Computational screening,ensemble docking and pharmacophore analysis of potential gefitinib analogues against epidermal growth factor receptor

The observable mutated isoforms of epidermal growth factor receptor (EGFR) are important considerable therapeutic benchmarks in moderating the non-small cell lung cancer (NSCLC). Recently, quinazoline-based ATP competitive inhibitors have been developed against the EGFR; however, these imply the mutation probabilities, which contribute to the discovery of high probable novel inhibitors for EGFR mutants. Therefore, SAR-based bioactivity analysis, molecular docking and computational toxicogenomics approaches were performed to identify and evaluate new analogs of gefitinib against the ligand-binding domain of the EGFR double-mutated model. From the diverse groups of molecular clustering and molecular screening strategies, top high-binding gefitinib-analogues were identified and studied against EGFR core cavity through three-phase ensemble docking approach. Resulted high possible leads showed good binding orientations than gefitinib (positive control) thus they were subjected to pharmacophore analysis that possesses possible molecular assets to tight binding with EGFR domain. Residues Ser720, Arg841 and Trp880 were observed as novel hot spots and involved in H-bonds, pi-stacking and π-cation interactions that contribute additional electrostatic potency to sustain stability and complexity of protein-ligand complexes, thus they have ability to profoundly adopted by pharmacophoric features. Furthermore, lead molecules have an inhibition percent probability, anticancer potency, toxic impacts, flexible pharmacokinetics, potential gene-chemical interactions towards EGFR were revealed by computational systems biology tools. Our multiple screening strategies confirmed that the druggable sub-pocket was crucial to strong EGFR-ligand binding. The essential pharmacophoric features of ligands provided viewpoints for new inhibitors envisaging, and predicted scaffolds could used as anticancer agents against selected EGFR mutated isoforms.     

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.     

Discovery of potential novel microsomal triglyceride transfer protein inhibitors via virtual screening of pharmacophore modelling and molecular docking

In order to identify potential natural inhibitors against the microsomal triglyceride transfer protein (MTP), HipHop models were generated using 20 known inhibitors from the Binding Database. Using evaluation indicators, the best hypothesis model, Hypo1, was selected and utilised to screen the Traditional Chinese Medicine Database, which resulted in a hit list of 58 drug-like compounds. A homology model of MTP was built by MODELLER and was minimised by CHARMm force field. It was then validated by Ramachandran plot and Verify-3D so as to obtain a stable structure, which was further used to refine the 58 hits using molecular docking studies. Then, five compounds with higher docking scores which satisfied the docking requirements were discovered. Among them, Ginkgetin and Dauricine were most likely to be candidates that exhibition inhibiting effect on MTP. The screening strategy in this study is relatively new to the discovery of MTP inhibitors in medicinal chemistry. Moreover, it is important to note that, lomitapide, an approved MTP inhibitor, fits well with Hypo1 as well as our homology model of MTP, which confirmed the rationality of our studies. The results indicated the applicability of molecular modeling for the discovery of potential natural MTP inhibitors from traditional Chinese herbs.     

Identification of potent virtual leads to design novel PLK1 inhibitors: pharmacophore modelling,virtual screening and molecular docking studies

The aim of this study was to identify novel scaffolds and utilise them in designing potent PLK1 inhibitors. Three-dimensional pharmacophore models on the basis of chemical features were developed for PLK1 on the basis of the known inhibitors. The best pharmacophore model, Hypo 1, which has the highest correlation (0.96), the highest cost difference (75.7494), the lowest total cost and RMSD (75.7494, 0.5458), contains two hydrophobics, one ring aromatic and one hydrogen donor. Hypo 1 was validated by the test set, decoy set and the Fischer's randomisation method. Then it was used for chemical database virtual screening. The hit compounds were filtered by Lipinski's rule of five and absorption, distribution, metabolism, elimination and toxicity properties. Finally, 24 compounds with good estimated activity values were used for docking studies. These results will be used to develop new inhibitors of PLK1 as leads.     

New potential inhibitors of mTOR: a computational investigation integrating molecular docking,virtual screening and molecular dynamics simulation

The mTOR (mammalian or mechanistic Target Of Rapamycin), a complex metabolic pathway that involves multiple steps and regulators, is a major human metabolic pathway responsible for cell growth control in response to multiple factors and that is dysregulated in various types of cancer. The classical inhibition of the mTOR pathway is performed by rapamycin and its analogs (rapalogs). Considering that rapamycin binds to an allosteric site and performs a crucial role in the inhibition of the mTOR complex without causing the deleterious side effects common to ATP-competitive inhibitors, we employ ligand-based drug design strategies, such as virtual screening methodology, computational determination of ADME/Tox properties of selected molecules, and molecular dynamics in order to select molecules with the potential to become non-ATP-competitive inhibitors of the mTOR enzymatic complex. Our findings suggest five novel potential mTOR inhibitors, with similar or better properties than the classic inhibitor complex, rapamycin.     

