Steered molecular dynamics simulations of protein-ligand interactions

2004 ◽  
Vol 47 (5) ◽  
pp. 355-366 ◽  
Author(s):  
Yechun Xu ◽  
Jianhua Shen ◽  
Xiaomin Luo ◽  
Xu Shen ◽  
Kaixian Chen ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Steered Molecular Dynamics ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Dynamics Simulations

scholarly journals Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11261
Author(s):  
Asim Kumar Bepari ◽  
Hasan Mahmud Reza
Keyword(s):  
Molecular Dynamics ◽  
Hydrogen Bonding ◽  
Virtual Screening ◽  
Molecular Dynamics Simulations ◽  
Mean Square ◽  
Autodock Vina ◽  
Protein Ligand Interactions ◽  
Main Protease ◽  
Ligand Interactions ◽  
Dynamics Simulations

Background The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has ravaged lives across the globe since December 2019, and new cases are still on the rise. Peoples’ ongoing sufferings trigger scientists to develop safe and effective remedies to treat this deadly viral disease. While repurposing the existing FDA-approved drugs remains in the front line, exploring drug candidates from synthetic and natural compounds is also a viable alternative. This study employed a comprehensive computational approach to screen inhibitors for SARS-CoV-2 3CL-PRO (also known as the main protease), a prime molecular target to treat coronavirus diseases. Methods We performed 100 ns GROMACS molecular dynamics simulations of three high-resolution X-ray crystallographic structures of 3CL-PRO. We extracted frames at 10 ns intervals to mimic conformational diversities of the target protein in biological environments. We then used AutoDock Vina molecular docking to virtual screen the Sigma–Aldrich MyriaScreen Diversity Library II, a rich collection of 10,000 druglike small molecules with diverse chemotypes. Subsequently, we adopted in silico computation of physicochemical properties, pharmacokinetic parameters, and toxicity profiles. Finally, we analyzed hydrogen bonding and other protein-ligand interactions for the short-listed compounds. Results Over the 100 ns molecular dynamics simulations of 3CL-PRO’s crystal structures, 6LZE, 6M0K, and 6YB7, showed overall integrity with mean Cα root-mean-square deviation (RMSD) of 1.96 (±0.35) Å, 1.98 (±0.21) Å, and 1.94 (±0.25) Å, respectively. Average root-mean-square fluctuation (RMSF) values were 1.21 ± 0.79 (6LZE), 1.12 ± 0.72 (6M0K), and 1.11 ± 0.60 (6YB7). After two phases of AutoDock Vina virtual screening of the MyriaScreen Diversity Library II, we prepared a list of the top 20 ligands. We selected four promising leads considering predicted oral bioavailability, druglikeness, and toxicity profiles. These compounds also demonstrated favorable protein-ligand interactions. We then employed 50-ns molecular dynamics simulations for the four selected molecules and the reference ligand 11a in the crystallographic structure 6LZE. Analysis of RMSF, RMSD, and hydrogen bonding along the simulation trajectories indicated that S51765 would form a more stable protein-ligand complexe with 3CL-PRO compared to other molecules. Insights into short-range Coulombic and Lennard-Jones potentials also revealed favorable binding of S51765 with 3CL-PRO. Conclusion We identified a potential lead for antiviral drug discovery against the SARS-CoV-2 main protease. Our results will aid global efforts to find safe and effective remedies for COVID-19.

scholarly journals Molecular dynamics simulations of mechanical failure in polymorphic arrangements of amyloid fibrils containing structural defects

2013 ◽  
Vol 4 ◽  
pp. 429-440 ◽  
Author(s):  
Hlengisizwe Ndlovu ◽  
Alison E Ashcroft ◽  
Sheena E Radford ◽  
Sarah A Harris
Keyword(s):  
Mechanical Properties ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Amyloid Fibrils ◽  
Amyloid Fibril ◽  
Steered Molecular Dynamics ◽  
Mechanical Failure ◽  
Structural Defects ◽  
Dynamics Simulations

We examine how the different steric packing arrangements found in amyloid fibril polymorphs can modulate their mechanical properties using steered molecular dynamics simulations. Our calculations demonstrate that for fibrils containing structural defects, their ability to resist force in a particular direction can be dominated by both the number and molecular details of the defects that are present. The simulations thereby suggest a hierarchy of factors that govern the mechanical resilience of fibrils, and illustrate the general principles that must be considered when quantifying the mechanical properties of amyloid fibres containing defects.

scholarly journals Enlighten2: molecular dynamics simulations of protein–ligand systems made accessible

2020 ◽  
Vol 36 (20) ◽  
pp. 5104-5106
Author(s):  
Kirill Zinovjev ◽  
Marc W van der Kamp
Keyword(s):  
Molecular Dynamics ◽  
Expert Knowledge ◽  
Structural Data ◽  
Md Simulations ◽  
Enzyme Engineering ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Dynamics Simulations ◽  
User Friendly ◽  
Dynamical Information

Abstract Motivation Experimental structural data can allow detailed insight into protein structure and protein–ligand interactions, which is crucial for many areas of bioscience, including drug design and enzyme engineering. Typically, however, little more than a static picture of protein–ligand interactions is obtained, whereas dynamical information is often required for deeper understanding and to assess the effect of mutations. Molecular dynamics (MD) simulations can provide such information, but setting up and running these simulations is not straightforward and requires expert knowledge. There is thus a need for a tool that makes protein–ligand simulation easily accessible to non-expert users. Results We present Enlighten2: efficient simulation protocols for protein–ligand systems alongside a user-friendly plugin to the popular visualization program PyMOL. With Enlighten2, non-expert users can straightforwardly run and visualize MD simulations on protein–ligand models of interest. There is no need to learn new programs and all underlying tools are free and open source. Availability and implementation The Enlighten2 Python package and PyMOL plugin are free to use under the GPL3.0 licence and can be found at https://enlighten2.github.io. We also provide a lightweight Docker image via DockerHub that includes Enlighten2 with all the required utilities.

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