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

Author(s):  
Nithya S. Rathinam ◽  
Madhana Priya N ◽  
Magesh Ramasamy

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.

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
C. George Priya Doss ◽  
Chiranjib Chakraborty ◽  
Luonan Chen ◽  
Hailong Zhu

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.


RSC Advances ◽  
2016 ◽  
Vol 6 (103) ◽  
pp. 100772-100782 ◽  
Author(s):  
Shaojie Ma ◽  
Shengfu Zhou ◽  
Weicong Lin ◽  
Rong Zhang ◽  
Wenjuan Wu ◽  
...  

We explored the structural features that have an impact on TgCDPK1 activity and TgCDPK1/Src selectivity by multi-computational methods with different statistical models.


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

Biomolecules ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 709
Author(s):  
Dakshinamurthy Sivakumar ◽  
Sathish-Kumar Mudedla ◽  
Seonghun Jang ◽  
Hyunjun Kim ◽  
Hyunjin Park ◽  
...  

PDE9 inhibitors have been studied to validate their potential to treat diabetes, neurodegenerative disorders, cardiovascular diseases, and erectile dysfunction. In this report, we have selected highly potent previously reported selective PDE9 inhibitors BAY73-6691R, BAY73-6691S, 28r, 28s, 3r, 3s, PF-0447943, PF-4181366, and 4r to elucidate the differences in their interaction patterns in the presence of different metal systems such as Zn/Mg, Mg/Mg, and Zn/Zn. The initial complexes were generated by molecular docking followed by molecular dynamics simulation for 100 ns in triplicate for each system to understand the interactions’ stability. The results were carefully analyzed, focusing on the ligands’ non-bonded interactions with PDE9 in different metal systems.


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