158 A molecular dynamics simulation-based prediction of deleterious angiogenin mutations causing amyotrophic lateral sclerosis

2013 ◽  
Vol 31 (sup1) ◽  
pp. 103-103
Author(s):  
Aditya K. Padhi ◽  
B. Jayaram ◽  
James Gomes
Keyword(s):  
Molecular Dynamics ◽  
Amyotrophic Lateral Sclerosis ◽  
Molecular Dynamics Simulation ◽  
Dynamics Simulation ◽  
Simulation Based ◽  
Lateral Sclerosis

Screening of natural compounds against SOD1 as a therapeutic target for Amyotrophic lateral sclerosis

2021 ◽  
Vol 19 ◽  
Author(s):  
Sonu Pahal ◽  
Amit Chaudhary ◽  
Sangeeta Singh
Keyword(s):  
Molecular Dynamics ◽  
Amyotrophic Lateral Sclerosis ◽  
Molecular Docking ◽  
Molecular Dynamics Simulation ◽  
Virtual Screening ◽  
Dynamics Simulation ◽  
Pharmacokinetic Parameters ◽  
Wild Type ◽  
Potential Inhibitors ◽  
Lateral Sclerosis

Background: Amyotrophic lateral sclerosis (ALS) is an uncommon and progressive neurological illness that predominantly includes the neurons liable for voluntary muscular activities. Starting from weakness or stiffness in muscles, this gradually exploits the strength and ability to speak, eat, move and even breathe. Its exact mechanism is still not clear, but mutations in the SOD1 gene have been reported to cause ALS, and some studies also found involvement of SOD1 overexpression in the pathogenesis of ALS. As of now, there is no remedy available for its cure. Objective: To identify the potential inhibitors for wild type 1HL5, l113T mutant, and A4V mutant of SOD1 (Superoxide Dismutase 1) protein. Methods: In this study, in silico approaches like virtual screening, molecular docking, pharmacokinetic parameters study, and molecular dynamics simulation were used to identify the best potential inhibitors against wild type and mutant SOD1 protein. Methods: In this study, in silico approaches like virtual screening, molecular docking, pharmacokinetic parameters study, and molecular dynamics simulation were used to identify the best potential inhibitors against wild type and mutant SOD1 protein. Results: On the basis of binding affinity and binding energy, the top three compounds ZINC000095486263, ZINC000095485989, and ZINC000028462577, were observed as the best compounds. In the case of 1HL5, ZINC000095486263 had the highest binding affinity with docking score -10.62 Kcal/mol, 1UXM with ZINC000095485989 had the highest docking score -12.03 Kcal/mol, and 4A7V with ZINC000028462577 was found -11.72 Kcal/mol. Further, Molecular Dynamic simulations (MDS) results showed that the ZINC000095486263, ZINC000095485989, and ZINC000095485956 compounds were formed a stable complex with 1HL5, 1UXM, and 4A7V, respectively Conclusions: : After analyzing the results, we hereby conclude that naturals compounds such as ZINC000095486263, ZINC000095485989, and ZINC000095485956 could be used as a potential inhibitor of 1HL5, 1UXM, and 4A7V, respectively, for ALS treatment and could be used as a drug. Further, In vivo/vitro study of these compounds could be a future direction in the field of drug discovery.

Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Structure Prediction ◽  
Protein Structures ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Model Refinement ◽  
Protein Model ◽  
Lower Accuracy ◽  
Simulation Based

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>

scholarly journals Investigation of the effect of Hydroxychloroquine, Remdesivir, Oseltamivir and some home remedies in the light of Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Keka Talukdar
Keyword(s):  
Molecular Dynamics ◽  
Clinical Trial ◽  
Computer Simulation ◽  
Molecular Dynamics Simulation ◽  
Human Cell ◽  
Physical Modeling ◽  
Chemical Species ◽  
Dynamics Simulation ◽  
Spike Protein ◽  
Simulation Based

Modeling and simulation is another way of finding the interaction between different drugs and chemical species with human cell. Preliminary studies before clinical trial involve computer simulation based on the physical modeling so that clinical trial can be made easier. In many aspects of drug developing, simulation is an essential tool. Here molecular dynamics simulation is performed for the interaction of the spike protein of Covid-19 virus and some of the recently used drugs. Also, the effect of caffeine, theanine, nicotine etc on the virus is found by simulation

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