Glide Docking Research Articles

Identification of Novel Nrf2 Activator via Protein-ligand Interactions as Remedy for Oxidative Stress in Diabetes Mellitus

Background: Oxidative stress is a significant player in the pathogenesis of diabetes mellitus and the Kelch-like ECH-associated protein1/nuclear factor erythroid 2-related factor 2/antioxidant response element (Keap1/Nrf2/ARE) signaling pathway serves as the essential defense system to mitigate oxidative stress. Nrf2 is responsible for the mitigation of oxidative stress while Keap1 represses Nrf2’s activation upon binding. Identification of Nrf2 activators has started to pick up enthusiasm as they can be used as therapeutic agents against diabetes mellitus. One of the ongoing mechanisms in the activation of Nrf2 is to disrupt Keap1/Nrf2 protein-protein interaction. This study aimed at using computational analysis to screen natural compounds capable of inhibiting Keap1/Nrf2 protein-protein interaction. Methods: A manual curated library of natural compounds was screened against crystal structure of Keap1 using glide docking algorithm. Binding free energy of the docked complexes, and adsorption, digestion, metabolism and excretion (ADME) properties were further employed to identify the hit compounds. The bioactivity of the identified hit against Keap1 was predicted using quantitative structure-activity relationship (QSAR) model. Results: A total of 7 natural compounds (Compound 222, 230, 310, 208, 210, 229 and 205) identified from different medicinal plants were found to be potent against Keap1 based on their binding affinity and binding free energy. The internal validated model kpls_radial_30 with R2 of 0.9109, Q2 of 0.7287 was used to predict the compounds’ bioactivities. Compound 205 was considered as the ideal drug candidate because it showed moderation for ADME properties, had predicted pIC50 of 6.614 and obeyed Lipinski’s rule of five. Conclusion: This study revealed that Compound 205, a compound isolated from Amphipterygium adstringens is worth considering for further experimental analysis.

Molecular modelling and structure-activity relationship of a natural derivative of o-hydroxybenzoate as a potent inhibitor of dual NSP3 and NSP12 of SARS-CoV-2: in silico study

The nsp3 macrodomain and nsp12 (RdRp) enzymes are strongly implicated in the virulent regulation of the host immune response and viral replication of SARS-CoV-2, making them plausible therapeutic targets for mitigating infectivity. Remdesivir remains the only FDA-approved small-molecule inhibitor of the nsp12 in clinical conditions while none has been approved yet for the nsp3 macrodomain. In this study, 69,067 natural compounds from the IBScreen database were screened for efficacious potentials with mechanistic multitarget-directed inhibitory pharmacology against the dual targets using in silico approaches. Standard and extra precision (SP and XP) Maestro glide docking analyses were employed to evaluate their inhibitory interactions against the enzymes. Four compounds, STOCK1N-45901, 03804, 83408, 08377 consistently showed high XP scores against the respective targets and interacted strongly with pharmacologically essential amino acid and RNA residues, in better terms than the standard, co-crystallized inhibitors, GS-441524 and remdesivir. Further assessments through the predictions of ADMET and mutagenicity distinguished STOCK1N-45901, a natural derivative of o-hydroxybenzoate as the most promising candidate. The ligand maintained a good conformational and thermodynamic stability in complex with the enzymes throughout the trajectories of 100 ns molecular dynamics, indicated by RMSD, RMSF and radius of gyration plots. Its binding free energy, MM-GBSA was recorded as –54.24 and –31.77 kcal/mol against the respective enzyme, while its structure-activity relationships confer high probabilities as active antiviral, anti-inflammatory, antiinfection, antitussive and peroxidase inhibitor. The IBScreen database natural product, STOCK1N-45901 (2,3,4,5,6-pentahydroxyhexyl o-hydroxybenzoate) is thus recommended as a potent inhibitor of dual nsp3 and nsp12 of SARS-CoV-2 for further study. Communicated by Ramaswamy H. Sarma

Cholesteryl ester transfer protein inhibitory oxoacetamido-benzamide derivatives: Glide docking, pharmacophore mapping, and synthesis

