scholarly journals Prediction of Deleterious Non-synonymous SNPs of Human STK11 Gene by Combining Algorithms, Molecular Docking, and Molecular Dynamics Simulation

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Md. Jahirul Islam ◽  
Akib Mahmud Khan ◽  
Md. Rimon Parves ◽  
Md Nayeem Hossain ◽  
Mohammad A. Halim
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Protein Structures ◽  
Catalytic Efficiency ◽  
Suppressor Gene ◽  
Kinase Domain ◽  
Dynamics Simulation ◽  
Nucleotide Polymorphisms ◽  
Stk11 Gene ◽  
The Impact

Abstract Serine-threonine kinase11 (STK11) is a tumor suppressor gene which plays a key role in regulating cell growth and apoptosis. It is widely known as a multitasking kinase and engaged in cell polarity, cell cycle arrest, chromatin remodeling, energy metabolism, and Wnt signaling. The substitutions of single amino acids in highly conserved regions of the STK11 protein are associated with Peutz–Jeghers syndrome (PJS), which is an autosomal dominant inherited disorder. The abnormal function of the STK11 protein is still not well understood. In this study, we classified disease susceptible single nucleotide polymorphisms (SNPs) in STK11 by using different computational algorithms. We identified the deleterious nsSNPs, constructed mutant protein structures, and evaluated the impact of mutation by employing molecular docking and molecular dynamics analysis. Our results show that W239R and W308C variants are likely to be highly deleterious mutations found in the catalytic kinase domain, which may destabilize structure and disrupt the activation of the STK11 protein as well as reduce its catalytic efficiency. The W239R mutant is likely to have a greater impact on destabilizing the protein structure compared to the W308C mutant. In conclusion, these mutants can help to further realize the large pool of disease susceptibilities linked with catalytic kinase domain activation of STK11 and assist to develop an effective drug for associated diseases.

scholarly journals IntegratingIn SilicoPrediction Methods, Molecular Docking, and Molecular Dynamics Simulation to Predict the Impact of ALK Missense Mutations in Structural Perspective

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
C. George Priya Doss ◽  
Chiranjib Chakraborty ◽  
Luonan Chen ◽  
Hailong Zhu
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulation ◽  
In Silico ◽  
Drug Response ◽  
Genomic Medicine ◽  
Dynamics Simulation ◽  
Missense Mutations ◽  
Anaplastic Lymphoma ◽  
The Impact

Over the past decade, advancements in next generation sequencing technology have placed personalized genomic medicine upon horizon. Understanding the likelihood of disease causing mutations in complex diseases as pathogenic or neutral remains as a major task and even impossible in the structural context because of its time consuming and expensive experiments. Among the various diseases causing mutations, single nucleotide polymorphisms (SNPs) play a vital role in defining individual’s susceptibility to disease and drug response. Understanding the genotype-phenotype relationship through SNPs is the first and most important step in drug research and development. Detailed understanding of the effect of SNPs on patient drug response is a key factor in the establishment of personalized medicine. In this paper, we represent a computational pipeline in anaplastic lymphoma kinase (ALK) for SNP-centred study by the application ofin silicoprediction methods, molecular docking, and molecular dynamics simulation approaches. Combination of computational methods provides a way in understanding the impact of deleterious mutations in altering the protein drug targets and eventually leading to variable patient’s drug response. We hope this rapid and cost effective pipeline will also serve as a bridge to connect the clinicians andin silicoresources in tailoring treatments to the patients’ specific genotype.

Prediction of Deleterious nsSNPs Causing CHARGE Syndrome Associated with the CHD7 Protein using Computational Approaches

2021 ◽  
Author(s):  
Nithya S. Rathinam ◽  
Madhana Priya N ◽  
Magesh Ramasamy
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Stability Analysis ◽  
Molecular Dynamics Simulation ◽  
Computational Methods ◽  
Catalytic Domain ◽  
Computational Prediction ◽  
Charge Syndrome ◽  
Dynamics Simulation ◽  
The Impact

