Systematic Virtual Screening Research Articles

Screening approach by a combination of computational and in vitro experiments: identification of fluvastatin sodium as a potential SIRT2 inhibitor.

Sirtuins (SIRTs) are NAD+-dependent deacetylases that play various roles in numerous pathophysiological processes, holding promise as therapeutic targets worthy of further investigation. Among them, the SIRT2 subtype is closely associated with tumorigenesis and malignancies. Dysregulation of SIRT2 activation can regulate the expression levels of related genes in cancer cells, leading to tumor occurrence and metastasis. In this study, we used computer simulations to screen for novel SIRT2 inhibitors from the FDA database, based on which 10 compounds with high docking scores and good interactions were selected for in vitro anti-pancreatic cancer metastasis testing and enzyme binding inhibition experiments. The results showed that fluvastatin sodium may possess inhibitory activity against SIRT2. Subsequently, fluvastatin sodium was subjected to molecular docking experiments with various SIRT isoforms, and the combined results from Western blotting experiments indicated its potential as a SIRT2 inhibitor. Next, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations were performed, revealing the binding mode of fluvastatin sodium at the SIRT2 active site, further validating the stability and interaction of the ligand-protein complex under physiological conditions. Overall, this study provides a systematic virtual screening workflow for the discovery of SIRT2 activity inhibitors, identifies the potential inhibitory effect of fluvastatin sodium as a lead compound on SIRT2, and opens up a new direction for developing highly active and selectively targeted SIRT2 inhibitors.

A computational strategy for systematic virtual screening of plasmodium falciparum heme detoxification protein inhibitors from the Drugbank database

Antimalarial drug resistance poses one of the greatest threats to malaria treatment, resulting in increased morbidity and mortality. Heme Detoxification Protein (HDP) is among the essential hemoglobinases of P. falciparum (Pf), a vital molecular target for the treatment of malaria. In this study, we utilized the virtual screening workflow tool of the Schrodinger suite to find the best hits for the PfHDP from the DrugBank library. A total of 14,942 compounds were identified against the PfHDP. The top compounds with the highest docking scores and least energy scores were subjected to molecular simulations for 500 nanosecond to check the stability of the protein-drug complexes. The top three DrugBank compounds were found to be stable over 500 ns, namely DB09298 (silibinin), DB07426 (1-Hydroxy-2-(1,1':3',1''-Terphenyl-3-Yloxy) Ethane-1,1-Diyl] Bis (Phosphonic Acid), and DB07410 [(2-(3-Dibenzofuran-4-yl-Phenyl)-1-Hydroxy-1-Phosphono-Ethyl]-Phosphonic Acid). Overall analysis suggests that the top three compounds, DB09298, DB07426, and DB07410, have good stability for 500 ns. Their scaffolds can be used to design and develop new analogs of the target HDP protein. Silibinin, the anti-cancer drug, was found to be highly stable for the entire simulation period as compared to the other compounds. DB07426 shows its therapeutic effect on bones, especially in the treatment of osteoporosis, and DB07410 has anti-tumor, antibacterial, anti-oxidative, and anti-viral activities. All three compounds can be considered for repurposing as antimalarial drugs to evaluate the binding capacity or inhibition potential of these compounds. Further in-vivo and in-vitro analysis against the PfHDP protein should be conducted. Communicated by Ramaswamy H. Sarma

Discovery of andrographolide hit analog as a potent cyclooxygenase-2 inhibitor through consensus MD-simulation, electrostatic potential energy simulation and ligand efficiency metrics