Discover potential inhibitors for PFKFB3 using 3D-QSAR,virtual screening,molecular docking and molecular dynamics simulation

Abstract

The 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) is a master regulator of glycolysis in cancer cells by synthesizing fructose-2,6-bisphosphate (F-2,6-BP), a potent allosteric activator of phosphofructokinase-1 (PFK-1), which is a rate-limiting enzyme of glycolysis. PFKFB3 is an attractive target for cancer treatment. It is valuable to discover promising inhibitors by using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking and molecular dynamics simulation. Twenty molecules with known activity were used to build 3D-QSAR pharmacophore models. The best pharmacophore model was ADHR called Hypo1, which had the highest correlation value of 0.98 and the lowest RMSD of 0.82. Then, the Hypo1 was validated by cost value method, test set method and decoy set validation method. Next, the Hypo1 combined with Lipinski's rule of five and ADMET properties were employed to screen databases including Asinex and Specs, total of 1,048,159 molecules. The hits retrieved from screening were docked into protein by different procedures including HTVS, SP and XP. Finally, nine molecules were picked out as potential PFKFB3 inhibitors. The stability of PFKFB3-lead complexes was verified by 40?ns molecular dynamics simulation. The binding free energy and the energy contribution of per residue to the binding energy were calculated by MM-PBSA based on molecular dynamics simulation.     

In silico studies on potential MCF-7 inhibitors: a combination of pharmacophore and 3D-QSAR modeling,virtual screening,molecular docking,and pharmacokinetic analysis

Gallic acid and its derivatives exhibit a diverse range of biological applications, including anti-cancer activity. In this work, a data-set of forty-six molecules containing the galloyl moiety, and known to show anticarcinogenic activity against the MCF-7 human cancer cell line, have been chosen for pharmacophore modeling and 3D-Quantitative Structure Activity Relationship (3D-QSAR) studies. A tree-based partitioning algorithm has been used to find common pharmacophore hypotheses. The QSAR model was generated for three, four, and five featured hypotheses with increasing PLS factors and analyzed. Results for five featured hypotheses with three acceptors and two aromatic rings were the best out of all the possible combinations. On analyzing the results, the most robust (R^2?=?.8990) hypothesis with a good predictive power (Q^2?=?.7049) was found to be AAARR.35. A good external validation (R² = .6109) was also obtained. In order to design new MCF-7 inhibitors, the QSAR model was further utilized in pharmacophore-based virtual screening of a large database. The predicted IC₅₀ values of the identified potential MCF-7 inhibitors were found to lie in the micromolar range. Molecular docking into the colchicine domain of tubulin was performed in order to examine one of the probable mechanisms. This revealed various interactions between the ligand and the active site protein residues. The present study is expected to provide an effective guide for methodical development of potent MCF-7 inhibitors.     

Identification of novel urease inhibitors: pharmacophore modeling,virtual screening and molecular docking studies

Abstract

Pharmacophore modeling and atom-based three-dimensional quantitative structure–activity relationship (3D-QSAR) have been developed on N-acylglycino- and hippurohydroxamic acid derivatives, which are known potential inhibitors of urease. This is followed by virtual screening and ADMET (absorption, distribution, metabolism, excretion and toxicity) studies on a large library of known drugs in order to get lead molecules as Helicobacter pylori urease inhibitors. A suitable three-featured pharmacophore model comprising one H-bond acceptor and two H-bond donor features (ADD.10) has been found to be the best QSAR model. An external library of compounds (～3000 molecules), pre-filtered using Lipinski’s rule of five, has been further screened using the pharmacophore model ADD.10. By analyzing the fitness of the hits with respect to the pharmacophore model and their binding interaction inside the urease active site, four molecules have been predicted to be extremely good urease inhibitors. Two of these have significant potential and should be taken up for further drug-designing process.     

