scholarly journals Refinement of homology-based protein structures by molecular dynamics simulation techniques

2004 ◽  
Vol 13 (1) ◽  
pp. 211-220 ◽  
Author(s):  
H. Fan
2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>


2017 ◽  
Vol 33 (7) ◽  
pp. 1354-1365 ◽  
Author(s):  
Liao-Ran CAO ◽  
◽  
Chun-Yu ZHANG ◽  
Ding-Lin ZHANG ◽  
Hui-Ying CHU ◽  
...  

2021 ◽  
Author(s):  
Shaban Ahmad ◽  
Piyush Bhanu ◽  
Jitendra Kumar ◽  
Ravi Kant Pathak ◽  
Dharmendra Mallick ◽  
...  

Abstract Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rampant worldwide and is a deadly disease for humans. Our current work emphasizes on molecular dynamics simulation (MDS) targeting nuclear factor-kappa B (NF-κB), the well-known human transcription factor controlling innate and adaptive immunity, to understand its mechanism of action during COVID-19 in humans. NF-κB was interacted with the SARS-CoV-2 spike protein in an in silico MDS experiment, revealing the NF-κB site at which the SARS-CoV-2 spike protein interacts. We screened some known drugs via docking studies on NF-κB used as a receptor. The MDS software Schrodinger generated more than 2000 complexes from these compounds and using the SMILES format of these complexes, 243 structures were extracted and 411 conformers were generated. The drug used as a ligand that docked with NF-κB with the best docking score and binding affinity was Sulindac sodium as its trade name. Furthermore, RMSF data of sulindac sodium and NF-κB displayed minimal fluctuations in the protein structures, and the protein-ligand complex had reduced flexibility. Sulindac sodium is hence suggested as a suitable drug candidate for repurposing in clinical trials for SARS-CoV-2 infections. This drug potently blocked the spike protein’s interaction with NF-κB by inducing a conformational change in the latter. Arguably, NF-κB inaction is desired to have normal immunity and can possibly be retained using proposed drug. This work provides a significant lead for drug repurposing to combat SARS-CoV-2 and its various mutant forms and reveals new approach for controlling SARS-CoV-2-induced disease.


2004 ◽  
Vol 471-472 ◽  
pp. 144-148 ◽  
Author(s):  
Hui Wu ◽  
Bin Lin ◽  
S.Y. Yu ◽  
Hong Tao Zhu

Molecular dynamics (MD) simulation can play a significant role in addressing a number of machining problems at the atomic scale. This simulation, unlike other simulation techniques, can provide new data and insights on nanometric machining; which cannot be obtained readily in any other theory or experiment. In this paper, some fundamental problems of mechanism are investigated in the nanometric cutting with the aid of molecular dynamics simulation, and the single-crystal silicon is chosen as the material. The study showed that the purely elastic deformation took place in a very narrow range in the initial stage of process of nanometric cutting. Shortly after that, dislocation appeared. And then, amorphous silicon came into being under high hydrostatic pressure. Significant change of volume of silicon specimen is observed, and it is considered that the change occur attribute to phase transition from a diamond silicon to a body-centered tetragonal silicon. The study also indicated that the temperature distributing of silicon in nanometric machining exhibited similarity to conventional machining.


2015 ◽  
Vol 11 (11) ◽  
pp. 3068-3080 ◽  
Author(s):  
A. Tomić ◽  
M. Berynskyy ◽  
R. C. Wade ◽  
S. Tomić

A range of molecular dynamics simulation techniques were applied to investigate the DPP III conformational landscape and the influence of ligand binding on the protein structure and dynamics.


2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>


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