Cyclooxygenase-2 (COX-2) is the key enzyme responsible for the conversion of arachidonic acid to prostaglandins that display pro-inflammatory properties and thus, it is a potential target protein to develop anti-inflammatory drugs. In this study, chemical and bio-informatics approaches have been employed to find a novel potent andrographolide (AGP) analog as a COX-2 inhibitor having better pharmacological properties than aspirin and rofecoxib (controls). The full amino acid sequenced human Alpha fold (AF) COX-2 protein (604AA) was selected and validated for its accuracy against the reported COX-2 protein structures (PDB ID: 5F19, 5KIR, 5F1A, 5IKQ and 1V0X) followed by multiple sequence alignment analysis to establish the sequence conservation. The systematic virtual screening of 237 AGP analogs against AF-COX-2 protein yielded 22 lead compounds based on the binding energy score (< − 8.0 kcal/mol). These were further screened out to 7 analogs by molecular docking analysis and investigated further for ADMET prediction, ligand efficiency metrics calculations, quantum mechanical analysis, MD simulation, electrostatic potential energy (EPE) docking simulation, and MM/GBSA. In-depth analysis revealed that AGP analog A3 (3-[2-[(1R,4aR,5R,6R,8aR)-6-hydroxy-5,6,8a-trimethyl-2-methylidene-3,4,4a,5,7,8-hexahydro-1H-naphthalen-1-yl]ethylidene]-4-hydroxyoxolan-2-one) forms the most stable complex with the AF-COX-2 showing the least RMSD value (0.37 ± 0.03 nm), a good number of hydrogen bonds (protein–ligand H-bond = 11, and protein H-bond = 525), minimum EPE score (− 53.81 kcal/mol), and lowest MM-GBSA before and after simulation (− 55.37 and − 56.25 kcal/mol, respectively) value compared to other analogs and controls. Thus, we suggest that the identified A3 AGP analog could be developed as a promising plant-based anti-inflammatory drug by inhibiting COX-2.

Identification of potential inhibitors of tropomyosin receptor kinase B targeting CNS-related disorders and cancers

Tropomyosin receptor kinase B (TrkB), also known as neurotrophic tyrosine kinase receptor type 2 (NTRK2), is a protein that belongs to the family of receptor tyrosine kinases (RTKs). NTRK2 plays a crucial role in regulating the development and maturation of the central nervous system (CNS) and peripheral nervous system (PNS). Elevated TrkB expression levels observed in different pathological conditions make it a potential target for therapeutic interventions against neurological disorders, including depression, anxiety, Alzheimer’s disease, Parkinson’s disease, and certain types of cancer. Targeting TrkB using small molecule inhibitors is a promising strategy for the treatment of a variety of neurological disorders. In this research, a systematic virtual screening was carried out on phytoconstituents found in the IMPPAT library to identify compounds potentially inhibiting TrkB. The retrieved compounds from the IMPPAT library were first filtered using Lipinski’s rule of five. The compounds were then sorted based on their docking score and ligand efficiency. In addition, PAINS, ADMET, and PASS evaluations were carried out for selecting drug-like compounds. Finally, in interaction analysis, we found two phytoconstituents, Wedelolactone and 3,8-dihydroxy-1-methylanthraquinone-2-carboxylic acid (DMCA), which possessed considerable docking scores and specificity on the TrkB ATP-binding pocket. The selected compounds were further assessed employing molecular dynamics (MD) simulations and essential dynamics. The results revealed that the elucidated compounds bind well with the TrkB binding pocket and lead to fewer conformations fluctuations. This study highlighted using phytoconstituents, Wedelolactone and DMCA as starting leads in developing novel TrkB inhibitors. Communicated by Ramaswamy H. Sarma

Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies.

Protein kinase p38γ is an attractive target against cancer because it plays a pivotal role in cancer cell proliferation by phosphorylating the retinoblastoma tumour suppressor protein. Therefore, inhibition of p38γ with active small molecules represents an attractive alternative for developing anti-cancer drugs. In this work, we present a rigorous and systematic virtual screening framework to identify potential p38γ inhibitors against cancer. We combined the use of machine learning-based quantitative structure activity relationship modelling with conventional computer-aided drug discovery techniques, namely molecular docking and ligand-based methods, to identify potential p38γ inhibitors. The hit compounds were filtered using negative design techniques and then assessed for their binding stability with p38γ through molecular dynamics simulations. To this end, we identified a promising compound that inhibits p38γ activity at nanomolar concentrations and hepatocellular carcinoma cell growth in vitro in the low micromolar range. This hit compound could serve as a potential scaffold for further development of a potent p38γ inhibitor against cancer.

