scholarly journals Identification of potential inhibitors for Sterol C-24 reductase of Leishmania donovani through virtual screening of natural compounds

BIOCELL ◽  
2021 ◽  
Vol 45 (6) ◽  
pp. 1601-1610
Author(s):  
FAZLUR RAHMAN ◽  
SHAMS TABREZ ◽  
RAHAT ALI ◽  
SAJJADUL KADIR AKAND ◽  
MOHAMMED A. ALAIDAROUS ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Leishmania Donovani ◽  
Natural Compounds ◽  
Potential Inhibitors

Machine Learning-based Virtual Screening Strategy Reveals Some Natural Compounds As Potential PAK4 Inhibitors In Triple Negative Breast Cancer

2020 ◽  
Vol 18 ◽  
Author(s):  
Opeyemi Iwaloye ◽  
Olusola Olalekan Elekofehinti ◽  
Babatomiwa Kikiowo ◽  
Emmanuel Ayo Oluwarotimi ◽  
Toyin Mary Fadipe
Keyword(s):  
Breast Cancer ◽  
Virtual Screening ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Binding Free Energy ◽  
Natural Compounds ◽  
Energy Calculation ◽  
Reference Drug ◽  
Predictive Quality

Background: P-21 activating kinase 4 (PAK4) is implicated in poor prognosis of many cancers, especially in the progression of Triple Negative Breast Cancer (TNBC). The present study was aimed at designing some potential drug candidates as PAK4 inhibitors for breast cancer therapy. Objective: This study aimed to finding novel inhibitors of PAK4 from natural compounds using computational approach. Methods: An e-pharmacophore model was developed from docked PAK4-coligand complex and used to screen over a thousand natural compounds downloaded from BIOFACQUIM and NPASS databases to match a minimum of 5 sites for selected (ADDDHRR) hypothesis. The robustness of the virtual screening method was accessed by well-established methods including EF, ROC, BEDROC, AUAC, and the RIE. Compounds with fitness score greater than one were filtered by applying molecular docking (HTVS, SP, XP and Induced fit docking) and ADME prediction. Using Machine learningbased approach QSAR model was generated using Automated QSAR. The computed top model kpls_des_17 (R2= 0.8028, RMSE = 0.4884 and Q2 = 0.7661) was used to predict the pIC50 of the lead compounds. Internal and external validations were accessed to determine the predictive quality of the model. Finally the binding free energy calculation was computed. Results: The robustness/predictive quality of the models were affirmed. The hits had better binding affinity than the reference drug and interacted with key amino acids for PAK4 inhibition. Overall, the present analysis yielded three potential inhibitors that are predicted to bind with PAK4 better than reference drug tamoxifen. The three potent novel inhibitors vitexin, emodin and ziganein recorded IFD score of -621.97 kcal/mol, -616.31 kcal/mol and -614.95 kcal/mol, respectively while showing moderation for ADME properties and inhibition constant. Conclusion: It is expected that the findings reported in this study may provide insight for designing effective and less toxic PAK4 inhibitors for triple negative breast cancer.

The Research of New Inhibitors of Bacterial Methionine Aminopeptidase by Structure Based Virtual Screening Approach of ZINC DATABASE and In Vitro Validation

2020 ◽  
Vol 16 (4) ◽  
pp. 389-401 ◽  
Author(s):  
Hanane Boucherit ◽  
Abdelouahab Chikhi ◽  
Abderrahmane Bensegueni ◽  
Amina Merzoug ◽  
Jean-Michel Bolla
Keyword(s):  
Virtual Screening ◽  
Bacterial Survival ◽  
Methionine Aminopeptidase ◽  
Bacterial Strains ◽  
Microdilution Method ◽  
Zinc Database ◽  
Promising Target ◽  
New Antibiotics ◽  
Potential Inhibitors

Background: The great emergence of multi-resistant bacterial strains and the low renewal of antibiotics molecules are leading human and veterinary medicine to certain therapeutic impasses. Therefore, there is an urgent need to find new therapeutic alternatives including new molecules in the current treatments of infectious diseases. Methionine aminopeptidase (MetAP) is a promising target for developing new antibiotics because it is essential for bacterial survival. Objective: To screen for potential MetAP inhibitors by in silico virtual screening of the ZINC database and evaluate the best potential lead molecules by in vitro studies. Methods: We have considered 200,000 compounds from the ZINC database for virtual screening with FlexX software to identify potential inhibitors against bacterial MetAP. Nine chemical compounds of the top hits predicted were purchased and evaluated in vitro. The antimicrobial activity of each inhibitor of MetAP was tested by the disc-diffusion assay against one Gram-positive (Staphylococcus aureus) and two Gram-negative (Escherichia coli & Pseudomonas aeruginosa) bacteria. Among the studied compounds, compounds ZINC04785369 and ZINC03307916 showed promising antibacterial activity. To further characterize their efficacy, the minimum inhibitory concentration was determined for each compound by the microdilution method which showed significant results. Results: These results suggest compounds ZINC04785369 and ZINC03307916 as promising molecules for developing MetAP inhibitors. Conclusion: Furthermore, they could therefore serve as lead molecules for further chemical modifications to obtain clinically useful antibacterial agents.