Dyslipidemia is an abnormal lipid profile associated with many common diseases, including coronary heart disease and atherosclerosis. Cholesteryl ester transfer protein (CETP) is a hydrophobic plasma glycoprotein that is responsible for the transfer of cholesteryl ester from high-density lipoprotein athero-protective particles to pro-atherogenic very low-density lipoprotein and low-density lipoprotein particles. The requirement for new CETP inhibitors, which block this process has driven our current work. Here, the synthesis as well as the ligand-based and structure-based design of seven oxoacetamido-benzamides 9a-g with CETP inhibitory activity is described. An in vitro study demonstrated that most of these compounds have appreciable CETP inhibitory activity. Compound 9g showed the highest inhibitory activity against CETP with an IC50 of 0.96 μM. Glide docking data for compounds 9a-g and torcetrapib provide evidence that they are accommodated in the CETP active site where hydrophobic interactions drive ligand/CETP complex formation. Furthermore, compounds 9a-g match the features of known CETP active inhibitors, providing a rationale for their high docking scores against the CETP binding domain. Therefore, these oxoacetamido-benzamides show potential for use as novel CETP inhibitors.

Fluorinated Benzyloxalamides: Glide Docking Pharmacophore Mapping Synthesis and In Vitro Evaluation as Potential Cholesteryl Ester Transfer Protein Inhibitors

Worldwide, cardiovascular disease is considered a major cause of death and disability. Age, sex, blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking and diabetes contribute in the development of the disease. Hence, high-density lipoprotein cholesterol modulation using cholesteryl ester transfer protein inhibitors could have a great role in reducing cardiovascular disease risk and mortality. In this work, ten new benzyloxalamides 8a-j were synthesized and characterized targeting the cholesteryl ester transfer protein inhibition. Biological study results showed that compound 8f had the greatest activity with a percentage inhibition of 64.1 at a concentration of 10 μM and an half-maximal inhibitory concentration of 2.1 μM. Functional moieties of 8a-j matched the cholesteryl ester transfer protein inhibitors pharmacophoric features; mainly the aromatic and hydrophobic ones. Moreover, Glide docking revealed that the targeted compounds lodge the binding site of cholesteryl ester transfer protein and are surrounded by hydrophobic liner. Benzyloxalamides can serve as lead scaffold for the development of cholesteryl ester transfer protein inhibitors.

In silico binding affinity prediction for metabotropic glutamate receptors using both endpoint free energy methods and a machine learning-based scoring function.

Metabotropic glutamate receptors (mGluRs) play an important role in regulating glutamate signal pathways, which are involved in neuropathy and periphery homeostasis. mGluR4, which belongs to Group III mGluRs, is most widely distributed in the periphery among all the mGluRs. It has been proved that the regulation of this receptor is involved in diabetes, colorectal carcinoma and many other diseases. However, the application of structure-based drug design to identify small molecules to regulate the mGluR4 receptor is limited due to the absence of a resolved mGluR4 protein structure. In this work, we first built a homology model of mGluR4 based on a crystal structure of mGluR8, and then conducted hierarchical virtual screening (HVS) to identify possible active ligands for mGluR4. The HVS protocol consists of three hierarchical filters including Glide docking, molecular dynamic (MD) simulation and binding free energy calculation. We successfully prioritized active ligands of mGluR4 from a set of screening compounds using HVS. The predicted active ligands based on binding affinities can almost cover all the experiment-determined active ligands, with only one ligand missed. The correlation between the measured and predicted binding affinities is significantly improved for the MM-PB/GBSA-WSAS methods compared to the Glide docking method. More importantly, we have identified hotspots for ligand binding, and we found that SER157 and GLY158 tend to contribute to the selectivity of mGluR4 ligands, while ALA154 and ALA155 could account for the ligand selectivity to mGluR8. We also recognized other 5 key residues that are critical for ligand potency. The difference of the binding profiles between mGluR4 and mGluR8 can guide us to develop more potent and selective modulators. Moreover, we evaluated the performance of IPSF, a novel type of scoring function trained by a machine learning algorithm on residue-ligand interaction profiles, in guiding drug lead optimization. The cross-validation root-mean-square errors (RMSEs) are much smaller than those by the endpoint methods, and the correlation coefficients are comparable to the best endpoint methods for both mGluRs. Thus, machine learning-based IPSF can be applied to guide lead optimization, albeit the total number of actives/inactives are not big, a typical scenario in drug discovery projects.

