QSAR modeling,pharmacophore-based virtual screening,and ensemble docking insights into predicting potential epigallocatechin gallate (EGCG) analogs against epidermal growth factor receptor

Epigallocatechin gallate (EGCG) is a major polyphenols of green tea may have the possibility to inhibit epidermal growth factor receptor (EGFR) activity and lead to reduce non-small cell lung cancer (NSCLC) progression. However, EGCG has some toxic features; moreover, there is a lack of explorations into the molecular interaction mechanisms of EGCG and the EGFR. In this examination, integration of quantitative structure–activity relationship (QSAR) modeling, pharmacophore-based virtual screening, and ensemble docking approaches were used to predict potential novel EGCG analogs as effective EGFR inhibitors. QSAR modeling of logP and logS predictions and toxicity endpoint investigation for a set of 82 compounds were shown good predictive ability and robustness from the applicability domain and confusion matrix elucidations. Virtual screening and docking studies revealed that seven high potential EGCG analogs as strong EGFR binders. Molecular interactions interpretations indicated some insights into the structural features of ligands that efficiently interfere with mutation possible residues (Gly⁷¹⁹ and Thr⁷⁹⁰) of the EGFR. The hydrogen bonds, hydrophobic interactions, atomic π-cation interactions and salt bridges of ligands are contributing additional stability to receptor structure, which can lead to blocking the intracellular protein-tyrosine kinase activity, including EGFR associated pathways activation in NSCLC. Therefore, this can characterize as a block-cluster mechanism between EGCG analogs and EGFR complexes. In silico anti-EGFR and anticancer activity predictions suggested that, ligands could act as promising pharmacological, anticancer, and drug-like templates of EGFR towards moderating the NSCLC progressions. These results and provided pinpoints could be beneficial to recognize probable therapeutic targets for NSCLC therapy.     

Investigating molecular dynamics-guided lead optimization of EGFR inhibitors

The epidermal growth factor receptor (EGFR) is part of an extended family of proteins that together control aspects of cell growth and development, and thus a validated target for drug discovery. We explore in this work the suitability of a molecular dynamics-based end-point binding free energy protocol to estimate the relative affinities of a virtual combinatorial library designed around the EGFR model inhibitor 6{1} as a tool to guide chemical synthesis toward the most promising compounds. To investigate the validity of this approach, selected analogs including some with better and worse predicted affinities relative to 6{1} were synthesized, and their biological activity determined. To understand the binding determinants of the different analogs, hydrogen bonding and van der Waals contributions, and water molecule bridging in the EGFR–analog complexes were analyzed. The experimental validation was in good qualitative agreement with our theoretical calculations, while also a 6-dibromophenyl-substituted compound with enhanced inhibitory effect on EGFR compared to the reference ligand was obtained.     

Application of bioisosterism in design of the semicarbazone derivatives as cruzain inhibitors: a theoretical and experimental study

A series of semicarbazone, thiosemicarbazone, and aminoguanidine derivatives were synthesized and tested as antitrypanosomal agents. The theoretical NMR of the compounds was calculated using molecular modeling techniques (density functional theory (DFT) calculations) and confirmed the formation of the compounds. The ability to inhibit cruzain and Trypanosoma cruzi epimastigote replication was evaluated. Cruzain inhibition ranged between 70 and 75% (100 μM), and IC₅₀ values observed in epimastigote forms of T. cruzi ranged from 20 to 140 μM. Furthermore, the compounds did not present cytotoxicity at concentrations up to 50 and 250 μM in MTT tests. Molecular modeling studies were conducted using DFT method (B3LYP functional and the basis set 6-311G(d,p)) to understand the activity of the compounds, corroborating the observed cruzain inhibitory activity. In docking studies, the obtained analogs showed good complementarity with cruzain active site. In addition, docking results are in accordance with the susceptibility of these analogs to nucleophilic attack of the catalytic Cys25. Taken together, this study shows that this class of compounds can be used as a prototype in the identification of new antichagasic drugs.     