Integration of fingerprint-based similarity searching and kernel-based partial least squares analysis to predict inhibitory activity against CSK, HER2, JAK1, JAK2, and JAK3.

Fingerprint-based similarity searching is an important strategy for virtual screening in drug discovery. In the present study, we carried out a systematic virtual screening study, followed by the establishment of kernel-based partial least square (KPLS) analysis prediction models for five tyrosine kinase drug targets, C-terminal SRC kinase (CSK), human epidermal growth factor 2 (HER2), and Janus kinases 1, 2, and 3 (JAK1, JAK2, and JAK3), using a dataset of 3688 compounds. These kinases are important drug discovery targets, particularly as HER2 has been validated for the treatment of metastatic breast cancer, JAK inhibitors have been validated for the clinical management of arthritis and autoimmune diseases, and CSK has been found to play an important role in bone remodeling in arthritis. We conducted similarity screenings with the most active molecule for each target in the dataset as a query using eight (8) types of two-dimensional (2D) molecular fingerprints, comprising seven Hashed fingerprints, Linear, Dendritic, Radial, Pairwise, Triplet, Torsion, and MOLSPRINT2D, and one Structural keys fingerprint, MACCS. The top ranked 1% of compounds from each target's similarity screening results was used to set up kernel-based partial least square (KPLS) prediction models, with q2 values up to 0.8. The best KPLS model for each target was selected based on its predictive ability and boot strapping results and used for prediction. This integrated study approach combining similarity screening with KPLS analysis has a high potential to enhance the accuracy and efficiency of virtual screening and thus improve the drug discovery process.

An In-silico Multi-Targeted Approach in Search of Potential Drug Candidate( s) Against SARS-CoV-2 Lung Infection

Background: The multitargeted computational approach for the design of drugs to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lung infection from herbal sources may lead to compound(s) that is/are safe (derived from natural sources), effective (act on predefined targets) and broad spectrum (active in both, adult and juvenile population). Objective: The present work aims at developing a specific and effective treatment for a lung infection in both the adult and juvenile population, caused due to SARS-CoV-2 through a computational approach. Methods: A systematic virtual screening of 27 phytoconstituents from 11 Indian herbs with antiviral, antiinflammatory, and immunomodulatory activity was performed. After applying the Lipinski rule of five, 19 compounds that fitted well were subjected to molecular docking studies using Molegro virtual docker 6.0 with two targets viz. SARS-CoV-2 main protease (Mpro) (PDB ID 6LU7) and ACE receptor (PDB ID 6M0J). The best-docked complexes were used to develop a merged feature pharmacophore using Ligandscout software, to know the structural requirements to develop multitarget inhibitor(s) of SARS-CoV- 2. Drug likeliness and ADMET studies were also performed. Results: The results revealed that Syringin, a glycoside from Tinospora cordifolia, has a good binding affinity towards both targets as compared to Remdesivir. Furthermore, drug likeliness and ADMET studies established its better bioavailability and low toxicity. Conclusion: The pharmacophores developed from protein-ligand complexes provided an important understanding to design multitarget inhibitor(s) of SARS-CoV-2 to treat COVID-19 lung infection in both the adult and juvenile populations. Syringin may be subjected to further wet-lab studies to establish the results obtained through in-silico studies.

Identification of 11-Hydroxytephrosin and Torosaflavone A as Potential Inhibitors of 3-Phosphoinositide-Dependent Protein Kinase 1 (PDPK1): Toward Anticancer Drug Discovery.