Benchmarking Docking Protocols for Virtual Screenings of Novel Acetylcholinesterase Inhibitors

Molecular docking is emerging as a frequently applied structure-based virtual technique in the drug design processes. The method could significantly reduce the time required for the development of novel and effective molecules compared to high-throughput screening. However, a major drawback of the docking simulations is the high number of false-positive ligands in the top-ranked solutions. Thus, this work focuses on the optimization of genetic optimization for GOLD, and Glide docking protocols in the active sites of crystallographic acetylcholinesterase proteins, which could be used in the processes of design and optimization of novel acetylcholinesterase inhibitors. The performance of GOLD and Glide was assessed by their ability to reproduce known inhibitor conformations of co-crystallized ligands, and to efficiently detect known active compounds seeded into a decoy set. In addition, ensemble docking and molecular mechanics with generalised Born and surface area solvation (MM/GBSA) recalculations were introduced to observe the alterations in the enrichment factors. The variances in the enrichment values between both docking softwares were significant, considering the weak performance of Glide. In all of the employed crystallographic structures, GOLD 5.3 showed drastically better results. Interestingly, the enrichment factors were not increased after utilizing the ensemble docking simulations and the free binding energy recalculations with MM/GBSA. Overall, it was noted that the application of ChemPLP (GOLD 5.3) scoring function in a single acetylcholinesterase protein structure (PDB: 1Q84) displays the most reliable docking results. The obtained data will be beneficial for future virtual screenings of novel acetylcholinesterase inhibitors.

Design and Selection of Novel Kinase Inhibitors Implicated in Alzheimer’s Disease by High Throughput Virtual Screening

CDK5 (cyclin dependent kinase 5) is a promising target for treating a variety of neurodegenerative illnesses. The discovery of effective and selective CDK5 inhibitors that engage the polar side chains of the ATP-binding pocket as well as establish specific hydrogen bonds with the kinase has made significant progress. To find novel prospective CDK5 inhibitors with improved effectiveness, ADME characteristics, and a large margin of safety. High throughput virtual screening employing flexible docking was used to screen 2,50,000 compounds from the Specs database against the CDK5 crystal structure (PDB ID: 1UNL). The docking simulation was performed using HTVS first, then SP, and finally XP. The interaction pattern to the receptor with fresh lead candidates has been found and selected based on the GLIDE docking score. The lead moiety in the co-crystallized roscovitine with CDK5 complex retains critical H-bonding patterns while also introducing hydrophobic interactions with Ile10 and Leu133. Selected hits demonstrate hydrophobic interactions with Asn 144, Gly 13, and Ala143 within the ATP cleft in a comparable way to reference compounds (Roscovitine). The lead compound's binding pattern revealed by GLIDE docking experiments demonstrated that molecules bind to the kinase's well-conserved catalytic pocket.

In Silico Screening of Available Drugs Targeting Non-Small Cell Lung Cancer Targets: A Drug Repurposing Approach.

The RAS–RAF–MEK–ERK pathway plays a key role in malevolent cell progression in many tumors. The high structural complexity in the upstream kinases limits the treatment progress. Thus, MEK inhibition is a promising strategy since it is easy to inhibit and is a gatekeeper for the many malignant effects of its downstream effector. Even though MEK inhibitors are under investigation in many cancers, drug resistance continues to be the principal limiting factor to achieving cures in patients with cancer. Hence, we accomplished a high-throughput virtual screening to overcome this bottleneck by the discovery of dual-targeting therapy in cancer treatment. Here, a total of 11,808 DrugBank molecules were assessed through high-throughput virtual screening for their activity against MEK. Further, the Glide docking, MLSF and prime-MM/GBSA methods were implemented to extract the potential lead compounds from the database. Two compounds, DB012661 and DB07642, were outperformed in all the screening analyses. Further, the study results reveal that the lead compounds also have a significant binding capability with the co-target PIM1. Finally, the SIE-based free energy calculation reveals that the binding of compounds was majorly affected by the van der Waals interactions with MEK receptor. Overall, the in silico binding efficacy of these lead compounds against both MEK and PIM1 could be of significant therapeutic interest to overcome drug resistance in the near future.

