scholarly journals Computational modelling of nanotube delivery of anti-cancer drug into glutathione reductase enzyme

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saheen Shehnaz Begum ◽  
Dharitri Das ◽  
Nand Kishor Gour ◽  
Ramesh Chandra Deka
Keyword(s):  
Molecular Dynamics ◽  
Glutathione Reductase ◽  
Active Site ◽  
Density Functional ◽  
Computational Modelling ◽  
Density Functional Theory Method ◽  
Docking Simulation ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Cancer Drug

AbstractDensity functional theory method combined with docking and molecular dynamics simulations are used to understand the interaction of carmustine with human glutathione reductase enzyme. The active site of the enzyme is evaluated by docking simulation is used for molecular dynamics simulation to deliver the carmustine molecule by (5,5) single walled carbon nanotube (SWCNT). Our model of carmustine in the active site of GR gives a negative binding energy that is further refined by QM/MM study in gas phase and solvent phase to confirm the stability of the drug molecule inside the active site. Once released from SWCNT, carmustine forms multiple polar and non-polar hydrogen bonding interactions with Tyr180, Phe209, Lys318, Ala319, Leu320, Leu321, Ile350, Thr352 and Val354 in the range of 2–4 Å. The SWCNT vehicle itself is held fix at its place due to multiple pi-pi stacking, pi-amide, pi-sigma interactions with the neighboring residues. These interactions in the range of 3–5 Å are crucial in holding the nanotube outside the drug binding region, hence, making an effective delivery. This study can be extended to envisage the potential applications of computational studies in the modification of known drugs to find newer targets and designing new and improved controlled drug delivery systems.

scholarly journals Drug Binding Dynamics of the Dimeric SARS-CoV-2 Main Protease, Determined by Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Teruhisa S. KOMATSU ◽  
Noriaki Okimoto ◽  
Yohei M. KOYAMA ◽  
Yoshinori HIRANO ◽  
Gentaro MORIMOTO ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Active Site ◽  
Binding Sites ◽  
Complex Structure ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Trajectory Data ◽  
Main Protease ◽  
Dynamic Sampling

<div> <div> <div> <p>We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein- ligand complexes and suggest the possibilities of further drug optimisations. <br></p><p><br></p><p><br> </p><div> <div> <div> <p>Raw trajectory data analysed in this paper and movie examples are available at the zenodo repository.<br></p> </div> </div> </div> </div> </div> </div>

scholarly journals Drug binding dynamics of the dimeric SARS-CoV-2 main protease, determined by molecular dynamics simulation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Teruhisa S. Komatsu ◽  
Noriaki Okimoto ◽  
Yohei M. Koyama ◽  
Yoshinori Hirano ◽  
Gentaro Morimoto ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Active Site ◽  
Binding Sites ◽  
Complex Structure ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Main Protease ◽  
Encounter Complex ◽  
Dynamic Sampling

Abstract We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein–ligand complexes and suggest the possibilities of further drug optimisations.

scholarly journals Drug Binding Dynamics of the Dimeric SARS-CoV-2 Main Protease, Determined by Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Teruhisa S. KOMATSU ◽  
Noriaki Okimoto ◽  
Yohei M. KOYAMA ◽  
Yoshinori HIRANO ◽  
Gentaro MORIMOTO ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Active Site ◽  
Binding Sites ◽  
Complex Structure ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Trajectory Data ◽  
Main Protease ◽  
Dynamic Sampling

<div> <div> <div> <p>We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein- ligand complexes and suggest the possibilities of further drug optimisations. <br></p><p><br></p><p><br> </p><div> <div> <div> <p>Raw trajectory data analysed in this paper and movie examples are available at the zenodo repository.<br></p> </div> </div> </div> </div> </div> </div>

scholarly journals Drug Binding Dynamics of the Dimeric SARS-CoV-2 Main Protease, Determined by Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Teruhisa S. KOMATSU ◽  
Noriaki Okimoto ◽  
Yohei M. KOYAMA ◽  
Yoshinori HIRANO ◽  
Gentaro MORIMOTO ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Active Site ◽  
Binding Sites ◽  
Complex Structure ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Trajectory Data ◽  
Main Protease ◽  
Dynamic Sampling