The 3-phosphoinositide-dependent protein kinase 1 (PDPK1) has a significant role in cancer progression and metastasis as well as other inflammatory disorders, and has been proposed as a promising therapeutic target for several malignancies. In this work, we conducted a systematic virtual screening of natural compounds from the IMPPAT database to identify possible PDPK1 inhibitors. Primarily, the Lipinski rules, ADMET, and PAINS filter were applied and then the binding affinities, docking scores, and selectivity were carried out to find effective hits against PDPK1. Finally, we identified two natural compounds, 11-Hydroxytephrosin and Torosaflavone A, bearing substantial affinity with PDPK1. Both compounds showed drug-likeness as predicted by the ADMET analysis and their physicochemical parameters. These compounds preferentially bind to the ATP-binding pocket of PDPK1 and interact with functionally significant residues. The conformational dynamics and complex stability of PDPK1 with the selected compounds were then studied using interaction analysis and molecular dynamics (MD) simulations for 100 ns. The simulation results revealed that PDPK1 forms stable docked complexes with the elucidated compounds. The findings show that the newly discovered 11-Hydroxytephrosin and Torosaflavone A bind to PDPK1 in an ATP-competitive manner, suggesting that they could one day be used as therapeutic scaffolds against PDPK1-associated diseases including cancer.

Combined Pharmacophore Modeling, 3D-QSAR, Molecular Docking and Molecular Dynamics Study on Indolyl-aryl-sulfone Derivatives as New HIV1 Inhibitors.

The present study deals with the in silico of 45 indolyl-aryl-sulfones known as anti-HIV1. The data were collected from recent previously reported inhibitors and divided into a sub-set of 33 compounds as the training set and the remaining 12 compounds were kept in the test set. The selected pharmacophore-ADRRR-yielded a statistically significant 3D-QSAR model containing high confidence scores (R2 = 0.930, Q2 = 0.848, and RMSE = 0.460). The predictive power of the established pharmacophore model was validated with an external test (r2 = 0.848). A systematic virtual screening workflow shows an enrichment factor and has revealed a high predictive power. Then the model was used to screen the filtered PubChem database mapping all chemical features of model pharmacophore. The recognized hits were further assessed by in silico ADMET studies. Molecular dynamics also used to explore the stability of obtained complexes. Finally, these selected compounds are probably to become a good lead molecule for the development of effective anti-HIV-1 drugs.

Systematic virtual screening in search of SARS CoV-2 inhibitors against spike glycoprotein: pharmacophore screening, molecular docking, ADMET analysis and MD simulations.

In the absence of efficient anti-viral medications, the coronavirus disease 2019 (COVID-19), stemming from severe acute respiratory syndrome coronavirus-2 (SARS CoV-2), has spawned a worldwide catastrophe and global emergency. Amidst several anti-viral targets of COVID-19, spike glycoprotein has been recognized as an essential target for the viral entry into the host cell. In the search of effective SARS CoV-2 inhibitors acting against spike glycoprotein, the virtual screening of 175,851 ligands from the 2020.1 Asinex BioDesign library has been performed using in silico tools like SiteMap analysis, pharmacophore-based screening, molecular docking using different levels of precision, such as high throughput virtual screening, standard precision and extra precision, followed by absorption, distribution, metabolism, excretion and toxicity analysis, and molecular dynamics (MD) simulation. Following a molecular docking study, seventeen molecules (with a docking score of less than − 6.0) were identified having the substantial interactions with the catalytic amino acid and nucleic acid residues of spike glycoprotein at the binding site. In investigations using MD simulations for 10 ns, the hit molecules (1 and 2) showed adequate compactness and uniqueness, as well as satisfactory stability. These computational research findings have offered a key starting point in the field of design and development of novel SARS CoV-2 entry inhibitors with appropriate drug likeliness.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s11030-022-10394-9.