Novel inhibitors to ADP ribose phosphatase of SARS-CoV-2 identified by structure-based high throughput virtual screening and molecular dynamics simulations

The outbreak of a new coronavirus (SARS-CoV-2) was first identified in Wuhan, People's Republic of China, in 2019, which has led to a severe, life-threatening form of pneumonia (COVID-19). Research scientists all around the world have been trying to find small molecule drugs to treat COVID-19. In the present study, a conserved macrodomain, ADP Ribose phosphatase (ADRP), of a critical non-structural protein (Nsp3) in all coronaviruses was probed using large-scale Molecular Dynamics (MD) simulations to identify novel inhibitors. In our virtual screening workflow, the recently-solved X-ray complex structure, 6W6Y, with a substrate-mimics was used to screen 17 million ZINC15 compounds using drug property filters and Glide docking scores. The top twenty output compounds each underwent 200 ns MD simulations (i.e. 20 × 200 ns) to validate their individual stability as potential inhibitors. Eight out of the twenty compounds showed stable binding modes in the MD simulations, as well as favorable drug properties from our predctions. Therefore, our computational data suggest that the resulting top eight out of twenty compounds could potentially be novel inhibitors to ADRP of SARS-CoV-2.

Virtual Screening and Molecular Dynamics Simulation Study of Influenza Polymerase PB2 Inhibitors.

Influenza A virus is the main cause of worldwide epidemics and annual influenza outbreaks in humans. In this study, a virtual screen was performed to identify compounds that interact with the PB2 cap-binding domain (CBD) of influenza A polymerase. A virtual screening workflow based on Glide docking was used to screen an internal database containing 8417 molecules, and then the output compounds were selected based on solubility, absorbance, and structural fingerprints. Of the 16 compounds selected for biological evaluation, six compounds were identified that rescued cells from H1N1 virus-mediated death at non-cytotoxic concentrations, with EC50 values ranging from 2.5–55.43 μM, and that could bind to the PB2 CBD of H1N1, with Kd values ranging from 0.081–1.53 μM. Molecular dynamics (MD) simulations of the docking complexes of our active compounds revealed that each compound had its own binding characteristics that differed from those of VX-787. Our active compounds have novel structures and unique binding modes with PB2 proteins, and are suitable to serve as lead compounds for the development of PB2 inhibitors. An analysis of the MD simulation also helped us to identify the dominant amino acid residues that play a key role in binding the ligand to PB2, suggesting that we should focus on increasing and enhancing the interaction between inhibitors and these major amino acids during lead compound optimization to obtain more active PB2 inhibitors.

Discovery of a Potent Candidate for RET-Specific Non-Small-Cell Lung Cancer-A Combined In Silico and In Vitro Strategy.

Rearranged during transfection (RET) is a tyrosine kinase oncogenic receptor, activated in several cancers including non-small-cell lung cancer (NSCLC). Multiple kinase inhibitors vandetanib and cabozantinib are commonly used in the treatment of RET-positive NSCLC. However, specificity, toxicity, and reduced efficacy limit the usage of multiple kinase inhibitors in targeting RET protein. Thus, in the present investigation, we aimed to figure out novel and potent candidates for the inhibition of RET protein using combined in silico and in vitro strategies. In the present study, screening of 11,808 compounds from the DrugBank repository was accomplished by different hypotheses such as pharmacophore, e-pharmacophore, and receptor cavity-based models in the initial stage. The results from the different hypotheses were then integrated to eliminate the false positive prediction. The inhibitory activities of the screened compounds were tested by the glide docking algorithm. Moreover, RF score, Tanimoto coefficient, prime-MM/GBSA, and density functional theory calculations were utilized to re-score the binding free energy of the docked complexes with high precision. This procedure resulted in three lead molecules, namely DB07194, DB03496, and DB11982, against the RET protein. The screened lead molecules together with reference compounds were then subjected to a long molecular dynamics simulation with a 200 ns time duration to validate the inhibitory activity. Further analysis of compounds using MM-PBSA and mutation studies resulted in the identification of potent compound DB07194. In essence, a cell viability assay with RET-specific lung cancer cell line LC-2/ad was also carried out to confirm the in vitro biological activity of the resultant compound, DB07194. Indeed, the results from our study conclude that DB07194 can be effectively translated for this new therapeutic purpose, in contrast to the properties for which it was originally designed and synthesized.

