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.     

Computer-aided discovery of novel non-peptide inhibitors against amyloid-beta (Aβ) peptide aggregation for treating Alzheimer's disease

A non-peptide inhibitor that is metabolically stable, orally active and capable of crossing the blood–brain barrier has been a popular option for treating Alzheimer's disease (AD). To identify novel non-peptide inhibitors for AD drug development, a structure-based pharmacophore model (SBPM) was developed using the representative docked conformation of the recently discovered peptide inhibitor PGKLVYA in the potential binding site on the Aβ(17–42) protofibril. The best SBPM, consisting of two hydrophobic, one hydrogen bond donor, and one positive ionisable feature, was further validated using ligand pharmacophore mapping studies. The well-validated SBPM was then used as the 3D query in virtual screening to identify potential hits from the National Cancer Institute database. These hits were subsequently filtered by toxicity prediction and molecular docking, and their binding stabilities and affinities were validated by 20-ns molecular dynamics simulations and molecular mechanics Poisson–Boltzmann surface area analysis, respectively. Finally, two Hits (NSC35984 and NSC102747) were identified as potential leads, which exhibited higher binding stability and affinity towards Aβ compared with PGKVYA. Our results also suggest that these two Hits have the ability to prevent Aβ adopting toxic β-sheet structures, and can be easily synthesised and have structural novelty, indicating that they are promising candidates for treating AD.     

A Combination of Receptor-Based Pharmacophore Modeling & QM Techniques for Identification of Human Chymase Inhibitors

Inhibition of chymase is likely to divulge therapeutic ways for the treatment of cardiovascular diseases, and fibrotic disorders. To find novel and potent chymase inhibitors and to provide a new idea for drug design, we used both ligand-based and structure-based methods to perform the virtual screening(VS) of commercially available databases. Different pharmacophore models generated from various crystal structures of enzyme may depict diverse inhibitor binding modes. Therefore, multiple pharmacophore-based approach is applied in this study. X-ray crystallographic data of chymase in complex with different inhibitors were used to generate four structure–based pharmacophore models. One ligand–based pharmacophore model was also developed from experimentally known inhibitors. After successful validation, all pharmacophore models were employed in database screening to retrieve hits with novel chemical scaffolds. Drug-like hit compounds were subjected to molecular docking using GOLD and AutoDock. Finally four structurally diverse compounds with high GOLD score and binding affinity for several crystal structures of chymase were selected as final hits. Identification of final hits by three different pharmacophore models necessitates the use of multiple pharmacophore-based approach in VS process. Quantum mechanical calculation is also conducted for analysis of electrostatic characteristics of compounds which illustrates their significant role in driving the inhibitor to adopt a suitable bioactive conformation oriented in the active site of enzyme. In general, this study is used as example to illustrate how multiple pharmacophore approach can be useful in identifying structurally diverse hits which may bind to all possible bioactive conformations available in the active site of enzyme. The strategy used in the current study could be appropriate to design drugs for other enzymes as well.     

Discovery of potential pancreatic cholesterol esterase inhibitors using pharmacophore modelling, virtual screening, and optimization studies

Pancreatic cholesterol esterase (CEase) is a serine hydrolase involved in the hydrolysis of variety of lipids and transport of free cholesterol. In this study, pharmacophore hypotheses based on known inhibitors were generated using common feature pharmacophore generation protocol available in Discovery Studio program. The best pharmacophore model containing two hydrogen bond acceptor and three hydrophobic features was selected and validated. It was further used in screening three diverse chemical databases. Hit compounds were subjected to drug-likeness and molecular docking studies. Four hits, namely SEW00846, NCI0040784, GK03167, and CD10645, were selected based on the GOLD fitness score and interaction with active site amino acids. All hit compounds were further optimized to improve their binding in the active site. The optimized compounds were found to have improved binding at the active site. Strongly binding optimized hits at the active site can act as virtual leads in potent CEase inhibitor designing.     