scholarly journals Identification of New Potential Inhibitors of Quorum Sensing through a Specialized Multi-Level Computational Approach

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2600
Author(s):  
Fábio G. Martins ◽  
André Melo ◽  
Sérgio F. Sousa
Keyword(s):  
Virtual Screening ◽  
Quorum Sensing ◽  
In Silico ◽  
Bacterial Infections ◽  
Extracellular Polymeric Substances ◽  
Free Energy Calculations ◽  
Ligand Docking ◽  
Screening Tests ◽  
Scoring Functions ◽  
Potential Inhibitors

Biofilms are aggregates of microorganisms anchored to a surface and embedded in a self-produced matrix of extracellular polymeric substances and have been associated with 80% of all bacterial infections in humans. Because bacteria in biofilms are less amenable to antibiotic treatment, biofilms have been associated with developing antibiotic resistance, a problem that urges developing new therapeutic options and approaches. Interfering with quorum-sensing (QS), an important process of cell-to-cell communication by bacteria in biofilms is a promising strategy to inhibit biofilm formation and development. Here we describe and apply an in silico computational protocol for identifying novel potential inhibitors of quorum-sensing, using CviR—the quorum-sensing receptor from Chromobacterium violaceum—as a model target. This in silico approach combines protein-ligand docking (with 7 different docking programs/scoring functions), receptor-based virtual screening, molecular dynamic simulations, and free energy calculations. Particular emphasis was dedicated to optimizing the discrimination ability between active/inactive molecules in virtual screening tests using a target-specific training set. Overall, the optimized protocol was used to evaluate 66,461 molecules, including those on the ZINC/FDA-Approved database and to the Mu.Ta.Lig Virtual Chemotheca. Multiple promising compounds were identified, yielding good prospects for future experimental validation and for drug repurposing towards QS inhibition.

Sequence analysis, structure prediction of receptor proteins and In silico study of potential inhibitors for management of life threatening COVID-19

2021 ◽  
Vol 18 ◽  
Author(s):  
Hriday K. Basak ◽  
Soumen Saha ◽  
Joydeep Ghosh ◽  
Uttam Paswan ◽  
Sujoy Karmakar ◽  
...  
Keyword(s):  
Binding Site ◽  
Ursolic Acid ◽  
Density Functional ◽  
Actinomycin D ◽  
3D Structure ◽  
Natural Compounds ◽  
Surface Glycoprotein ◽  
Basis Set ◽  
3D Structures ◽  
Potential Inhibitors

Background: Treatment of the Covid-19 pandemic caused by the highly contagious and pathogenic SARS-CoV-2 is a global menace. Day by day this pandemic is getting worse. Doctors, Scientists and Researchers across the world are urgently scrambling for a cure for novel corona virus and continuously working at break neck speed to develop vaccine or drugs. But to date, there are no specific drugs or vaccine available in the market to cope up the virus. Objective: The present study helps us to elucidate 3D structures of SARS-CoV-2 proteins and also to identify best natural compounds as potential inhibitors against COVID-19. Methods: The 3D structures of the proteins were constructed using Modeller 9.16 modeling tool. Modelled proteins were validated with PROCHECK by Ramachandran plot analysis. In this study a small library of natural compounds (fifty compounds) was docked to the ACE2 binding site of the modelled surface glycoprotein of SARS-CoV-2 using Auto Dock Vina to repurpose these inhibitors for SARS-CoV-2. Conceptual density functional theory calculations of best eight compounds had been performed by Gaussian-09. Geometry optimizations for these molecules were done at M06-2X/ def2-TZVP level of theory. ADME parameters, pharmacokinetic properties and drug likeliness of the compounds were analyzed in the swissADME website. Results: In this study we analysed the sequences of surface glycoprotein, nucleocapsid phosphoprotein and envelope protein obtained from different parts of the globe. We have modelled all the different sequences of surface glycoprotein and envelop protein in order to derive 3D structure of a molecular target which is essential for the development of therapeutics. Different electronic properties of the inhibitors have been calculated using DFT through M06-2X functional with def2-TZVP basis set. Docking result at the hACE2 binding site of all modelled surface glycoproteins of SARS-CoV-2 showed that all the eight inhibitors (Actinomycin D, avellanin C, ichangin, kanglemycin A, obacunone, ursolic acid, ansamiotocin P-3 and isomitomycin A) studied here many folds better compared to hydroxychloroquine which has been found to be effective to treat patients suffering fromCOVID-19 pandemic. All the inhibitors meet most of criteria of drug likeness assessment. Conclusion: We will expect that eight compounds (Actinomycin D, avellanin C, ichangin, kanglemycin A, obacunone, ursolic acid, ansamiotocin P-3 and isomitomycin A) can be used as potential inhibitors against SARS-CoV-2.

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