Epithelial cell adhesion molecule (EpCAM) binding short peptides derived from antibody MOC-31; De-novo design, synthesis and their in-vitro evaluation

Epithelial cell adhesion molecule (EpCAM) is one of the critical bio-maker for circulating tumor cells (CTC) detection. For capturing CTC, antibody-antigen-based techniques have mainly been explored. However, the expensiveness and tedious manufacturing process have posed certain limitations for antibody-based techniques for its wide applications in cell capturing. On the other hand, peptides are inexpensive bimolecular probes with high specificity and tunability. Although there are few reports on EpCAM binding peptides are available in literature, those peptides were selected through random library screening. Interestingly, de-novo design of the peptides against EpCAM has not been reported till date. For the first time, we have developed a small peptide (Pep14) from the complementary derived region (CDRs) of antibody MOC31 through systematic virtual screening. Selected peptide has demonstrated good binding affinity towards EpCAM with dissociation constant (Kd) of 870 nM and found to be co-localized with the anti-EpCAM antibody in EpCAM expressing cancer cells (MCF-7). Therefore, the short peptide Pep14 hold promise for capturing circulatory tumor cells through EpCAM binding.

Identification of anti-mycobacterial agents against mmpL3: Virtual screening, ADMET analysis and MD simulations

• Virtual screening of 175,851 ligands from Asinex BioDesign library for anti-TB agents. • Molecular docking via HTVS, SP, and XP modes of variable precision and accuracy. • Study of drug-likeness using ADMET and Lipinski filters. • Time-dependent stability of the identified hits at active site using MD simulations. Tuberculosis has been the communicable disease being one of the top 10 causes of deaths worldwide and the leading cause of deaths from a single infectious agent (ranking above HIV/AIDS). Mycobacterial mmpL3 target plays significant role in synthesis of essential components of the mycobacterial outer membrane including iron uptake, energy production, membrane potential, and antibiotic susceptibility. The worsening situation of the TB treatment has urged the necessity of search of new anti-tubercular agents through computational chemistry driven in silico approach. In search of mmpL3 inhibitors (PDB ID: 6AJG ), molecular modeling of 175,851 ligands from 2020.1 Asinex BioDesign library through E-pharmacophore based screening followed by virtual screening using three different modes of precision (HTVS, SP and XP), ADMET analysis, Lipinski filters and MD simulations. These studies have provided an important start-point in the field of anti-mycobacterial agents through molecular modeling in search of mmpL3 inhibitors. For the first time, systematic virtual screening of 175,851 ligands from 2020 to 01 Asinex BioDesign library against mmpL3 in search of anti-TB agents has been executed using three different level of precision modes (HTVS, SP, and XP) followed by profiling of ADMET assay and evaluation of drug-likeness in search of potent anti-TB agents. The stability of the identified three hits at active site of co-crystalized SQ109, was evaluated using MD simulations.

Identification of Potential Mpro Inhibitors for the Treatment of COVID-19 by Targeted Covalent Inhibition

A novel coronavirus is the causative agent identified for the current COVID-19 outbreak. Globally, more than 43 million people have been infected by this virus. The total number of deaths has surpassed 1.6 million across 210 countries due to the current pandemic. Till date, there is no specific therapeutic agent available for its treatment. Mpro, a non-structural protein cleaves viral polyproteins into other non-structural proteins. Inhibition of Mpro could prevent the virus replication projecting it as a potential candidate for anti-COVID-19 drug development. The authors report herein 10 top-ranked curcumin derivatives as non-peptide covalent-binding Mpro inhibitors using systematic virtual screening approach. Detailed ligand-receptor interaction analysis conferred that the α,β-unsaturated carbonyl moiety of curcumin functions as a warhead to yield a Michael adduct with Cys145 of the catalytic dyad of Mpro. Collectively, these results have offered new high affinity molecules for the development of potential drugs for the treatment of COVID-19.

Identification of potential Mpro inhibitors for the treatment of COVID-19 by using systematic virtual screening approach.