New leads of metallo-beta-lactamase inhibitors from structure-based pharmacophore design

We have applied pharmacophore generation, database searching, docking methodologies, and experimental enzyme kinetics to discover new structures for design of di-zinc metallo-beta-lactamase inhibitors. Based on crystal structures of class B1 metallo-beta-lactamases with a succinic acid and a mercapto-carboxylic acid inhibitor bound to the enzyme, two pharmacophore models were constructed. With the Catalyst program, these pharmacophores were used to search the ACD database, which provided a total of 74 hits representing four different chemical classes of compounds: Dicarboxylic acids, phosphonic and sulfonic acid derivatives, and mercapto-carboxylic acids. All hits were docked into different metallo-beta-lactamases (from classes B1 and B3) using the GOLD docking program. A selection scheme based on the GOLD scores, the Catalyst fit and shape values, and the size of the compounds (molecular weight, surface area, and number of rotatable bonds) was developed and thirteen compounds representing all four chemical classes were selected for experimental studies. Three compounds with new scaffolds hitherto not present in metallo-beta-lactamase inhibitors have IC50 values less than 15 microM and may serve as starting points in the design of metallo-beta-lactamase inhibitors.     

Pharmacophore modeling,molecular docking and molecular dynamics studies on natural products database to discover novel skeleton as non-purine xanthine oxidase inhibitors

Gout is a common inflammatory arthritis caused by the deposition of urate crystals within joints. It is increasingly in prevalence during the past few decades as shown by the epidemiological survey results. Xanthine oxidase (XO) is a key enzyme to transfer hypoxanthine and xanthine to uric acid, whose overproduction leads to gout. Therefore, inhibiting the activity of xanthine oxidase is an important way to reduce the production of urate. In the study, in order to identify the potential natural products targeting XO, pharmacophore modeling was employed to filter databases. Here, two methods, pharmacophore based on ligand and pharmacophore based on receptor-ligand, were constructed by Discovery Studio. Then GOLD was used to refine the potential compounds with higher fitness scores. Finally, molecular docking and dynamics simulations were employed to analyze the interactions between compounds and protein. The best hypothesis was set as a 3D query to screen database, returning 785 and 297 compounds respectively. A merged set of the above 1082 molecules was subjected to molecular docking, which returned 144 hits with high-fitness scores. These molecules were clustered in four main kinds depending on different backbones. What is more, molecular docking showed that the representative compounds established key interactions with the amino acid residues in the protein, and the RMSD and RMSF of molecular dynamics results showed that these compounds can stabilize the protein. The information represented in the study confirmed previous reports. And it may assist to discover and design new backbones as potential XO inhibitors based on natural products.     

Identification of selective MMP-9 inhibitors through multiple e-pharmacophore,ligand-based pharmacophore,molecular docking,and density functional theory approaches

Matrix metalloproteinase-9 (MMP-9) is a significant target for the development of drugs for the treatment of arthritis, CNS disorders, and cancer metastasis. The structure-based and ligand-based methods were used for the virtual screening (VS) of database compounds to obtain potent and selective MMP-9 inhibitors. Experimentally known MMP-9 inhibitors were used to grow up ligand-based three pharmacophore models utilizing Schrodinger suite. The X-ray crystallographic structures of MMP-9 with different inhibitors were used to develop five energy-optimized structure-based (e-pharmacophore) models. All developed pharmacophores were validated and applied to screen the Zinc database. Pharmacophore matched compounds were subjected to molecular docking to retrieve hits with novel scaffolds. The molecules with diverse structures, high docking scores and low binding energies for various crystal structures of MMP-9, were selected as final hits. The Induced fit docking (IFD) analysis provided significant information about the driving of inhibitor to approve a suitable bioactive conformational position in the active site of protein. Since charge transfer reaction occurs during receptor–ligand interaction, therefore, electronic features of hits (ligands) are interesting parameters to explain the binding interactions. Density functional theory (DFT) at B3LYP/6-31G* level was utilized to explore electronic features of hits. The docking study of hits using AutoDock was helpful to establish the binding interactions. The study illustrates that the combined pharmacophore approach is advantageous to identify diverse hits which have better binding affinity to the active site of the enzyme for all possible bioactive conformations. The approach used in the study is worthy to design drugs for other targets.     

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.

A profound computational study to prioritize the natural compound inhibitors against the P. falciparum orotidine-5-monophosphate decarboxylase enzyme

Abstract

In the current contribution, a multicomplex-based pharmacophore modeling approach was employed on the structural proteome of Plasmodium falciparum orotidine-5-monophosphate decarboxylase enzyme (PfOMPDC). Among the constructed pharmacophore models, the representative hypotheses were selected as the primary filter to screen the molecules with the complementary features responsible for showing inhibition. Thereafter, auxiliary evaluations were performed on the screened candidates via drug-likeness and molecular docking studies. Subsequently, the stability of the docked protein-ligand complexes was scrutinized by employing molecular dynamics simulations and molecular mechanics-Poisson Boltzmann surface area based free binding energy calculations. The stability the docked candidates was compared with the highly active crystallized inhibitor (3S9Y-FNU) to seek more potential candidates. All the docked molecules displayed stable dynamic behavior and high binding free energy in comparison to 3S9Y-FNU. The employed workflow resulted in the retrieval of five drug-like candidates with diverse scaffolds that may show inhibitory activity against PfOMPDC and could be further used as the novel scaffold to develop novel antimalarials.