Structure-based docking, pharmacokinetic evaluation, and molecular dynamics-guided evaluation of traditional formulation against SARS-CoV-2 spike protein receptor bind domain and ACE2 receptor complex

There is an urgent need for reliable cure and preventive measures in this hour of the outbreak of SARS-CoV-2. Siddha- and Ayurvedic-based classical formulations have antiviral properties and great potential therapeutic choice in this pandemic situation. In the current study, in silico-based analysis for the binding potential of phytoconstituents from the classical formulations suggested by the Ministry of Ayush (Kabasura Kudineer, Shwas Kuthar Rasa with Kantakari and pippali churna, Talisadi churna) to the interface domain of the SARS-CoV-2 receptor-binding domain and angiotensin-converting enzyme 2 was performed. Maestro software from Schrodinger and tools like Glide Docking, induced fit docking, MM-GBSA, molecular dynamics (MD) simulation, and thermal MM-GBSA was used to analyze the binding of protein PDB ID:6VW1 and the selected 133 ligands in comparison with drug molecules like favipiravir and ribavirin. QikProp-based ADMET evaluation of all the phytoconstituents found them nontoxic and with drug-like properties. Selection of top ten ligands was made based on docking score for further MM-GBSA analysis. After performing IFD of top five molecules iso-chlorogenic acid, taxiphyllin, vasicine, catechin and caffeic acid, MD simulation and thermal MM-GBSA were done. Iso-chlorogenic acid had formed more stable interaction with key residue among all phytoconstituents. Computational-based study has highlighted the potential of the many constituents of traditional medicine to interact with the SARS-CoV-2 RBD and ACE2, which might stop the viral entry into the cell. However, in vivo experiments and clinical trials are necessary for supporting this claim.

Assessing the affinity of oxazolidinedione/hydantoin derivatives for the human 5HT1A/2A receptor through homology modeling and docking studies

A flexible docking of a series of heteroaryl compounds to the binding site of a model of human 5-HT1A/2A receptor was exercised using GLIDE docking methods. The resultant docking scores were used to correlate the in vivo affinity data. The GLIDE docking algorithm when used with a homology model of 5HT1A/2A was based on β2- adrenergic receptor template. The influence of structure and hydrophobic properties of aryl moiety on binding affinities was discussed and a model for ligand binding in the hydrophobic part of the binding site was proposed.

Drug repurposing combined with MM/PBSA based validation strategies towards MEK inhibitors screening

Emergence of oncogenic mutations in the MAPK pathway gaining more impact in the recent years. Importantly, MEK is a core element of this pathway as it is easy to inhibit and is a gatekeeper of multiple malignancies. Therefore, we performed in-silico strategy to screen repurposed candidate for MEK protein using a library of 11,808 compounds from different clusters in the DrugBank database. Glide docking, Prime-MM/GBSA and QikProp analysis were implemented to retrieve the hits with high precision. The stability of the binding mode and binding affinity of the resultant hit were explored using molecular dynamic simulations and MM/PBSA approach. The results highlight that Nebivolol (DB04861) not only achieved a stable conformation in the MEK binding pocket but also displayed highest binding affinity than the other molecules investigated in our study. Taken together, we hypothesized that Nebivolol is an excellent candidate for the inhibition of MEK in NSCLC patients in future. Communicated by Ramaswamy H. Sarma

Identification of lead compounds from large natural product library targeting 3C-like protease of SARS-CoV-2 using E-pharmacophore modelling, QSAR and molecular dynamics simulation.