Molecular docking based virtual screening of natural compounds as potential BACE1 inhibitors: 3D QSAR pharmacophore mapping and molecular dynamics analysis

Beta-site APP cleaving enzyme1 (BACE1) catalyzes the rate determining step in the generation of Aβ peptide and is widely considered as a potential therapeutic drug target for Alzheimer’s disease (AD). Active site of BACE1 contains catalytic aspartic (Asp) dyad and flap. Asp dyad cleaves the substrate amyloid precursor protein with the help of flap. Currently, there are no marketed drugs available against BACE1 and existing inhibitors are mostly pseudopeptide or synthetic derivatives. There is a need to search for a potent inhibitor with natural scaffold interacting with flap and Asp dyad. This study screens the natural database InterBioScreen, followed by three-dimensional (3D) QSAR pharmacophore modeling, mapping, in silico ADME/T predictions to find the potential BACE1 inhibitors. Further, molecular dynamics of selected inhibitors were performed to observe the dynamic structure of protein after ligand binding. All conformations and the residues of binding region were stable but the flap adopted a closed conformation after binding with the ligand. Bond oligosaccharide interacted with the flap as well as catalytic dyad via hydrogen bond throughout the simulation. This led to stabilize the flap in closed conformation and restricted the entry of substrate. Carbohydrates have been earlier used in the treatment of AD because of their low toxicity, high efficiency, good biocompatibility, and easy permeability through the blood–brain barrier. Our finding will be helpful in identify the potential leads to design novel BACE1 inhibitors for AD therapy.     

Structure-based virtual screening,molecular docking,ADMET and molecular simulations to develop benzoxaborole analogs as potential inhibitor against Leishmania donovani trypanothione reductase

Visceral leishmaniasis (VL) is the most fatal form of leishmaniasis and it affects 70 countries worldwide. Increasing drug resistant for antileishmanial drugs such as miltefosine, sodium stibogluconate and pentamidine has been reported in the VL endemic region. Amphotericin B has shown potential antileishmanial activity in different formulations but its cost of treatment and associated nephrotoxicity have limited its use by affected people living in the endemic zone. To control the VL infection in the affected countries, it is necessary to develop new antileishmanial compounds with high efficacy and negligible toxicity. Computer aided programs such as binding free energy estimation; ADMET prediction and molecular dynamics simulation can be used to investigate novel antileishmanial molecules in shorter duration. To develop antileishmanial lead molecule, we performed standard precision (SP) docking for 1160 benzoxaborole analogs along with reference inhibitors against trypanothione reductase of Leishmania parasite. Furthermore, extra precision (XP) docking, ADMET prediction, prime MM-GBSA was conducted over 115 ligands, showing better docking score than reference inhibitors to get potential antileishmanial compounds. Simultaneously, area under the curve (AUC) was estimated using ROC plot to validate the SP and XP docking protocol. Later on, two benzoxaborole analogs with best MM-GBSA ΔG-bind were subjected to molecular simulation and docking confirmation to ensure the ligand interaction with TR. The presented drug discovery based on computational study confirms that BOB27 can be used as a potential drug candidate and warrants further experimental investigation to fight against VL in endemic areas.     

Shape-based virtual screening,docking, and molecular dynamics simulations to identify Mtb-ASADH inhibitors

Aspartate β-semialdehyde dehydrogenase (ASADH) is a key enzyme for the biosynthesis of essential amino acids and several important metabolites in microbes. Inhibition of ASADH enzyme is a promising drug target strategy against Mycobacterium tuberculosis (Mtb). In this work, in silico approach was used to identify potent inhibitors of Mtb-ASADH. Aspartyl β-difluorophosphonate (β-AFP), a known lead compound, was used to understand the molecular recognition interactions (using molecular docking and molecular dynamics analysis). This analysis helped in validating the computational protocol and established the participation of Arg99, Glu224, Cys130, Arg249, and His256 amino acids as the key amino acids in stabilizing ligand–enzyme interactions for effective binding, an essential feature is H-bonding interactions with the two arginyl residues at the two ends of the ligand. Best binding conformation of β-AFP was selected as a template for shape-based virtual screening (ZINC and NCI databases) to identify compounds that competitively inhibit the Mtb-ASADH. The top rank hits were further subjected to ADME and toxicity filters. Final filter was based on molecular docking analysis. Each screened molecule carries the characteristics of the highly electronegative groups on both sides separated by an average distance of 6?Å. Finally, the best predicted 20 compounds exhibited minimum three H-bonding interactions with Arg99 and Arg249. These identified hits can be further used for designing the more potent inhibitors against ASADH family. MD simulations were also performed on two selected compounds (NSC4862 and ZINC02534243) for further validation. During the MD simulations, both compounds showed same H-bonding interactions and remained bound to key active residues of Mtb-ASADH.     