Communicated by Ramaswamy H. Sarma     

Identification of Pak1 inhibitors using water thermodynamic analysis

Abstract

p21-activated kinases (Paks) play an integral component in various cellular diverse processes. The full activation of Pak is dependent upon several serine residues present in the N-terminal region, a threonine present at the activation loop, and finally the phosphorylation of these residues ensure the complete activation of Pak1. The present study deals with the identification of novel potent candidates of Pak1 using computational methods as anti-cancer compounds. A diverse energy based pharmacophore (e-pharmacophore) was developed using four co-crystal inhibitors of Pak1 having pharmacophore features of 5 (DRDRR), 6 (DRHADR), and 7 (RRARDRP and DRRDADH) hypotheses. These models were used for rigorous screening against e-molecule database. The obtained hits were filtered using ADME/T and molecular docking to identify the high affinity binders. These hits were subjected to hierarchical clustering using dendritic fingerprint inorder to identify structurally diverse molecules. The diverse hits were scored against generated water maps to obtain WM/MM ΔG binding energy. Furthermore, molecular dynamics simulation and density functional theory calculations were performed on the final hits to understand the stability of the complexes. Five structurally diverse novel Pak1 inhibitors (4835785, 32198676, 32407813, 76038049, and 32945545) were obtained from virtual screening, water thermodynamics and WM/MM ΔG binding energy. All hits revealed similar mode of binding pattern with the hinge region residues replacing the unstable water molecules in the binding site. The obtained novel hits could be used as a platform to design potent drugs that could be experimentally tested against cancer patients having increased Pak1 expression.     

Discovery of promising FtsZ inhibitors by E-pharmacophore, 3D-QSAR,molecular docking study,and molecular dynamics simulation

Filamentous temperature-sensitive protein Z (FtsZ), playing a key role in bacterial cell division, is regarded as a promising target for the design of antimicrobial agent. This study is looking for potential high-efficiency FtsZ inhibitors. Ligand-based pharmacophore and E-pharmacophore, virtual screening and molecular docking were used to detect promising FtsZ inhibitors, and molecular dynamics simulation was used to study the stability of protein-ligand complexes in this paper. Sixty-three inhibitors from published literatures with pIC₅₀ ranging from 2.483 to 5.678 were collected to develop ligand-based pharmacophore model. 4DXD bound with 9PC was selected to develop the E-pharmacophore model. The pharmacophore models validated by test set method and decoy set were employed for virtual screening to exclude inactive compounds against ZINC database. After molecular docking, ADME analysis, IFD docking and MM-GBSA, 8 hits were identified as potent FtsZ inhibitors. A 50?ns molecular dynamics simulation was implemented on the compounds to assess the stability between potent inhibitors and FtsZ. The results indicated that the candidate compounds had a high docking score and were strongly combined with FtsZ by forming hydrogen bonding interactions with key amino acid residues, and van der Waals forces and hydrophobic interactions had significant contribution to the stability of the binding. Molecular dynamics simulation results showed that the protein-ligand compounds performed well in both the stability and flexibility of the simulation process.     

Pharmacoinformatics analysis to identify inhibitors of Mtb-ASADH

Aspartate-semialdehyde dehydrogenase (ASADH; EC 1.2.1.11) is a key enzyme in the biosynthesis of essential amino acids in prokaryotes and fungi, inhibition of ASADH leads to the development of novel antitubercular agents. In the present work, a combined structure and ligand-based pharmacophore modeling, molecular docking, and molecular dynamics (MD) approaches were employed to identify potent inhibitors of mycobacterium tuberculosis (Mtb)-ASADH. The structure-based pharmacophore hypothesis consists of three hydrogen bond acceptor (HBA), two negatively ionizable, and one positively ionizable center, while ligand-based pharmacophore consists of additional one HBA and one hydrogen bond donor features. The validated pharmacophore models were used to screen the chemical databases (ZINC and NCI). The screened hits were subjected to ADME and toxicity filters, and subsequently to the molecular docking analysis. Best-docked 25 compounds carry the characteristics of highly electronegative functional groups (–COOH and –NO₂) on both sides and exhibited the H-bonding interactions with highly conserved residues Arg99, Arg249, and His256. For further validation of docking results, MD simulation studies were carried out on two representative compounds NSC51108 and ZINC04203124. Both the compounds remain bound to the key active residues of Mtb-ASADH during the MD simulations. These identified hits can be further used for lead optimization and in the design more potent inhibitors against Mtb-ASADH.     