New molecular scaffolds for the design of Mycobacterium tuberculosis type II dehydroquinase inhibitors identified using ligand and receptor based virtual screening

Using ligand and receptor based virtual screening approaches we have identified potential virtual screening hits targeting type II dehydroquinase from Mycobacterium tuberculosis, an effective and validated anti-mycobacterial target. Initially, we applied a virtual screening workflow based on a combination of 2D structural fingerprints, 3D pharmacophore and molecular docking to identify compounds that rigidly match specific aspects of ligand bioactive conformation. Subsequently, the resulting compounds were ranked and prioritized using receptor interaction fingerprint based scoring and quantitative structure activity relationship model developed using already known actives. The virtual screening hits prioritized belong to several classes of molecular scaffolds with several available substitution positions that could allow chemical modification to enhance binding affinity. Finally, identified hits may be useful to a medicinal chemist or combinatorial chemist to pick up the new molecular starting points for medicinal chemistry optimization for the design of novel type II dehydroquinase inhibitors.     

CoMFA studies and in vitro evaluation of some 3-substituted benzylthio quinolinium salts as anticryptococcal agents

The 3-dimensional quantitative structure–activity relationship (3D-QSAR) molecular modeling technique or comparative molecular field analysis (CoMFA) has been used to design analogs of the natural product cryptolepine (1). Twenty-three compounds with their in vitro biological activities (IC₅₀ values) against Crytococcus neoformans were used to generate the training set database of compounds for the CoMFA studies. The cross-validated q², noncross-validated r², and partial least squares (PLS) analysis results were used to predict the biological activity of 11 newly designed test set compounds. The best CoMFA model produced a q² of 0.815 and an r² of 0.976 indicating high statistical significance as a predictive model. The steric and electrostatic contributions from the contour map were interpreted from the color-coded contour plots generated from the PLS model and the active structural components for potency against C. neoformans were determined and validated in the test set compounds. The 3-substituted benzylthio quinolinium salts (4) that make up the test set were synthesized and evaluated based on the predicted activity from the CoMFA model and the results produced a good correlation between the predicted and experimental activity (R = 0.82). Thus, CoMFA has served as an effective tool to aid the design of new analogs and in this case, it has aided the identification of compounds equipotent with amphotericin B, the gold standard in antifungal drug design.     

Structure–activity studies of (−)-epigallocatechin gallate derivatives as HCV entry inhibitors

Preventing viral entry into cells is a recognized approach for HIV therapy and has attracted attention for use against the hepatitis C virus (HCV). Recent reports described the activity of (−)-epigallocatechin gallate (EGCG) as an inhibitor of HCV entry with modest potency. EGCG is a polyphenolic natural product with a wide range of biological activity and unfavorable pharmaceutical properties. In an attempt to identify more drug-like EGCG derivatives with improved efficacy as HCV entry inhibitors, we initiated structure–activity investigations using semi-synthetic and synthetic EGCG analogs. The data show that there are multiple regions in the EGCG structure that contribute to activity. The gallate ester portion of the molecule appears to be of particular importance as a 3,4-difluoro analog of EGCG enhanced potency. This derivative and other active compounds were shown not to be cytotoxic in Huh-7 cell culture. These data suggest that more potent, non-cytotoxic EGCG analogs can be prepared in an attempt to identify more drug-like candidates to treat HCV infection by this mechanism.     

Dynamic and Multi-Pharmacophore Modeling for Designing Polo-Box Domain Inhibitors

The polo-like kinase 1 (Plk1) is a critical regulator of cell division that is overexpressed in many types of tumors. Thus, a strategy in the treatment of cancer has been to target the kinase activity (ATPase domain) or substrate-binding domain (Polo-box Domain, PBD) of Plk1. However, only few synthetic small molecules have been identified that target the Plk1-PBD. Here, we have applied an integrative approach that combines pharmacophore modeling, molecular docking, virtual screening, and in vitro testing to discover novel Plk1-PBD inhibitors. Nine Plk1-PBD crystal structures were used to generate structure-based hypotheses. A common pharmacophore model (Hypo1) composed of five chemical features was selected from the 9 structure-based hypotheses and used for virtual screening of a drug-like database consisting of 159,757 compounds to identify novel Plk1-PBD inhibitors. The virtual screening technique revealed 9,327 compounds with a maximum fit value of 3 or greater, which were selected and subjected to molecular docking analyses. This approach yielded 93 compounds that made good interactions with critical residues within the Plk1-PBD active site. The testing of these 93 compounds in vitro for their ability to inhibit the Plk1-PBD, showed that many of these compounds had Plk1-PBD inhibitory activity and that compound Chemistry_28272 was the most potent Plk1-PBD inhibitor. Thus Chemistry_28272 and the other top compounds are novel Plk1-PBD inhibitors and could be used for the development of cancer therapeutics.     

