scholarly journals Identification of Influenza PAN Endonuclease Inhibitors via 3D-QSAR Modeling and Docking-Based Virtual Screening

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7129
Author(s):  
Chao Zhang ◽  
Junjie Xiang ◽  
Qian Xie ◽  
Jing Zhao ◽  
Hong Zhang ◽  
...  

Structural and biochemical studies elucidate that PAN may contribute to the host protein shutdown observed during influenza A infection. Thus, inhibition of the endonuclease activity of viral RdRP is an attractive approach for novel antiviral therapy. In order to envisage structurally diverse novel compounds with better efficacy as PAN endonuclease inhibitors, a ligand-based-pharmacophore model was developed using 3D-QSAR pharmacophore generation (HypoGen algorithm) methodology in Discovery Studio. As the training set, 25 compounds were taken to generate a significant pharmacophore model. The selected pharmacophore Hypo1 was further validated by 12 compounds in the test set and was used as a query model for further screening of 1916 compounds containing 71 HIV-1 integrase inhibitors, 37 antibacterial inhibitors, 131 antiviral inhibitors and other 1677 approved drugs by the FDA. Then, six compounds (Hit01–Hit06) with estimated activity values less than 10 μM were subjected to ADMET study and toxicity assessment. Only one potential inhibitory ‘hit’ molecule (Hit01, raltegravir’s derivative) was further scrutinized by molecular docking analysis on the active site of PAN endonuclease (PDB ID: 6E6W). Hit01 was utilized for designing novel potential PAN endonuclease inhibitors through lead optimization, and then compounds were screened by pharmacophore Hypo1 and docking studies. Six raltegravir’s derivatives with significant estimated activity values and docking scores were obtained. Further, these results certainly do not confirm or indicate the seven compounds (Hit01, Hit07, Hit08, Hit09, Hit10, Hit11 and Hit12) have antiviral activity, and extensive wet-laboratory experimentation is needed to transmute these compounds into clinical drugs.

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Naresh Kandakatla ◽  
Geetha Ramakrishnan

Histone deacetylases 2 (HDAC2), Class I histone deacetylase (HDAC) family, emerged as an important therapeutic target for the treatment of various cancers. A total of 48 inhibitors of two different chemotypes were used to generate pharmacophore model using 3D QSAR pharmacophore generation (HypoGen algorithm) module in Discovery Studio. The best HypoGen model consists of four pharmacophore features namely, one hydrogen bond acceptor (HBA), and one hydrogen donor (HBD), one hydrophobic (HYP) and one aromatic centres, (RA). This model was validated against 20 test set compounds and this model was utilized as a 3D query for virtual screening to validate against NCI and Maybridge database and the hits further screened by Lipinski’s rule of 5, and a total of 382 hit compounds from NCI and 243 hit compounds from Maybridge were found and were subjected to molecular docking in the active site of HDAC2 (PDB: 3MAX). Finally eight hit compounds, NSC108392, NSC127064, NSC110782, and NSC748337 from NCI database and MFCD01935795, MFCD00830779, MFCD00661790, and MFCD00124221 from Maybridge database, were considered as novel potential HDAC2 inhibitors.


2017 ◽  
Vol 1145 ◽  
pp. 278-284 ◽  
Author(s):  
Adib Ghaleb ◽  
Adnane Aouidate ◽  
Mounir Ghamali ◽  
Abdelouahid Sbai ◽  
Mohammed Bouachrine ◽  
...  

2020 ◽  
Author(s):  
Samira Norouzi ◽  
Maryam Farahani ◽  
Samad Nejad Ebrahimi

Background: The current outbreak of Coronavirus Disease 2019 (SARS-CoV-2) led to public health emergencies all over the world and made it a global concern. Also, the lack of an effective treatment to combat this virus is another concern that has appeared. Today, increasing knowledge of biological structures like increasing computer power brings about a chance to use computational methods efficiently in different phases of the drug discovery and development for helping solve this new global problem. Methods: In this study, 3D pharmacophores were generated based on thirty-one structures with functional affinity inhibition (antiviral drugs used for SARS and MERS) with IC50<250 µM from the literature data. A 3D-QSAR model has been developed and validated to be utilized in virtual screening. Results: The best pharmacophore models have been utilized as 3D queries for virtual screening to gain promising inhibitors from a data set of thousands of natural compounds retrieved from PubChem. The hit compounds were subsequently used for molecular docking studies to investigate their affinity to the 3D structure of the SARS-CoV-2 receptors. The ADMET properties calculate for the hits with high binding affinity. Conclusion: The study outcomes can help understand the molecular characteristics and mechanisms of the binding of hit compounds to SARS-CoV-2 receptors and promising identification inhibitors that are likely to be evolved into drugs.


2020 ◽  
Author(s):  
Zizhong Tang ◽  
Lu Huang ◽  
Xiaoli Fu ◽  
Haoxiang Wang ◽  
Biao Tang ◽  
...  

Abstract The FGF/FGFR system may affect tumor cells and stromal microenvironment through autocrine and paracrine stimulation, thereby significantly promoting oncogene transformation and tumor growth. Abnormal expression of FGFR1 in cells is considered to be the main cause of tumorigenesis and a potential target for the treatment of cancer. In this study, a combination of structure-based drug carriers and molecular docking-based virtual screening was used to screen new potential FGFR1 inhibitors. Twenty-one known inhibitors were collected as training sets to establish a 3D-QSAR pharmacophore model, and cost analysis, test set validation, and Fischer randomization test were used to validate the efficiency of the pharmacophore model. In Accelrys Discovery Studio 2016, the zinc database was filtered by Lipinski's Rule of Five and SMART's filtration. Then, Hypo01 was used for virtual screening of ZINC database. Compounds with predicted activity values less than 1 μM were molecularly docked with FGFR1 protein crystals, the docking results were observed, and the interaction between compounds and targets was studied. The absorption, distribution, metabolism and excretion (ADME) and toxicity of potential inhibitors were studied, and a compound with new structural scaffolds were obtained. It could be further studied to explore their better therapeutic effects.


