinetic profile. The molecules considered to be within the range of acceptable limits of ADME properties were further employed for docking simulations with JAK1 and JAK2 proteins to explore the final hits that possess structural features of both pharmacophore models as well as display essential interactions with both of them. Thus, the new molecules obtained in this way should show inhibitory activity against JAK1 and JAK2 and may serve as novel therapeutic agents for the treatment of cancerous disease conditions.     

Pharmacophore modelling,virtual screening and molecular docking studies on PLD1 inhibitors

Lipid metabolism plays a significant role in influenza virus replication and subsequent infection. The regulatory mechanism governing lipid metabolism and viral replication is not properly understood to date, but both Phospholipase D (PLD1 and PLD2) activities are stimulated in viral infection. In vitro studies indicate that chemical inhibition of PLD1 delays viral entry and reduction of viral loads. The current study reports a three-dimensional pharmacophore model based on 35 known PLD1 inhibitors. A sub-set of 25 compounds was selected as the training set and the remaining 10 compounds were kept in the test set. One hundred and twelve pharmacophore models were generated; a six-featured pharmacophore model (AADDHR.57) with survival score (2.69) produced a statistically significant three-dimensional quantitative structure–activity relationship model with r² = 0.97 (internal training set), r² = 0.71 (internal test set) and Q² = 0.64. The predictive power of the pharmacophore model was validated with an external test set (r² = 0.73) and a systematic virtual screening work-flow was employed showing an enrichment factor of 23.68 at the top 2% of the dataset (active and decoys). Finally, the model was used for screening of the filtered PubChem database to fetch molecules which can be proposed as potential PLD1 inhibitors for blocking influenza infection.     

Surveying FDA-approved drugs as new potential inhibitors of N-cadherin protein: a virtual screening approach

Since N-cadherin protein plays a remarkable role in cancer metastasis and tumor growth and progression, finding new effective inhibitors of this protein can be of high importance in cancer treatment. Nevertheless, few molecules have been introduced to inhibit N-cadherin protein to date. In this work, in order to find and present potent inhibitors, 3358 FDA-approved small molecules were docked against N-cadherin protein. All complexes with binding energy ??9 to ??8 kcal/mol were selected for protein-ligand interaction analysis. In the following, Tanimoto coefficient (T_c) was calculated for those molecules that established appropriate interactions with N-cadherin in order to compute the similarity score between them. Afterwards, molecular dynamics simulation and free energy calculations were done to estimate the stability and ability of the chosen ligands in complex with the target protein. Finally, seven small molecules among 3358 FDA-approved were suggested as potential inhibitors of N-cadherin protein.

     

Structure-based virtual screening to identify the beta-lactamase CTX-M-9 inhibitors: An in silico effort to overcome antibiotic resistance in E. coli

Recently, the quick spreads of broad-spectrum beta-lactams antibiotic resistance in pathogenic strains of bacteria have become a major global health problem. These new emerging resistances cause ineffectiveness of antibiotics and increasing the severity of diseases and treatment costs. Among different and diverse resistance targets, we chose a class A beta lactamase, CTX-M-9, with the aim of identifying new chemical entities to be used for further rational drug design. Based on this purpose, a set of 5000 molecules from the Zinc database have been screened by docking experiments using AutoDock Vina software. The best ranked compound, with respect of the previously proved inhibitor compound 19, was further tested by molecular dynamics (MD) simulation. Our molecular modeling analysis demonstrates that ZINC33264777 has ideal characteristics a potent beta lactamase CTX-M-9 inhibitor. In the free form of beta lactamase, NMR relaxation studies showed the extensive motions near the active site and in the Ω-loop. However, our molecular dynamics studies revealed that in the compound 1: beta lactamase complex, the flexibility of Ω-loop was restricted.     