Exploration of Virtual Candidates for Human HMG-CoA Reductase Inhibitors Using Pharmacophore Modeling and Molecular Dynamics Simulations

3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) is a rate-controlling enzyme in the mevalonate pathway which involved in biosynthesis of cholesterol and other isoprenoids. This enzyme catalyzes the conversion of HMG-CoA to mevalonate and is regarded as a drug target to treat hypercholesterolemia. In this study, ten qualitative pharmacophore models were generated based on chemical features in active inhibitors of HMGR. The generated models were validated using a test set. In a validation process, the best hypothesis was selected based on the statistical parameters and used for virtual screening of chemical databases to find novel lead candidates. The screened compounds were sorted by applying drug-like properties. The compounds that satisfied all drug-like properties were used for molecular docking study to identify their binding conformations at active site of HMGR. The final hit compounds were selected based on docking score and binding orientation. The HMGR structures in complex with the hit compounds were subjected to 10 ns molecular dynamics simulations to refine the binding orientation as well as to check the stability of the hits. After simulation, binding modes including hydrogen bonding patterns and molecular interactions with the active site residues were analyzed. In conclusion, four hit compounds with new structural scaffold were suggested as novel and potent HMGR inhibitors.     

Statistical molecular design of a focused salicylidene acylhydrazide library and multivariate QSAR of inhibition of type III secretion in the Gram-negative bacterium Yersinia

A combined application of statistical molecular design (SMD), quantitative structure–activity relationship (QSAR) modeling and prediction of new active compounds was effectively used to develop salicylidene acylhydrazides as inhibitors of type III secretion (T3S) in the Gram-negative pathogen Yersinia pseudotuberculosis. SMD and subsequent synthesis furnished 50 salicylidene acylhydrazides in high purity. Based on data from biological evaluation in T3S linked assays 18 compounds were classified as active and 25 compounds showed a dose-dependent inhibition. The 25 compounds were used to compute two multivariate QSAR models and two multivariate discriminant analysis models were computed from both active and inactive compounds. Three of the models were used to predict 4416 virtual compounds in consensus and eight new compounds were selected as an external test set. Synthesis and biological evaluation of the test set in Y. pseudotuberculosis and the intracellular pathogen Chlamydia trachomatis validated the models. Y. pseudotuberculosis and C. trachomatis cell-based infection models showed that compounds identified in this study are selective and non-toxic inhibitors of T3S dependent virulence.     

Combinatorial design of nonsymmetrical cyclic urea inhibitors of aspartic protease of HIV-1

The aspartic protease (PR) of the human immunodeficiency virus type 1 (HIV-1) is an important target for the design of specific antiviral agents dedicated to treatment of HIV-1 infection. We have employed computer-assisted combinatorial chemistry methods to design a small focused virtual library of nonsymmetrically substituted cyclic urea inhibitors of the PR. Nonsymmetrical compounds with decreased peptidic character were namely found to inhibit the PR with comparable inhibition potencies as their C2-pseudosymmetric counterparts and to possess superior pharmacokinetic properties. To generate the virtual library of fully nonsymmetrical cyclic urea analogs, diverse reagents were selected from databases of available chemicals with characteristics similar to those of the building blocks of known potent PR inhibitors. The X-ray structure of the protease-inhibitor complex PR-XV-638 was used as the receptor model in the structure-based focusing and in silico screening of the virtual library. A target-specific LUDI-type scoring function, parameterized for a QSAR training set of known cyclic urea inhibitors and validated on a set of compounds not included into the training set, was used to predict the inhibition constants (Ki) of the generated analogs toward the HIV-1 PR. The fragments most frequently occurring in the analogs with the highest predicted inhibition potencies (Ki*<10 pM) were then selected to constitute a highly focused library subset containing novel nonsymmetrical cyclic ureas with predicted Ki*s 1 order of magnitude lower than the most potent known cyclic urea inhibitors. ADME properties calculated for the most promising analogs suggested that the cyclic ureas are endowed with a wide range of favorable pharmacokinetic properties, which may favor the discovery of a potent orally administrable antiviral drug.     