The Corona virus Disease (COVID-19) is caused because of novel coronavirus (SARS-CoV-2) pathogen detected in China for the first time, and from there it spread across the globe creating a worldwide pandemic of severe respiratory complications. The virus requires structural and non-structural proteins for its multiplication that are produced from polyproteins obtained by translation of its genomic RNA. These polyproteins are converted into structural and non-structural proteins mainly by the main protease (Mpro). A systematic screening of a drug library (having drugs and diagnostic agents which are approved by FDA or other world authorities) and the Asinex BioDesign library was carried out using pharmacophore and sequential conformational precision level filters using the Schrodinger Suite. From the screening of approved drug library, three antiviral agents ritonavir, nelfinavir and saquinavir were predicted to be the most potent Mpro inhibitors. Apart from these pralmorelin, iodixanol and iotrolan were also identified from the systematic screening. As iodixanol and iotrolan carry some limitations, structural modifications in them could lead to stable and safer antiviral agents. Screenings of Asinex BioDesign library resulted in 20 molecules exhibiting promising interactions with the target protein Mpro. They can broadly be categorized into four classes based on the nature of the scaffold, viz. disubstituted pyrazoles, cyclic amides, pyrrolidine-based compounds and miscellaneous derivatives. These could be used as potential molecules or hits for further drug development to obtain clinically useful therapeutic agents for the treatment of COVID-19.Graphic abstract Electronic supplementary materialThe online version of this article (10.1007/s11030-020-10130-1) contains supplementary material, which is available to authorized users.

Discovery of specific HDAC6 inhibitor with anti-metastatic effects in pancreatic cancer cells through virtual screening and biological evaluation.

Histone deacetylase 6 (HDAC6) has been demonstrated to play a major role in cell motility and aggresome formation, and HDAC6 inhibition is therefore considered as a promising epigenetic strategy for cancer treatment. At present, only a minority of compounds have been reported as HDAC6 inhibitors, so specific HDAC6 inhibitors with safety profile need to be discovered urgently. In this paper, HDAC6 inhibitors with diverse structures were used to generate the pharmacophore model by ligand-based method, which contained two hydrogen bond acceptors and two hydrophobic groups. A combined virtual screening based on pharmacophore model and molecular docking was adopted to screen potential HDAC6 inhibitors. Subsequently, the HDAC6 inhibitory activity of the hit compounds were evaluated using an in vitro enzyme binding inhibition assay. The experimental results illustrated that cefoperazone sodium had the strongest inhibitory effect on HDAC6 among the six screened compounds, and its IC50 value was 8.59±1.06μM. Cefoperazone sodium significantly catalyzed the hyperacetylation of α-tubulin but not histone H3, proving that cefoperazone sodium was a selective inhibitor of HDAC6. Since the expression of HDAC6 plays an important role in cancer metastasis, the effects of cefoperazone sodium on migration and invasion of human pancreatic cancer PANC-1 cells were further investigated by wound healing and transwell chamber assays. It was found that cefoperazone sodium could evidently inhibit the migration and invasion of PANC-1 cells. Furthermore, the binding pattern of inhibitor at the active site of the crystal structure was revealed by molecular docking, providing a reference value for the structural design and optimization of HDAC6 inhibitors. This study provides a systematic virtual screening approach for discovering HDAC6 active inhibitors, and by which the specific effect of cefoperazone sodium against HDAC6 was found, suggesting its potential application on cancer therapy.

