scholarly journals Molecular Docking Studies of Phytocompounds from Aloe vera (L.) Burm.f. having Anticancer Property, against an Antiapoptotic Bcl-2 Protein

2017 ◽  
Vol 14 (4) ◽  
pp. 1449-1456
Author(s):  
Dhanya Narayanan Nair ◽  
S. Padmavathy
Keyword(s):  
Molecular Docking ◽  
Aloe Vera ◽  
Scoring Function ◽  
Binding Pocket ◽  
Docking Studies ◽  
Total Binding Energy ◽  
Docking Result ◽  
Function Calculation ◽  
Potential Inhibitors ◽  
Pi Interactions

ABSTRACT: B-cell lymphocyte-2 (Bcl-2) is an antiapoptotic protein, which is an important member of Bcl-2 family. The current study involves molecular docking of six antineoplastic phytocompounds from Aloe vera (L.) Burm.f. against the protein Bcl-2. Docetaxel, a known inhibitor of Bcl-2 was used as a control in this study. All the studied phytocompounds bound within the same binding pocket as that of Docetaxel and thus can be considered as potential inhibitors of Bcl-2 protein. Among the six phytocompounds studied, AVG4 showed the best docking result, with a minimum pharmacological energy, -198.9 kcal/mol, followed by AVG6 and AVG3 as the second and third best phytocompound while AVL3 has the maximum pharmacological energy -103.8 kcal/mol. AVL3 is involved in cation-pi interactions with the Tyr9 residue of the Bcl-2 protein which is not considered while calculating pharmacological energy scoring function. Calculation of energy due to cation-pi interactions may result in the increase in total binding energy of AVL3, which may significantly increase the pharmacological energy, EPharma by approximately -8 kcal/mol, resulting in another potential anticancer phytocompound.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals Synthesis, antimalarial activity evaluation and molecular docking studies of some novel dispiro-1,2,4,5-tetraoxanes

2015 ◽  
Vol 10 (4) ◽  
pp. 917 ◽  
Author(s):  
Mukesh Kumar Kumawat ◽  
Dipak Chetia
Keyword(s):  
Plasmodium Falciparum ◽  
Molecular Docking ◽  
Resistant Strain ◽  
Antimalarial Activity ◽  
Binding Pocket ◽  
Docking Studies ◽  
Spectroscopic Techniques ◽  
Molecular Docking Studies ◽  
Activity Screening

<p class="Abstract">Seven novel dispiro-1,2,4,5-tetraoxane derivatives were synthesized and characterized by a number of analytical and spectroscopic techniques. The molecules were subsequently screened for in vitro antimalarial activity against chloroquine resistant strain of <em>Plasmodium falciparum</em> (RKL-9). At antimalarial activity screening, two compounds, namely 5d (MIC = 15.6 µg/mL or 64.5 µM) and 5f (MIC = 15.6 µg/mL or 54.6 µM) were found to be about 1.5 times more potent against chloroquine resistant strain-RKL-9 compared to chloroquine (MIC = 25.0 µg/mL or 78.3 µM). Molecular docking studies of potent ligands were also performed in cysteine protease binding pocket residues of falcipain-2 as a target protein.</p><p> </p>

scholarly journals MOLECULAR DOCKING STUDIES ON SCREENING AND ASSESSMENT OF SELECTED BIOFLAVONOIDS AS POTENTIAL INHIBITORS OF COVID-19 MAIN PROTEASE

2020 ◽  
pp. 174-178
Author(s):  
SHAILENDRA SANJAY SURYAWANSHI ◽  
POOJA BHAVAKANA JAYANNACHE ◽  
RAJKUMAR SANJAY PATIL ◽  
PALLED MS ◽  
ALEGAON SG
Keyword(s):  
Molecular Docking ◽  
Treatment Options ◽  
Docking Studies ◽  
Binding Energies ◽  
Molecular Docking Studies ◽  
Discovery Studio ◽  
Main Protease ◽  
Potential Inhibitors ◽  
Binding Energy Calculations ◽  
Native Ligand

Objectives: The objective of the study was to screen and assess the selected bioactive bioflavonoids in medicinal plants as potential coronaviruses (CoV) main protease (Mpro) inhibitors using molecular docking studies. Methods: We have investigated several bioflavonoids which include apigenin, galangin, glycitein, luteolin, morin, naringin, resveratrol, and rutin. Nelfinavir and lopinavir were used as standard antiviral drugs for comparison. Mpro was docked with selected compounds using PyRx 0.8 and docking was analyzed by PyRx 0.8 and Biovia Discovery Studio 2019. Results: The binding energies obtained from the docking of 6LU7 with native ligand, nelfinavir, lopinavir, apigenin, galangin, glycitein, luteolin, morin, naringin, resveratrol, and rutin were found to be −7.4, −8.3, −8.0, −7.8, −7.3, −7, −7.4, −7.6, −7.8, −6.9, and −9 kcal/mol, respectively. Conclusion: From the binding energy calculations, we can conclude that nelfinavir and lopinavir may represent potential treatment options and apigenin, galangin, glycitein, luteolin, morin, naringin, resveratrol, and rutin found to possess the best inhibitors of CoV disease-19 main protease.

scholarly journals GNINA 1.0: Molecular Docking with Deep Learning

2021 ◽  
Author(s):  
Andrew McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2A root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under and open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals Novel inhibitors designing against the potential drug target of Plasmodium falciparum M17 Leucyl Aminopeptidase - PfM17LAP

2021 ◽  
Author(s):  
Govinda Rao Dabburu ◽  
Manish Kumar ◽  
Naidu Subbarao
Keyword(s):  
Plasmodium Falciparum ◽  
Molecular Docking ◽  
Drug Target ◽  
Docking Studies ◽  
Simulation Studies ◽  
Docking Analysis ◽  
Discovery Studio ◽  
Potential Inhibitors ◽  
Key Words Malaria ◽  
Leucyl Aminopeptidase

Abstract: Malaria is one of the major disease of concern worldwide especially in the African regions. According to the recent WHO reports, African regions share 95% of the total deaths worldwide that occurs due to malaria. Plasmodium falciparum M17 Leucyl Aminopeptidase (PfM17LAP) plays an important role in the regulation of amino acids release and for the survival of the parasite. We performed molecular docking and simulation studies to find the potential inhibitors against PfM17LAP using ChEMBL antimalarial library. Molecular docking studies and post-docking analysis revealed that molecules CHEMBL369831 and CHEMBL176888 showed better binding than the reference molecule BESTATIN. LibDock and X-SCORES of molecules BES, CHEMBL369831 and CHEMBL176888 are 130.071, 230.38, 223.56 and -8.75 Kcal/mol, -10.90 Kcal/mol, -11.05 Kcal/mol respectively. ADMET profiling of the top ten ranked molecules was done by using the Discovery Studio. Molecular dynamic studies revealed that the complex PfM17LAP-CHEMBL369831 is stable throughout the simulation. Finally, we have reported novel inhibitors which possess more binding affinity towards PfM17LAP. Key words: Malaria, M17 Leucyl Aminopeptidase, ADMET, X-SCORE

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