Identifications small molecules inhibitor of p53-mortalin complex for cancer drug using virtual screening

Mortalin was over expressed in tumor cells and bind to p53 protein. This interaction was suggested to promote sequestration of p53 in the cytoplasm, thereby inhibiting its nuclear activity. The p53 is a tumor suppressor that is essential for the prevention of cancer development and loss of p53 function is one of the early events in immortalization of human cells. Therefore, abrogation p53-mortalin interaction using small molecule is guaranteed stop cancer cell grow. However study interaction of p53-mortalin, and its inhibition using small molecule is still challenging because specific site of mortalin that bind to p53, vice versa, is still debatable. This study has aims to analyze the p53-binding site of mortalin using molecular docking and to screen drug-like compounds that have potential as inhibitors of p53-mortalin interaction using virtual screening. The result showed that the lowest energy binding of p53-mortalin complex is -31.89 kcal/mol, and p53 protein bind to substrate binding domain of mortalin (THR433; VAL435; LEU436; LEU437; PRO442; ILE558; LYS555). Furthermore, the p53-binding domain of mortalin was used as receptor to screen 9000 drug-like compounds from ZINC database using molecular docking program Auto Dock Vina in PyRx 0.8 (Virtual Screening Tools). Here, we have identified three drug-like compounds that are ZINC01019934, ZINC00624418 and ZINC00664532 adequate to interrupt stability of p53-mortalin complex that warrant for anticancer agent.     

Differential gene expression analysis of RAGE-S100A6 complex for target selection and the design of novel inhibitors for anticancer drug discovery

Receptor for advanced glycation end products (RAGE), a member of the immunoglobulin family, interactions with its ligands trigger downstream signaling and induce an inflammatory response linked to diabetes, inflammation, carcinogenesis, cardiovascular disease, and a variety of other human disorders. The interaction of RAGE and S100A6 has been associated with a variety of malignancies. For the control of RAGE-related illnesses, there is a great demand for more specialized drug options. To identify the most effective target for combating human malignancies associated with RAGE-S100A6 complex, we conducted single and differential gene expression analyses of S100A6 and RAGE, comparing normal and malignant tissues. Further, a structure-based virtual screening was conducted using the ZINC15 database. The chosen compounds were then subjected to a molecular docking investigation on the RAGE active site region, recognized by the various cancer-related RAGE ligands. An optimized RAGE structure was screened against a library of drug-like molecules. The screening results suggested that three promising compounds were presented as the top acceptable drug-like molecules with a high binding affinity at the RAGE V-domain catalytic region. We depicted that these compounds may be potential RAGE inhibitors and could be used to produce a successful medication against human cancer and other RAGE-related diseases based on their various assorted parameters, binding energy, hydrogen bonding, ADMET characteristics, etc. MD simulation on a time scale of 50 ns was used to test the stability of the RAGE-inhibitor complexes. Therefore, targeting RAGE and its ligands using these drug-like molecules may be an effective therapeutic approach.     

Pharmacophore-based virtual screening approach for identification of potent natural modulatory compounds of human Toll-like receptor 7

Abstract

Toll-like receptor 7 (TLR7) is a transmembrane glycoprotein playing very crucial role in the signaling pathways involved in innate immunity and has been demonstrated to be useful in fighting against infectious disease by recognizing viral ssRNA & specific small molecule agonists. In order to find novel human TLR7 (hTLR7) modulators, computational ligand-based pharmacophore modeling approach was used to identify the molecular chemical features required for the modulation of hTLR7 protein. A training set of 20 TLR7 agonists with their known experimental activity was used to create pharmacophore model using 3D-QSAR pharmacophore generation (HypoGen algorithm) module in Discovery Studio. The best developed hypothesis consists of four pharmacophoric features namely, one hydrogen bond donor (HBD), one ring aromatic (RA), and two hydrophobic (HY) character. The developed hypothesis was then validated by different methods such as cost analysis, test set method, and Fischer’s test method for consistency. Hence, this validated model was further employed for screening of natural hit compounds from InterBioScreen Natural product database, consisting of more than 60,000 natural compounds and derivatives. The screened hit compounds were subsequently filtered by Lipinski’s rule of 5, ADME and toxicity parameters and molecular docking studies to remove the false positive rates. Finally, molecular docking analysis led to identification of the (3a′S,6a′R)-3′-(3,4-dihydroxybenzyl)-5′-(3,4-dimethoxyphenethyl)-5-ethyl-3′,3a′-dihydro-2′H-spiro[indoline-3,1′-pyrrolo[3,4-c]pyrrole]-2,4′,6′(5′H,6a′H)-trione (Compound ID: STOCK1N-65837) as potent hTLR7 modulator due to its better docking score and molecular interactions compared to other compounds. The result of virtual screening was further validated using molecular dynamics (MD) simulation analysis. Thus, a 30?ns MD simulation analysis revealed high stability and effective binding of STOCK1N-65837 within the binding site of hTLR7. Therefore, the present study provides confidence for the utility of the selected chemical feature based pharmacophore model to design novel TLR7 modulators with desired biological activity.     