Discovery of novel indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors by virtual screening

In this study, a combination of virtual screening methods were utilized to identify novel potential indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors. A series of IDO1 potential inhibitors were identified by a combination of following steps: Lipinski's Rule of Five, Veber rules filter, molecular docking, HipHop pharmacophores, 3D-Quantitative structure activity relationship (3D-QSAR) studies and Pan-assay Interference Compounds (PAINS) filter. Three known categories of IDO1 inhibitors were used to constructed pharmacophores and 3D-QSAR models. Four point pharmacophores (RHDA) of IDO1 inhibitors were generated from the training set. The 3D-QSAR models were obtained using partial least squares (PLS) analyze based on the docking conformation alignment from the training set. The leave-one-out correlation (q²) and non-cross-validated correlation coefficient (r²_pred) of the best CoMFA model were 0.601 and 0.546, and the ones from the best CoMSIA model were 0.506 and 0.541, respectively. Six hits from Specs database were identified and analyzed to confirm their binding modes and key interactions to the amino acid residues in the protein. This work may provide novel backbones for new generation of inhibitors of IDO1.     

Identification of novel inhibitors of mitogen-activated protein kinase phosphatase-1 with structure-based virtual screening

Mitogen-activated protein kinase phosphatase-1 (MKP-1) has proved to be an attractive target for the development of therapeutics for the treatment of cancer. We report the first example for a successful application of the structure-based virtual screening to identify the novel inhibitors of MKP-1. It is shown that the efficiency of virtual screening can be enhanced significantly by the incorporation of a new solvation energy term in the scoring function. The newly found inhibitors have desirable physicochemical properties as a drug candidate and reveal a moderate potency with IC₅₀ values ranging from 20 to 50 μM. Therefore, they deserve a consideration for further development by structure–activity relationship studies to optimize the inhibitory activities. Structural features relevant to the stabilization of the inhibitors in the active site of MKP-1 are discussed in detail.     

Design of new plasmepsin inhibitors: a virtual high throughput screening approach on the EGEE grid

Though different species of the genus Plasmodium may be responsible for malaria, the variant caused by P. falciparum is often very dangerous and even fatal if untreated. Hemoglobin degradation is one of the key metabolic processes for the survival of the Plasmodium parasite in its host. Plasmepsins, a family of aspartic proteases encoded by the Plasmodium genome, play a prominent role in host hemoglobin cleavage. In this paper we demonstrate the use of virtual screening, in particular molecular docking, employed at a very large scale to identify novel inhibitors for plasmepsins II and IV. A large grid infrastructure, the EGEE grid, was used to address the problem of large computation resources required for docking hundreds of thousands of chemical compounds on different plasmepsin targets of P. falciparum. A large compound library of about 1 million chemical compounds was docked on 5 different targets of plasmepsins using two different docking software, namely FlexX and AutoDock. Several strategies were employed to analyze the results of this virtual screening approach including docking scores, ideal binding modes, and interactions to key residues of the protein. Three different classes of structures with thiourea, diphenylurea, and guanidino scaffolds were identified to be promising hits. While the identification of diphenylurea compounds is in accordance with the literature and thus provides a sort of "positive control", the identification of novel compounds with a guanidino scaffold proves that high throughput docking can be effectively used to identify novel potential inhibitors of P. falciparum plasmepsins. Thus, with the work presented here, we do not only demonstrate the relevance of computational grids in drug discovery but also identify several promising small molecules which have the potential to serve as candidate inhibitors for P. falciparum plasmepsins. With the use of the EGEE grid infrastructure for the virtual screening campaign against the malaria causing parasite P. falciparum we have demonstrated that resource sharing on an eScience infrastructure such as EGEE provides a new model for doing collaborative research to fight diseases of the poor.     