Author(s):  
Arjun Anant ◽  
Kamalpreet Kaur ◽  
Vivek Asati

Background: Thiosemicarbazones belongs to the group of semicarbazides which contains sulfur atom instead of the oxygen atom. Several studies have shown that they are effective against extracellular protozoans like Trichomonas vaginalis, Plasmodium falciparum, Trypanosoma cruzi and other parasites. Objective: The current research involves pharmacophore model design, 3-D-QSAR, virtual screening, and docking studies, all of which are evaluated using various parameters. Methods: The present study was performed by Schrodinger software. A total of 40 ligands were selected for the development of 3D QSAR models. To predict the pIC50 values in 3D-QSAR analysis, the entire dataset was divided into two sets, training and test sets, in a 7:3 ratio. The selected pharmacophore hypothesis has been selected for the virtual screening study. Results: DHHRR_1 emerged as the best pharmacophore model with a survival score of 5.80. The 3D QSAR study showed a significant model with R2 =0.91 and. Q2 = 0.73. The series top-scoring compound 7e had a docking score of -10.44 and showed interactions with the amino acids ARG-265, PHE-227, and LEU-531 required for activity. The developed pharmacophore model has been used for screening of ZINC compounds where ZINC26244107, ZINC13469100, ZINC01290725and ZINC01350173 showed thebest XP docking scores (-11.60, -11.27, -11.35, -10.52, consecutively) with binding important amino acids ARG265, HIE185 and LEU 531 against plasmodium falciparum, PDB ID: 5TBO. These results wereevaluated with thestandard antimalarial drug chloroquine. ADME analysis showed the drug-likeness properties of the compounds. Conclusion: The results of the present study may be helpful for the future development of antimalarial compounds against Plasmodium falciparum.


2019 ◽  
Vol 18 (01) ◽  
pp. 1950002
Author(s):  
Anshika Mittal ◽  
Ritu Arora ◽  
Rita Kakkar

Pharmacophore modeling and 3D-Quantitative Structure Activity Relationship (3D-QSAR) studies have been performed on a dataset of thirty-two quinazoline and aminopyridine derivatives to get an insight into the important structural features required for binding to inducible nitric oxide synthase (iNOS). A four-point CPH (Common Pharmacophore Hypothesis), AHPR.29, with a hydrogen bond acceptor, hydrophobic group, positively charged ionizable group and an aromatic ring, has been obtained as the best pharmacophore model. Satisfactory statistical parameters of correlation ([Formula: see text]) and cross-validated ([Formula: see text]) correlation coefficients, 0.9288 and 0.6353, respectively, show high robustness and good predictive ability of our selected model. The contour maps have been developed from this model and the analysis has provided an interpretable explanation of the effect that various features and substituents have on the potency and selectivity of inhibitors towards iNOS. Docking studies have also been performed in order to analyze the interactions between the enzyme and the inhibitors. Our proposed model can thus be further used for screening a large database of compounds and design new iNOS inhibitors.


2021 ◽  
Author(s):  
Revanth Bathula ◽  
Sree kanth Sivan ◽  
Gururaj Somadi ◽  
Narasimha Muddagoni ◽  
Goverdhan Lanka ◽  
...  

Abstract Protein arginine methyltransferase 5 (PRMT5) is a member of the methyltransferases family, a type II arginine enzyme that is crucial for many cellular processes and is associated with many cancer diseases. In this study, pharmacophore-based 3D QSAR modeling, virtual screening and binding free energy studies were carried out from a set of 61 potent compounds reported being inhibitors of PRMT5 protein. A five-point pharmacophore model (AADHR) was generated and this model is used to generate an atom-based 3-Dimensional quantitative structure-activity relationship (3D-QSAR). The obtained 3D-QSAR model has high correlation coefficient (R2 = 0.91), cross-validation coefficient (Q2 = 0.82), F value (140.3), low RMSE (0.47) and pearson R-value (0.91). A library of 329825 molecules (ChEMBL database) is screened with pharmacophore model to retrieve hit molecules that are further subjected for molecular docking to identify best fit-active conformations binding at the receptor site of PRMT5 protein. Further, we are calculated ADME and toxicity properties using QikProp module and pkCSM server and finally prioritized the lead molecules by binding free energy prediction.


2021 ◽  
Vol 1 (1) ◽  
pp. 25-31
Author(s):  
Achal Mishra ◽  
Radhika Waghela

SARS-CoV-2, a new type of Coronavirus, has affected more millions of people worldwide. From the spread of this infection, many studies related to this virus and drug designing for the treatment have been started. Most of the studies target the SARS-CoV-2 main protease, spike protein of SASR-CoV-2, and some are targeting the human furin protease. In the current work, we chose the clinically used drug molecules remdesivir, favipiravir, lopinavir, hydroxychloroquine, and chloroquine onto the target protein SARS-CoV-2 main protease. Docking studies were performed using Arguslab, while Discovery Studio collected 2D and 3D pose views with the crystal structure of COVID-19 main protease in complex with an inhibitor N3 with PDB ID 6LU7. Computational studies reveal that all ligands provided good binding affinities towards the target protein. Among all the chosen drugs, lopinavir showed the highest docking score of -11.75 kcal/mol. The results from this molecular docking study encourage the use of lopinavir as the first-line treatment drug due to its highest binding affinity.


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