<div> <div> <div> <p>We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein- ligand complexes and suggest the possibilities of further drug optimisations. <br></p><p><br></p><p><br> </p><div> <div> <div> <p>Raw trajectory data analysed in this paper and movie examples are available at the zenodo repository.<br></p> </div> </div> </div> </div> </div> </div>

Bayesian active learning of interatomic force field for molecular dynamics simulation of Pt/Ag(111)

2021 ◽  
Author(s):  
Kai Xu ◽  
Lei Yan ◽  
Bingran You
Keyword(s):  
Molecular Dynamics ◽  
Active Learning ◽  
Force Field ◽  
Density Functional ◽  
Process Model ◽  
Large Scale ◽  
Computational Cost ◽  
Dynamics Simulation ◽  
Potential Energy Landscape ◽  
Three Body

Force field is a central requirement in molecular dynamics (MD) simulation for accurate description of the potential energy landscape and the time evolution of individual atomic motions. Most energy models are limited by a fundamental tradeoff between accuracy and speed. Although ab initio MD based on density functional theory (DFT) has high accuracy, its high computational cost prevents its use for large-scale and long-timescale simulations. Here, we use Bayesian active learning to construct a Gaussian process model of interatomic forces to describe Pt deposited on Ag(111). An accurate model is obtained within one day of wall time after selecting only 126 atomic environments based on two- and three-body interactions, providing mean absolute errors of 52 and 142 meV/Å for Ag and Pt, respectively. Our work highlights automated and minimalistic training of machine-learning force fields with high fidelity to DFT, which would enable large-scale and long-timescale simulations of alloy surfaces at first-principles accuracy.

scholarly journals Theoretical Insights into the Electron Capture Behavior of H2SO4···N2O Complex: A DFT and Molecular Dynamics Study

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2349 ◽  
Author(s):  
Wei-Hua Wang ◽  
Wen-Ling Feng ◽  
Wen-Liang Wang ◽  
Ping Li
Keyword(s):  
Molecular Dynamics ◽  
Electron Capture ◽  
Atmospheric Chemistry ◽  
Density Functional ◽  
Molecular Complexes ◽  
Dft Method ◽  
Dynamics Simulation ◽  
Oh Radical ◽  
Before And After ◽  
Reduced Density

Both sulfuric acid (H2SO4) and nitrous oxide (N2O) play a central role in the atmospheric chemistry in regulating the global environment and climate changes. In this study, the interaction behavior between H2SO4 and N2O before and after electron capture has been explored using the density functional theory (DFT) method as well as molecular dynamics simulation. The intermolecular interactions have been characterized by atoms in molecules (AIM), natural bond orbital (NBO), and reduced density gradient (RDG) analyses, respectively. It was found that H2SO4 and N2O can form two transient molecular complexes via intermolecular H-bonds within a certain timescale. However, two molecular complexes can be transformed into OH radical, N2, and HSO4− species upon electron capture, providing an alternative formation source of OH radical in the atmosphere. Expectedly, the present findings not only can provide new insights into the transformation behavior of H2SO4 and N2O, but also can enable us to better understand the potential role of the free electron in driving the proceeding of the relevant reactions in the atmosphere.

Three-dimensional model and molecular dynamics simulation of the active site of the self-splicing intervening sequence of the bacteriophage T4 nrdB messenger RNA

Biochemistry ◽  
1990 ◽  
Vol 29 (45) ◽  
pp. 10317-10322 ◽  
Author(s):  
Lennart Nilsson ◽  
Agneta Aahgren-Staalhandske ◽  
Ann Sofie Sjoegren ◽  
Solveig Hahne ◽  
Britt Marie Sjoeberg
Keyword(s):  
Molecular Dynamics ◽  
Active Site ◽  
Messenger Rna ◽  
Bacteriophage T4 ◽  
Three Dimensional ◽  
The Self ◽  
Dynamics Simulation ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
Self Splicing
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