Discovery of potent inhibitors for interleukin-2-inducible T-cell kinase: structure-based virtual screening and molecular dynamics simulation approaches

In our study, a structure-based virtual screening study was conducted to identify potent ITK inhibitors, as ITK is considered to play an important role in the treatment of inflammatory diseases. We developed a structure-based pharmacophore model using the crystal structure (PDB ID: 3MJ2) of ITK complexed with BMS-50944. The most predictive model, SB-Hypo1, consisted of six features: three hydrogen-bond acceptors (HBA), one hydrogen-bond donor (HBD), one ring aromatic (RA), and one hydrophobic (HY). The statistical significance of SB-Hypo1 was validated using wide range of test set molecules and a decoy set. The resulting well-validated model could then be confidently used as a 3D query to screen for drug-like molecules in a database, in order to retrieve new chemical scaffolds that may be potent ITK inhibitors. The hits retrieved from this search were filtered based on the maximum fit value, drug-likeness, and ADMET properties, and the hits that were retained were used in a molecular docking study to find the binding mode and molecular interactions with crucial residues at the active site of the protein. These hits were then fed into a molecular dynamics simulation to study the flexibility of the activation loop of ITK upon ligand binding. This combination of methodologies is a valuable tool for identifying structurally diverse molecules with desired biological activities, and for designing new classes of selective ITK inhibitors.

Identification of potential inhibitors for HCV NS5b of genotype 4a by combining dynamic simulation,protein–ligand interaction fingerprint, 3D pharmacophore,docking and 3D QSAR

Abstract

HCV NS5B polymerase has been one of the most attractive targets for developing new drugs for HCV infection and many drugs were successfully developed, but all of them were designed for targeting Hepatitis C Virus genotype 1 (HCV GT1). Hepatitis C virus genotype 4a (HCV GT4a) dominant in Egypt has paid less attention. Here, we describe our protocol of virtual screening in identification of novel potential potent inhibitors for HCV NS5B polymerase of GT4a using homology modeling, protein–ligand interaction fingerprint (PLIF), docking, pharmacophore, and 3D CoMFA quantitative structure activity relationship (QSAR). Firstly, a high-quality 3D model of HCV NS5B polymerase of GT4a was constructed using crystal structure of HCV NS5B polymerase of GT1 (PDB ID: 3hkw) as a template. Then, both the model and the template were simulated to compare conformational stability. PLIF was generated using five crystal structures of HCV NS5B (PDB ID: 4mia, 4mib, 4mk9, 4mka, and 4mkb), which revealed the most important residues and their interactions with the co-crystalized ligands. After that, a 3D pharmacophore model was developed from the generated PLIF data and then used as a screening filter for 17000328 drug-like zinc database compounds. 900 compounds passed the pharmacophore filter and entered the docking-based virtual screening stage. Finally, a 3D CoMFA QSAR was developed using 42 compounds as a training and 19 compounds as a test set. The 3D CoMFA QSAR was used to design and screen some potential inhibitors, these compounds were further evaluated by the docking stage. The highest ranked five hits from docking result (compounds (p1–p4) and compound q1) were selected for further analysis.

Communicated by Ramaswamy H. Sarma     

In silico design of potent EGFR kinase inhibitors using combinatorial libraries

This paper is an attempt to design 4-anilinoquinazoline compounds having promising anticancer activities against epidermal growth factor (EGFR) kinase inhibition, using virtual combinatorial library approach. Partial least squares method has been applied for the development of a quantitative structure–activity relationship (QSAR) model based on training and test set approaches. The partial least squares model showed some interesting results in terms of internal and external predictability against EGFR kinase inhibition for such type of anilinoquinazoline derivatives. In virtual screening study, out of 4860 compounds in chemical library, 158 compounds were screened and finally, 10 compounds were selected as promising EGFR kinase inhibitors based on their predicted activities from the QSAR model. These derivatives were subjected to molecular docking study to investigate the mode of binding with the EGFR kinase, and the two compounds (ID 3639 and 3399) showing similar type of docking score and binding patterns with that of the existing drug molecules like erlotinib were finally reported.