Discovery of novel anaplastic lymphoma kinase inhibitors: Structure and energy-based pharmacophore strategy

The clinical outcomes in patients with non-small cell lung cancer have improved, as a result of anaplastic lymphoma kinase (ALK) inhibition. Therefore in the current study, substantial effort has been made to identify ALK inhibitors through systematic virtual screening experiment consisting of e-pharmacophore and pharmacophore perception techniques. Initially, a pharmacophore model (AAAHP.193) and an e-pharmacophore model (DDRRR) encompassing the whole dataset of 12 known ALK inhibitors were developed. The hypotheses could retrieve effective compounds from DrugBank database (8621 molecules), which were then subjected to molecular docking and ADME prediction. These approaches resulted in the identification of five hits, namely, nebivolol, HDY, D42, 796, and LZE having higher Glide docking scores and promising ADME properties with augmented CNS involvement. Moreover, molecular dynamics simulations were performed to validate the inhibitory activity of the hit compounds, and density functional theory calculations were carried out to scrutinize the chemical reactivity of the hits. Subsequent interaction and scaffold analysis identified prominent interactions of the hits with ALK kinase domain and scaffolds with anti-tumor activity against lung cancer cell lines. We strongly believe that the study provides an outlook for the sighting of novel and potent ALK inhibitors in the near future.

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.

Ligand-based and e-pharmacophore modeling, 3D-QSAR and hierarchical virtual screening to identify dual inhibitors of spleen tyrosine kinase (Syk) and janus kinase 3 (JAK3)

The clinical efficacy of multiple kinase inhibitors has caught the interest of Pharmaceutical and Biotech researchers to develop potential drugs with multi-kinase inhibitory activity for complex diseases. In the present work, we attempted to identify dual inhibitors of spleen tyrosine kinase (Syk) and janus kinase 3 (JAK3), keys players in immune signaling, by developing ideal pharmacophores integrating Ligand-based pharmacophore models (LBPMs) and Structure-based pharmacophore models (SBPMs), thereby projecting the optimum pharmacophoric required for inhibition of both the kinases. The four point LBPM; ADPR.14 suggested the presence of one hydrogen bond acceptor, one hydrogen bond donor, one positive ionizable, and one ring aromatic feature for Syk inhibitory activity and AADH.54 proposed the necessity of two hydrogen bond acceptor, one hydrogen bond donor, and one hydrophobic feature for JAK3 inhibitory activity. To our interest, SBPMs identified additional ring aromatic features required for inhibition of both the kinases. For Syk inhibitory activity, the hydrogen bond acceptor feature indicated by LBPM was devoid of forming hydrogen bonding interaction with the hinge region amino acid residue (Ala451). Thus merging the information revealed by both LBPMs and SBPMs, ideal pharmacophore models i.e. ADPRR.14 (Syk) and AADHR.54 (JAK3) were generated. These models after rigorous statistical validation were used for screening of Asinex database. The systematic virtual screening protocol, including pharmacophore and docking-based screening, ADME property, and MM-GBSA energy calculations, retrieved final 10 hits as dual inhibitors of Syk and JAK3. Final 10 hits thus obtained can aid in the development of potential therapeutic agents for autoimmune disorders. Also the top two hits were evaluated against both the enzymes.

Molecular modeling and identification of novel glucokinase activators through stepwise virtual screening

The glucose phosphorylating enzyme glucokinase (GK) is a 50kD monomeric protein having 465 amino acids. It maintains glucose homeostasis inside cells, acts as a glucose sensor in pancreatic β-cells and as a rate controlling enzyme for hepatic glucose clearance and glycogen synthesis. It has two binding sites, one for binding d-glucose and the other for a putative allosteric activator named glucokinase activator (GKA). The GKAs interact with the same region of the GK enzyme that is commonly affected by naturally occurring mutations in humans. However, many GKAs do not bind to GK in the absence of glucose. Recently, it has been reported that GKAs are highly effective in patients with type 2 diabetes mellitus.In this milieu a molecular modeling study has been carried out on three natural variants of GK that lie in the GKA binding site and are known to cause maturity onset diabetes of young (MODY). Additionally, a 10ns molecular dynamics simulation was done on each of the modeled variant in order to explore the flexibility of this site. Subsequently, a systematic virtual screening study was done to identify compounds which can bind with high affinity at GKA binding site of mutant GK.

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.