Identification of novel CDK2 inhibitors by a multistage virtual screening method based on SVM,pharmacophore and docking model

Abstract

Cyclin-dependent kinase 2 (CDK2) is the family of Ser/Thr protein kinases that has emerged as a highly selective with low toxic cancer therapy target. A multistage virtual screening method combined by SVM, protein-ligand interaction fingerprints (PLIF) pharmacophore and docking was utilised for screening the CDK2 inhibitors. The evaluation of the validation set indicated that this method can be used to screen large chemical databases because it has a high hit-rate and enrichment factor (80.1% and 332.83 respectively). Six compounds were screened out from NCI, Enamine and Pubchem database. After molecular dynamics and binding free energy calculation, two compounds had great potential as novel CDK2 inhibitors and they also showed selective inhibition against CDK2 in the kinase activity assay.     

Discovering novel carbonic anhydrase type IX (CA IX) inhibitors from seven million compounds using virtual screening and in vitro analysis

Carbonic anhydrase type IX (CA IX) enzyme is mostly over expressed in different cancer cell lines and tumor tissues. Potent CA IX inhibitors can be effective for adjusting the pH imbalance in tumor cells. In the present work, we represented the successful application of high throughput virtual screening (HTVS) of large dataset from ZINC database included of ～7 million compounds to discover novel inhibitors of CA IX. HTVS and molecular docking were performed using consequence Glide/standard precision (SP), extra precision (XP) and induced fit docking (IFD) molecular docking protocols. For each compound, docking code calculates a set of low-energy poses and then exhaustively scans the binding pocket of the target with small compounds. Novel CA IX inhibitor candidates were suggested based on molecular modeling studies and a few of them were tested using in vitro analysis. These compounds were determined as good inhibitors against human CA IX target with K_i in the range of 0.85–1.58?μM. In order to predict the pharmaceutical properties of the selected compounds, ADME (absorption, distribution, metabolism and excretion) analysis was also carried out.     

Discovery of novel inhibitors of Mycobacterium tuberculosis MurG: homology modelling,structure based pharmacophore,molecular docking,and molecular dynamics simulations

MurG (Rv2153c) is a key player in the biosynthesis of the peptidoglycan layer in Mycobacterium tuberculosis (Mtb). This work is an attempt to highlight the structural and functional relationship of Mtb MurG, the three-dimensional (3D) structure of protein was constructed by homology modelling using Discovery Studio 3.5 software. The quality and consistency of generated model was assessed by PROCHECK, ProSA and ERRAT. Later, the model was optimized by molecular dynamics (MD) simulations and the optimized model complex with substrate Uridine-diphosphate-N-acetylglucosamine (UD1) facilitated us to employ structure-based virtual screening approach to obtain new hits from Asinex database using energy-optimized pharmacophore modelling (e-pharmacophore). The pharmacophore model was validated using enrichment calculations, and finally, validated model was employed for high-throughput virtual screening and molecular docking to identify novel Mtb MurG inhibitors. This study led to the identification of 10 potential compounds with good fitness, docking score, which make important interactions with the protein active site. The 25 ns MD simulations of three potential lead compounds with protein confirmed that the structure was stable and make several non-bonding interactions with amino acids, such as Leu290, Met310 and Asn167. Hence, we concluded that the identified compounds may act as new leads for the design of Mtb MurG inhibitors.     

3D QSAR pharmacophore-based virtual screening for the identification of potential inhibitors of tyrosinase

Abstract

Tyrosinase plays an important role in melanin biosynthesis and protects skin against ultraviolet radiations. Functional deficiency of tyrosinase results in serious dermatological diseases. Tyrosinase also participates in neuromelanin formation in the human brain, which leads to neurodegeneration resulting in Parkinson’s disease. In fruits and vegetables, tyrosinase plays a critical role in senescence, causing undesired browning that results in faster deterioration and shorter shelf lines. The only commercially available tyrosinase is mushroom tyrosinase and it shows the highest homology to the mammalian tyrosinase. Although kojic acid is currently used as a tyrosinase inhibitor, they have serious side effects such as dermatitis, carcinogenesis and hepatotoxicity. Therefore, in order to develop a more active and safer tyrosinase inhibitor, 3D QSAR pharmacophore models were generated based on experimentally known inhibitors. The pharmacophore model, Hypo1, was developed with a large cost difference, high correlation coefficient and low RMS deviation. Hypo1 showed a good spatial arrangement; consisting of five-point features including two hydrogen bond acceptor, one hydrogen bond donor and two hydrophobic features. Hypo1 was further validated by cost analysis, test set and Fisher’s randomisation method. Hypo1 was used as a 3D query for screening the in-house drug-like databases, and the hits were further selected by applying ADMET, Lipinski’s rule of five and fit value criteria. To identify binding conformations, the obtained hits were subjected to molecular docking. Finally, molecular dynamics simulations revealed the appropriate binding modes of hit compounds. To conclude, we propose the final three hit compounds with new structural scaffolds as a virtual candidate as tyrosinase inhibitors.