COVID-19 is a novel disease caused by SARS-CoV-2 and has made a catastrophic impact on the global economy. As it is, there is no officially FDA approved drug to alleviate the negative impact of SARS-CoV-2 on human health. Numerous drug targets for neutralizing coronavirus infection have been identified, among them is 3-chymotrypsin-like-protease (3CLpro), a viral protease responsible for the viral replication is chosen for this study. This study aimed at finding novel inhibitors of SARS-CoV-2 3C-like protease from the natural library using computational approaches. A total of 69,000 compounds from natural product library were screened to match a minimum of 3 features from the five sites e-pharmacophore model. Compounds with fitness score of 1.00 and above were consequently filtered by executing molecular docking studies via Glide docking algorithm. Qikprop also predicted the compounds drug-likeness and pharmacokinetic features; besides, the QSAR model built from KPLS analysis with radial as binary fingerprint was used to predict the compounds inhibition properties against SARS-CoV-2 3C-like protease. Fifty ns molecular dynamics (MD) simulation was carried out using GROMACS software to understand the dynamics of binding. Nine (9) lead compounds from the natural products library were discovered; seven among them were found to be more potent than lopinavir based on energies of binding. STOCK1N-98687 with docking score of −9.295 kcal/mol had considerable predicted bioactivity (4.427 µM) against SARS-CoV-2 3C-like protease and satisfactory drug-like features than the experimental drug lopinavir. Post-docking analysis by MM-GBSA confirmed the stability of STOCK1N-98687 bound 3CLpro crystal structure. MD simulation of STOCKIN-98687 with 3CLpro at 50 ns showed high stability and low fluctuation of the complex. This study revealed compound STOCK1N-98687 as potential 3CLpro inhibitor; therefore, a wet experiment is worth exploring to confirm the therapeutic potential of STOCK1N-98687 as an antiviral agent.

Decolourisation of textile dye by laccase: Process evaluation and assessment of its degradation bioproducts

Biodegradation of environmentally hazardous synthetic dyes by enzymes has been achieved the highest interest in recent years. In this work, we optimized Remazol Brilliant Blue R (RBBR) dye biodegradation by Arthrographis kalrae derived laccase via the Box-Behnken design (BBD) approach of the surface response methodology (RSM). Optimization of dye decolourisation by one variable at a time (OVAT) approach resulted in optimal dye decolourisation at laccase dose (2 IU mL−1), pH (7.0), temperature (35 °C), incubation time (240 min), and initial dye concentration (100 mg L-1). The optimized process through BBD enhanced dye decolourisation (97.18%). Fourier Transform Infrared Spectroscopy and UV–Visible Spectrophotometry have proven biodegradation. In addition, in comparison to untreated samples, the laccase-treated dye sample showed relatively less phyto- and cytotoxic effect on Allium cepa L. Extra Precision Glide docking exhibited the binding affinity score of −5.355 kcal mol−1, between laccase-RBBR complex.

In silico molecular docking studies of oxadiazole and pyrimidine bearing heterocyclic compounds as potential antimicrobial agents.

Microbial resistance is a major problem faced by the scientific community. It has created an urgent need to develop antimicrobial agents with novel structures and mechanisms of action. With this aim, a series of novel 1,3,4-oxadiazoles bearing 3,4-dihydropyrimidine heterocyclic motifs 4a-l were designed and synthesized. One-pot Biginelli synthesis is pivotal due to the use of readily available chemicals, shorter reaction time, and ecofriendly synthesis with a good yield. The structures of the synthesized molecules were characterized and confirmed by infrared, 1 H nuclear magnetic resonance (NMR), 13 C NMR, and mass spectroscopic techniques. The title compounds were screened against Gram-positive and -negative strains of bacteria and fungi using the Mueller-Hinton broth method. Compound 4d was found to be the most promising against Escherichia coli (12.5 µg/ml), whereas the same compound showed good activity against Staphylococcus aureus at a concentration of 50 µg/ml. Other compounds of the same series, 4c and 4h, displayed moderate activity against Streptococcus pyogenes at a concentration of 50 µg/ml. Furthermore, results of the antifungal activity tests revealed that compound 4i showed promising activity against all the strains of fungi, Candida albicans, Aspergillus niger, and Aspergillus clavatus, at concentrations of 100, 50, and 100 µg/ml, respectively. Molecular docking also showed that these compounds had a significant binding affinity (Glide docking score: -7.74 to -6.531) for DNA gyrase, engaging in a series of bonded and nonbonded interactions with residues lining the active site. The results of molecular docking study validated the experimental findings, thereby providing an initiation mark to optimize this motif using a structure-based drug design approach.