New approach to molecular docking and its application to virtual screening of chemical databases

This paper describes the validation of a molecular docking method and its application to virtual database screening. The code flexibly docks ligand molecules into rigid receptor structures using a tabu search methodology driven by an empirically derived function for estimating the binding affinity of a protein-ligand complex. The docking method has been tested on 70 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. The lowest energy geometry produced by the docking protocol is within 2.0 A root mean square of the experimental binding mode for 79% of the complexes. The method has been applied to the problem of virtual database screening to identify known ligands for thrombin, factor Xa, and the estrogen receptor. A database of 10,000 randomly chosen "druglike" molecules has been docked into the three receptor structures. In each case known receptor ligands were included in the study. The results showed good separation between the predicted binding affinities of the known ligand set and the database subset.     

Solid-phase assembly and in situ screening of protein tyrosine phosphatase inhibitors

A highly efficient solid-phase strategy for assembly of small molecule inhibitors against protein tyrosine phosphatase 1B (PTP1B) is described. The method is highlighted by its simplicity and product purity. A 70-member combinatorial library of analogues of a known PTP1B inhibitor has been synthesized, which upon direct in situ screening revealed a potent inhibitor ( Ki = 7.0 microM) against PTP1B.     

Library fingerprints: a novel approach to the screening of virtual libraries

We propose a novel method to prioritize libraries for combinatorial synthesis and high-throughput screening that assesses the viability of a particular library on the basis of the aggregate physical-chemical properties of the compounds using a na?ve Bayesian classifier. This approach prioritizes collections of related compounds according to the aggregate values of their physical-chemical parameters in contrast to single-compound screening. The method is also shown to be useful in screening existing noncombinatorial libraries when the compounds in these libraries have been previously clustered according to their molecular graphs. We show that the method used here is comparable or superior to the single-compound virtual screening of combinatorial libraries and noncombinatorial libraries and is superior to the pairwise Tanimoto similarity searching of a collection of combinatorial libraries.     

Structure-based virtual screening of natural products as potential stearoyl-coenzyme a desaturase 1 (SCD1) inhibitors

Stearyl coenzyme A desaturase enzyme 1 (SCD1) is a key enzyme that catalyzes the conversion of saturated fatty acids (SFA) into monounsaturated fatty acids (MUFA) and plays a vital role in lipid metabolism of tumor cells. SCD1 is overexpressed in a variety of malignant tumors, and its related inhibitors showed significant anti-tumor activity in vitro and in vivo experiments, which is a new target for tumor therapy. The focus of this study is to identify novel SCD1 inhibitors from natural products through computer simulations. First, 176,602 compounds from natural product databases were virtually screened. By molecular dynamics (MD) simulations, the ligand-protein interactions of 5 compounds with high docking manifestation were analyzed accurately. Then, MM-GBSA and MM-PBMA methods were used to verify the results. Finally, ADMET prediction was performed for the 5 compounds. As a result, two natural products with potential inhibition towards SCD1 were identified, which had the excellent docking manifestation, binding mode within SCD1 pocket and stability during molecular dynamics simulation. This study provides a meaningful model for the development and optimization of new inhibitors and anti-tumor drugs targeting SCD1.     

A virtual screening approach for thymidine monophosphate kinase inhibitors as antitubercular agents based on docking and pharmacophore models

Docking and pharmacophore screening tools were used to examine the binding of ligands in the active site of thymidine monophosphate kinase of Mycobacterium tuberculosis. Docking analysis of deoxythymidine monophosphate (dTMP) analogues suggests the role of hydrogen bonding and other weak interactions in enzyme selectivity. Water-mediated hydrogen-bond networks and a halogen-bond interaction seem to stabilize the molecular recognition. A pharmacophore model was developed using 20 dTMP analogues. The pharmacophoric features were complementary to the active site residues involved in the ligand recognition. On the basis of these studies, a composite screening model that combines the features from both the docking analysis and the pharmacophore model was developed. The composite model was validated by screening a database spiked with 47 known inhibitors. The model picked up 42 of these, giving an enrichment factor of 17. The validated model was used to successfully screen an in-house database of about 500,000 compounds. Subsequent screening with other filters gave 186 hit molecules.