Combined structure- and ligand-based pharmacophore modeling and molecular dynamics simulation studies to identify selective inhibitors of MMP-8

Matrix metalloproteinase-8 (MMP-8) is the key mediator in initiating type I collagen degradation and is associated with rheumatoid arthritis. In the present study, a pharmacophore hypothesis was developed based on selective non zinc binding inhibitors of MMP-8. The pharmacophore hypothesis was refined manually and validated by observing structures and the interactions of MMP-8 inhibitors. The refined pharmacophore model was able to discriminate the non-zinc binding inhibitors of MMP-8 with respect to other inhibitors. Hence this study proposes a combined structure- and ligand-based pharmacophore model that is suitable for retrieving the novel inhibitors of MMP-8. The pharmacophore hypothesis AADRH was used as query for retrieving potential compounds from the Zinc database and hits were selected based on the catalytic selective amino acid residues of Arg 222, and Tyr 227. We identified six compounds as potent inhibitors and their selectivity profile were checked against different subtypes of MMPs using the cross-docking method. Molecular dynamics results indicated that ZINC 00673680 forms a stable interaction with the key amino acid residues and avoids the zinc atom with a distance of 5.49?Å. Our computational study might be useful for further development of selective MMP-8 inhibitors.     

Identification of novel G-protein-coupled receptor 40 (GPR40) agonists by hybrid in silico-screening techniques and molecular dynamics simulations thereof

Abstract

Diabetes is a major health problem worldwide predisposing to increased mortality and morbidity. The current antidiabetic therapies have serious side effects and thus have emphasis on further need to develop effective medication therapy. Free fatty acid1 receptor (FFA1R) or G-protein-coupled receptor 40 (GPR40) represents an interesting target for developing novel antidiabetic drug. In the current study, the FFA1R agonistic activity of drug-like molecules was screened by employing pharmacophore modeling, docking, and molecular dynamics (MD) simulation. Hierarchical screening of virtual library of drug-like compounds was performed. This combined computational approach of pharmacophore mapping and structure-based approach was used to identify common hits, and the absorption, distribution, metabolism and excretion (ADME) prediction supported the analysis of their pharmacokinetic potential. MD simulation studies of the GPR40 complex with the most promising hit found in this study further validated are approached. The key residues Arg183, Arg258, Tyr91, and Tyr240 of the binding pocket were acknowledged as essential and were found to be associated in the key interactions with the most potential hit. These studies will hopefully provide scope for efficiently designing and screening new compounds as active drug candidates with more selectivity for hGPR40. To the best of our knowledge, this is the first example of the successful application of both ligand and structurebased virtual-screening techniques to discover novel GPR40 agonists.

Communicated by Ramaswamy H. Sarma     

Ligand- and structure-based in silico studies to identify kinesin spindle protein (KSP) inhibitors as potential anticancer agents

Kinesin spindle protein (KSP) belongs to the kinesin superfamily of microtubule-based motor proteins. KSP is responsible for the establishment of the bipolar mitotic spindle which mediates cell division. Inhibition of KSP expedites the blockade of the normal cell cycle during mitosis through the generation of monoastral MT arrays that finally cause apoptotic cell death. As KSP is highly expressed in proliferating/cancer cells, it has gained considerable attention as a potential drug target for cancer chemotherapy. Therefore, this study envisaged to design novel KSP inhibitors by employing computational techniques/tools such as pharmacophore modelling, virtual database screening, molecular docking and molecular dynamics. Initially, the pharmacophore models were generated from the data-set of highly potent KSP inhibitors and the pharmacophore models were validated against in house test set ligands. The validated pharmacophore model was then taken for database screening (Maybridge and ChemBridge) to yield hits, which were further filtered for their drug-likeliness. The potential hits retrieved from virtual database screening were docked using CDOCKER to identify the ligand binding landscape. The top-ranked hits obtained from molecular docking were progressed to molecular dynamics (AMBER) simulations to deduce the ligand binding affinity. This study identified MB-41570 and CB-10358 as potential hits and evaluated these experimentally using in vitro KSP ATPase inhibition assays.     