Glide Docking, Autodock, Binding Free Energy and Drug-Likeness Studies for Prediction of Potential Inhibitors of Cyclin-Dependent Kinase 14 Protein in Wnt Signaling Pathway

Cyclin-dependent kinase 14 plays an essential role in multiple cancers. Cyclin-dependent kinase 14 is a serine/threonine kinase and is a member of the cell division cycle 2(cdc2) related protein kinase family, which plays a key role in promoting Wnt signaling pathway of the cell cycle and its overexpression causes various human cancers. The 3D structure of cyclin-dependent kinase 14 was built using the homology-based modeling technique. The generated model is optimized by NAMD-VMD software. The quality of stabilized CDK14 protein was checked using Ramachandran plot and ProSA servers. The potential binding site region was recognized using SiteMap and manual correlation techniques from literature studies. The virtual screening was performed with the TOSLab database of 27253 output molecules against CDK14 protein using Glide docking to assess novel chemical entities. Their binding energies were calculated from PrimeMMGSA and AutoDock. The novel lead molecules have been prioritized based on efficient binding energies (from AutoDock and PrimeMMGBSA), better glide scores, good bioavailability, and acceptable ADME properties. Thus, these are considered as CDK14 protein inhibitors for cancer therapeutics.

In Vitro Identification and In Vivo Confirmation of Inhibitors for Sweet Potato Chlorotic Stunt Virus RNA Silencing Suppressor, a Viral RNase III.

ABSTRACTSweet potato virus disease (SPVD), caused by synergistic infection of Sweet potato chlorotic stunt virus (SPCSV) and Sweet potato feathery mottle virus (SPFMV), is responsible for substantial yield losses all over the world. However, there are currently no approved treatments for this severe disease. The crucial role played by RNase III of SPCSV (CSR3) as an RNA silencing suppressor during the viruses’ synergistic interaction in sweetpotato makes it an ideal drug target for developing antiviral treatment. In this study, high-throughput screening (HTS) of small molecular libraries targeting CSR3 was initiated by a virtual screen using Glide docking, allowing the selection of 6,400 compounds out of 136,353. We subsequently developed and carried out kinetic-based HTS using fluorescence resonance energy transfer technology, which isolated 112 compounds. These compounds were validated with dose-response assays including kinetic-based HTS and binding affinity assays using surface plasmon resonance and microscale thermophoresis. Finally, the interference of the selected compounds with viral accumulation was verified in planta. In summary, we identified five compounds belonging to two structural classes that inhibited CSR3 activity and reduced viral accumulation in plants. These results provide the foundation for developing antiviral agents targeting CSR3 to provide new strategies for controlling sweetpotato virus diseases.IMPORTANCE We report here a high-throughput inhibitor identification method that targets a severe sweetpotato virus disease caused by coinfection with two viruses (SPCSV and SPFMV). The disease is responsible for up to 90% yield losses. Specifically, we targeted the RNase III enzyme encoded by SPCSV, which plays an important role in suppressing the RNA silencing defense system of sweetpotato plants. Based on virtual screening, laboratory assays, and confirmation in planta, we identified five compounds that could be used to develop antiviral drugs to combat the most severe sweetpotato virus disease.

Systematic comparison of ligand-based and structure-based virtual screening methods on poly (ADP-ribose) polymerase-1 inhibitors

The poly (ADP-ribose) polymerase-1 (PARP1) has been regarded as a vital target in recent years and PARP1 inhibitors can be used for ovarian and breast cancer therapies. However, it has been realized that most of PARP1 inhibitors have disadvantages of low solubility and permeability. Therefore, by discovering more molecules with novel frameworks, it would have greater opportunities to apply it into broader clinical fields and have a more profound significance. In the present study, multiple virtual screening (VS) methods had been employed to evaluate the screening efficiency of ligand-based, structure-based and data fusion methods on PARP1 target. The VS methods include 2D similarity screening, structure-activity relationship (SAR) models, docking and complex-based pharmacophore screening. Moreover, the sum rank, sum score and reciprocal rank were also adopted for data fusion methods. The evaluation results show that the similarity searching based on Torsion fingerprint, six SAR models, Glide docking and pharmacophore screening using Phase have excellent screening performance. The best data fusion method is the reciprocal rank, but the sum score also performs well in framework enrichment. In general, the ligand-based VS methods show better performance on PARP1 inhibitor screening. These findings confirmed that adding ligand-based methods to the early screening stage will greatly improve the screening efficiency, and be able to enrich more highly active PARP1 inhibitors with diverse structures.