Abstract The Chromo domain helicase DNA binding protein 7 (CHD7) is also known as ATP-dependent helicase CHD7, in humans, the CHD7 gene encodes it. Heterozygous mutations in this protein cause aggregation and has been determined to have an adverse role in causing CHARGE syndrome. Non-synonymous single nucleotide polymorphism (nsSNP) analysis tends to be deleterious of the protein yet to be employed with computational methods though being the highlight for novel investigations. Various computational methods were used to categorize the 201 identified nsSNPs in the catalytic domain of the CHD7 protein (the nsSNPs are observed to have a damaging effect in the catalytic domain). Pathogenicity analysis determined 81 nsSNPs to be pathogenic and further narrowed down to 61 nsSNPs by stability analysis. Based on the structure availability, the two nsSNPs (P2683S and R2702C) were selected and were checked in the computational tools for sequence analysis (pathogenicity analysis, stability analysis, physiochemical property analysis, and conservational analysis) and were determined to have a high impact over the protein molecule. The molecular dynamics simulation and principal component analysis (PCA) were performed to determine the conformational stability and flexibility change of the proteins. Subsequently, a molecular dynamic simulation (MDS) for 100ns was performed to understand the impact of the differences between the native and the mutant structures of the CHD7 protein. The simulation plots disclose very minute changes in patterns of stability, residue fluctuation, structure compactness, and flexibility regarding P2683S and R2702C mutation compared to the native structure. Further, Molecular docking was performed for the native and the mutant structures P2683S and R2702C to study the binding efficacy of the drugs Methyltestosterone and Estradiol resulting in a similar score with a very little difference to each other. The Native and mutants P2683S and R2702C have similar interaction of -5.7 kcal/mol, -5.9 kcal/mol and − 5.6 kcal/mol respectively with Methyltestosterone followed by a binding score of -6 kcal/mol, -5.6 kcal/mol and − 5.8 kcal/mol respectively for Estradiol. Detailed study about the disease, effect of nsSNP’s and the response of the drug towards the mutation are the key factors in order to launch a new personalized medicine. Therefore, in this study using various computational prediction methods, molecular dynamics simulation and molecular docking studies we have determined the nsSNP’s responsible to cause CHARGE syndrome and the drug response with respect to the determined nsSNP mutations. The outcomes acquired from our investigation will provide the data for experimental biologists for the additional procedure for examining the rest of the variations in CDH7 protein.

scholarly journals Prediction and Expression Analysis of Deleterious Nonsynonymous SNPs of Arabidopsis ACD11 Gene by Combining Computational Algorithms and Molecular Docking Approach

2021 ◽  
Author(s):  
Mahmudul Hasan Rifat ◽  
Jamil Ahmed ◽  
Milad Ahmed ◽  
Foeaz Ahmed ◽  
Airin Gulshan ◽  
...  
Keyword(s):  
Cell Death ◽  
Molecular Docking ◽  
Protein Structures ◽  
Protein Domain ◽  
Nonsynonymous Snps ◽  
Nucleotide Polymorphisms ◽  
Defensive Responses ◽  
Leaf Chlorosis ◽  
Docking Approach ◽  
The Impact

Accelerated cell death 11 (ACD11) is an autoimmune gene that suppresses pathogen infection in plants by preventing plant cells from becoming infected by any pathogen. This gene is widely known for growth inhibition, premature leaf chlorosis, and defense-related programmed cell death (PCD) in seedlings before flowering in Arabidopsis plant. Specific amino acid changes in the ACD11 protein's highly conserved domains are linked to autoimmune symptoms including constitutive defensive responses and necrosis without pathogen awareness. The molecular aspect of the aberrant activity of the ACD11 protein is difficult to ascertain. The purpose of our study was to find the most deleterious mutation position in the ACD11 protein and correlate them with their abnormal expression pattern. Using several computational methods, we discovered PCD vulnerable single nucleotide polymorphisms (SNPs) in ACD11. We analysed the RNA-Seq data, identified the detrimental nonsynonymous SNPs (nsSNP), built genetically mutated protein structures and used molecular docking to assess the impact of mutation. Our results demonstrated that the A15T and A39D variations in the GLTP domain were likely to be extremely detrimental mutations that inhibit the expression of the ACD11 protein domain by destabilizing its composition, as well as disrupt its catalytic effectiveness. When compared to the A15T mutant, the A39D mutant was more likely to destabilize the protein structure. In conclusion, these mutants can aid in the better understanding of the vast pool of PCD susceptibilities connected to ACD11 gene GLTP domain activation.

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>

Exploring the effect of inhibitor AKB‐9778 on VE‐PTP by molecular docking and molecular dynamics simulation

2019 ◽  
Vol 120 (10) ◽  
pp. 17015-17029 ◽  
Author(s):  
Wen‐Shan Liu ◽  
Rui‐Rui Wang ◽  
Ying‐Zhan Sun ◽  
Wei‐Ya Li ◽  
Hong‐Lian Li ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulation ◽  
Dynamics Simulation

scholarly journals The Impact of the Electric Field on Surface Condensation of Water Vapor: Insight from Molecular Dynamics Simulation

Nanomaterials ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 64 ◽  
Author(s):  
Qin Wang ◽  
Hui Xie ◽  
Zhiming Hu ◽  
Chao Liu
Keyword(s):  
Molecular Dynamics ◽  
Water Vapor ◽  
Electric Field ◽  
Intermolecular Forces ◽  
Dynamics Simulation ◽  
Water Molecules ◽  
Attraction Force ◽  
Condensation Rate ◽  
Shape Transformation ◽  
The Impact

In this study, molecular dynamics simulations were carried out to study the coupling effect of electric field strength and surface wettability on the condensation process of water vapor. Our results show that an electric field can rotate water molecules upward and restrict condensation. Formed clusters are stretched to become columns above the threshold strength of the field, causing the condensation rate to drop quickly. The enhancement of surface attraction force boosts the rearrangement of water molecules adjacent to the surface and exaggerates the threshold value for shape transformation. In addition, the contact area between clusters and the surface increases with increasing amounts of surface attraction force, which raises the condensation efficiency. Thus, the condensation rate of water vapor on a surface under an electric field is determined by competition between intermolecular forces from the electric field and the surface.

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