Communicated by Ramaswamy H. Sarma     

Identification of potential tumour-associated carbonic anhydrase isozyme IX inhibitors: atom-based 3D-QSAR modelling,pharmacophore-based virtual screening and molecular docking studies

Abstract

Tumour hypoxia results in dramatic changes in the gene expression, proliferation and survival of tumour cells. The tumour cells shift towards anaerobic glycolysis which results in change of pH in their microenvironment. In response to this stress, over expression of carbonic anhydrase IX (CA IX) genes is observed in many solid tumours. So, selective inhibition of CA IX can be a promising target for anti-cancer drugs. In this work in silico tools like atom-based 3D-QSAR modelling, pharmacophore-based virtual screening and molecular docking were used to identify potential CA IX inhibitors. Based on the training set used in the QSAR model, twenty pharmacophore models were generated. Out of these, HHHR_1, AHHR_1, DHHHR_1, AHHHR_1 model was used to screen a database of 1,50,000 compounds retrieved from ZINC 15 database. R² and Q² was 0.9864 and 0.8799, respectively, for the developed QSAR model. 163 compounds showed a phase screen score above 2.4 in which ZINC02260669 was the highest ranked (screen score, 2.852058) compound in all the four models. Built QSAR model was used to predict the activity of all these 163 compounds and ZINC72370966 showed the highest predicted activity with pKi value of 7.649. These compounds were docked against CA IX (human) protein (PDB ID 5FL6) and molecular docking results showed favourable binding interactions for the best ten identified hits. This work gives design insights and some potential scaffolds which can be developed as CA IX inhibitors.

Communicated by Ramaswamy H. Sarma     

Hierarchical virtual screening of the dual MMP-2/HDAC-6 inhibitors from natural products based on pharmacophore models and molecular docking

The dual-target inhibitors tend to improve the response rate in treating tumors, comparing with the single-target inhibitors. Matrix metalloproteinase-2 (MMP-2) and histone deacetylase-6 (HDAC-6) are attractive targets for cancer therapy. In this study, the hierarchical virtual screening of dual MMP-2/HDAC-6 inhibitors from natural products is investigated. The pharmacophore model of MMP-2 inhibitors is built based on ligands, but the pharmacophore model of HDAC-6 inhibitors is built based on the experimental crystal structures of multiple receptor–ligand complexes. The reliability of these two pharmacophore models is validated subsequently. The hierarchical virtual screening, combining these two different pharmacophore models of MMP-2 and HDAC-6 inhibitors with molecular docking, is carried out to identify the dual MMP-2/HDAC-6 inhibitors from a database of natural products. The four potential dual MMP-2/HDAC-6 inhibitors of natural products, STOCK1 N-46177, STOCK1 N-52245, STOCK1 N-55477, and STOCK1 N-69706, are found. The studies of binding modes show that the screened four natural products can simultaneously well bind with the MMP-2 and HDAC-6 active sites by different kinds of interactions, to inhibit the MMP-2 and HDAC-6 activities. In addition, the ADMET properties of screened four natural products are assessed. These found dual MMP-2/HDAC-6 inhibitors of natural products could serve as the lead compounds for designing the new dual MMP-2/HDAC-6 inhibitors having higher biological activities by carrying out structural modifications and optimizations in the future studies.     

Multiple e-pharmacophore modelling pooled with high-throughput virtual screening,docking and molecular dynamics simulations to discover potential inhibitors of Plasmodium falciparum lactate dehydrogenase (PfLDH)

Development of new antimalarial drugs continues to be of huge importance because of the resistance of malarial parasite towards currently used drugs. Due to the reliance of parasite on glycolysis for energy generation, glycolytic enzymes have played important role as potential targets for the development of new drugs. Plasmodium falciparum lactate dehydrogenase (PfLDH) is a key enzyme for energy generation of malarial parasites and is considered to be a potential antimalarial target. Presently, there are nearly 15 crystal structures bound with inhibitors and substrate that are available in the protein data bank (PDB). In the present work, we attempted to consider multiple crystal structures with bound inhibitors showing affinity in the range of 1.4 × 10²–1.3 × 10⁶ nM efficacy and optimized the pharmacophore based on the energy involved in binding termed as e-pharmacophore mapping. A high throughput virtual screening (HTVS) combined with molecular docking, ADME predictions and molecular dynamics simulation led to the identification of 20 potential compounds which could be further developed as novel inhibitors for PfLDH.