Multiple e-Pharmacophore modeling to identify a single molecule that could target both streptomycin and paromomycin binding sites for 30S ribosomal subunit inhibition

The bacterial ribosome is an established target for anti-bacterial therapy since decades. Several inhibitors have already been developed targeting both defined subunits (50S and 30S) of the ribosome. Aminoglycosides and tetracyclines are two classes of antibiotics that bind to the 30S ribosomal subunit. These inhibitors can target multiple active sites on ribosome that have a complex structure. To screen putative inhibitors against 30S subunit of the ribosome, the crystal structures in complex with various known inhibitors were analyzed using pharmacophore modeling approach. Multiple active sites were considered for building energy-based three-dimensional (3D) pharmacophore models. The generated models were validated using enrichment factor on decoy data-set. Virtual screening was performed using the developed 3D pharmacophore models and molecular interaction towards the 30S ribosomal unit was analyzed using the hits obtained for each pharmacophore model. The hits that were common to both streptomycin and paromomycin binding sites were identified. Further, to predict the activity of these hits a robust 2D-QSAR model with good predictive ability was developed using 16 streptomycin analogs. Hence, the developed models were able to identify novel inhibitors that are capable of binding to multiple active sites present on 30S ribosomal subunit.     

Discovery of selective farnesoid X receptor agonists for the treatment of hyperlipidemia from traditional Chinese medicine based on virtual screening and in vitro validation

Abstract

Farnesoid X receptor (FXR), a bile acid receptor, has important roles in maintaining bile acid and cholesterol homeostasis, which is an attractive target for hyperlipidemia. Present study aimed to discover potential selective FXR agonists over G-protein coupled bile acid receptor 1 (GPBAR1, TGR5) from traditional Chinese medicine (TCM) by using virtual screening, in vitro studies and molecular dynamics simulation (MD). Ligand-based pharmacophore model for FXR was firstly built to screen FXR agonists from the Traditional Chinese Medicine Database (TCMD). Then, 21 FXR crystal structures were clustered in two types and two representative structures (PDB ID: 3OMM and 3P89) were, respectively, used to carry out molecular docking to refine the screened result. Moreover, the pharmacophore model for GPBAR1 was built to screen selective FXR agonists with no activity on GPBAR1. A set of 24 candidate selective FXR agonists which fitvalue of FXR pharmacophore model and docking score of 3OMM and 3P89 were in the top 100 and cannot match the pharmacophore model for GPBAR1 were obtained. By the lipid-lowering activity test in HepG2 cell lines, Arctigenin was identified to be potential selective FXR agonist with the activity of 20?μmol·L^?1. After down-regulating FXR, Arctigenin could increase the mRNA of FXR while exerted no effect on the mRNA of GPBAR1. MD was further used to interpret the mechanism of Arctigenin with the representative structures. This research provided a new screening procedure for finding selective candidate compounds and appropriate docking models of a target by considering the structure diversity of PDB structures, which was applied to discovery novel selective FXR agonists to treat hyperlipidemia.

Communicated by Ramaswamy H. Sarma     

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     

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.     

Multi-stage structure-based virtual screening approach towards identification of potential SARS-CoV-2 NSP13 helicase inhibitors

On account of its crucial role in the virus life cycle, SARS-COV-2 NSP13 helicase enzyme was exploited as a promising target to identify a novel potential inhibitor using multi-stage structure-based drug discovery approaches. Firstly, a 3D pharmacophore was generated based on the collected data from a protein-ligand interaction fingerprint (PLIF) study using key interactions between co-crystallised fragments and the NSP13 helicase active site. The ZINC database was screened through the generated 3D-pharmacophore retrieving 13 potential hits. All the retrieved hits exceeded the benchmark score of the co-crystallised fragments at the molecular docking step and the best five-hit compounds were selected for further analysis. Finally, a combination between molecular dynamics simulations and MM-PBSA based binding free energy calculations was conducted on the best hit (compound FWM-1) bound to NSP13 helicase enzyme, which identified FWM-1 as a potential potent NSP13 helicase inhibitor with binding free energy equals −328.6 ± 9.2 kcal/mol.