Comprehensive in-silico Analysis of High-risk Non-synonymous SNPs in Dectin-1 Gene of Human and their Impact on Protein Structure

2018 ◽  
Vol 15 (2) ◽  
pp. 144-155 ◽  
Author(s):  
Raman Thakur ◽  
Jata Shankar
Keyword(s):  
Protein Structure ◽  
High Risk ◽  
In Silico ◽  
In Silico Analysis ◽  
Synonymous Snps ◽  
Silico Analysis

scholarly journals In Silico Analysis of High-Risk Missense Variants in Human ACE2 Gene and Susceptibility to SARS-CoV-2 Infection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Asmae Saih ◽  
Hana Baba ◽  
Meryem Bouqdayr ◽  
Hassan Ghazal ◽  
Salsabil Hamdi ◽  
...  
Keyword(s):  
Protein Structure ◽  
High Risk ◽  
In Silico ◽  
In Silico Analysis ◽  
Host Cells ◽  
Structure Stability ◽  
Missense Variants ◽  
Cumulative Score ◽  
And Function ◽  
Silico Analysis

SARS-CoV-2 coronavirus uses for entry to human host cells a SARS-CoV receptor of the angiotensin-converting enzyme (ACE2) that catalyzes the conversion of angiotensin II into angiotensin (1-7). To understand the effect of ACE2 missense variants on protein structure, stability, and function, various bioinformatics tools were used including SIFT, PANTHER, PROVEAN, PolyPhen2.0, I. Mutant Suite, MUpro, SWISS-MODEL, Project HOPE, ModPred, QMEAN, ConSurf, and STRING. All twelve ACE2 nsSNPs were analyzed. Six ACE2 high-risk pathogenic nsSNPs (D427Y, R514G, R708W, R710C, R716C, and R768W) were found to be the most damaging by at least six software tools (cumulative score between 6 and 7) and exert deleterious effect on the ACE2 protein structure and likely function. Additionally, they revealed high conservation, less stability, and having a role in posttranslation modifications such a proteolytic cleavage or ADP-ribosylation. This in silico analysis provides information about functional nucleotide variants that have an impact on the ACE2 protein structure and function and therefore susceptibility to SARS-CoV-2.

In-silico analysis of non- synonymous SNPs of human LDLR gene and their impact on familial hypercholesterolemia

Gene Reports ◽  
2021 ◽  
pp. 101127
Author(s):  
Pratik Ghosh ◽  
Samarpita Ghosh ◽  
Bhaskar Behera ◽  
Jiban Kumar Behera ◽  
Manojit Bhattacharya ◽  
...  
Keyword(s):  
Familial Hypercholesterolemia ◽  
In Silico ◽  
In Silico Analysis ◽  
Ldlr Gene ◽  
Synonymous Snps ◽  
Silico Analysis

In silico analysis of the EPS8 gene family: genomic organization, expression profile, and protein structure

Genomics ◽  
2003 ◽  
Vol 81 (2) ◽  
pp. 234-244 ◽  
Author(s):  
Arianna Tocchetti ◽  
Stefano Confalonieri ◽  
Giorgio Scita ◽  
Pier Paolo Di Fiore ◽  
Christer Betsholtz
Keyword(s):  
Protein Structure ◽  
Gene Family ◽  
Expression Profile ◽  
In Silico ◽  
Genomic Organization ◽  
In Silico Analysis ◽  
Silico Analysis

scholarly journals Identification of HPV16 in suspected cases of cervical lesions and docking Study of its L1 protein with some natural inhibitors.

2020 ◽  
Author(s):  
Yusuf Lukman ◽  
Doro Aliyu Bala ◽  
Kabir Imam Malik ◽  
Abdulkadir Saidu ◽  
Abdulhadi Sale Kumurya ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Prevalence Rate ◽  
In Silico ◽  
In Silico Analysis ◽  
Low Grade ◽  
Cervical Lesions ◽  
Hpv 16 ◽  
L1 Protein ◽  
Silico Analysis

Abstract Background The Human papillomavirus (HPV) causes sexually transmitted diseases. Among several types of HPV variants, HPV 16 is listed as a high-risk group, the primary cervical cancer etiologic agent, which causes life-threatening disease among women worldwide. The presence of L1, E6 and E7 encoded oncoproteins are largely responsible for virulence and pathogenicity that leads to cervical lesions. This menace is required to be curbed by designing an anti-cancerous drugs. The protein receptor-inhibitor interaction adopted using in silico analysis is very important in drug designing. It was the objective of this study to identify HPV16 isolates from suspected cases of cervical cancer at SH Sokoto and SYMH Birnin Kebbi hospitals and also to identify potent HPV16’s L1 protein inhibitor using in silico analysis of Echinacoside, curcumin and Cichoric acid against the viral protein. Methods A total of 140 cervical smear samples consisting of 21 low grade squamous intraepithelial lesion, 6 high grade lesion and 117 negative pap smears were collected. The samples were subjected for molecular detection using PCR targeting E6 and L1 genes of the virus. Positive samples were sequenced using Sanger sequencing platform. All the sequencing data were analysed using bioedit software while data generated for the molecular prevalence was statistically analyzed using Chi-square. A comprehensive HPV L1 protein homology model was designed to predict the L1 protein interaction mechanism with natural inhibitory molecules using a structural drug design approach. AutoDock Vina was used to carry out the molecular docking. Results Out of the 140 samples, 24 samples were positive for the PCR representing 16.7% molecular prevalence rate. There is statistically significant association between cyto-diagnoses and presence of HPV16 ( P ˂0.05). The highest prevalence rate of 12(50% of positive sample) was recorded among women between 30-39 years old. Docking analysis showed that the Chicoric acid components of Echinacea purpurae have strong binding affinity to the L1 protein of the HPV. Conclusion This study provides data on HPV 16 epidemiology in northern Nigeria, High-risk type 16 HPV variant was identified and also provides novel evidence for consideration on certain interacting residues, when synthesizing Anti-HPV compounds in the wet lab.

scholarly journals Identification of High risk nsSNPs in Human TP53 Gene Associated with Li–Fraumeni Syndrome: An In Silico Analysis Approach

2020 ◽  
Author(s):  
Mujahed I. Mustafa ◽  
Naseem S. Murshed ◽  
Mazen A. Elbasher ◽  
Abdelrafie M. Makhawi
Keyword(s):  
High Risk ◽  
In Silico ◽  
3D Structure ◽  
In Silico Analysis ◽  
Tp53 Gene ◽  
Li Fraumeni Syndrome ◽  
Functional Inference ◽  
Li Fraumeni ◽  
The Impact ◽  
Silico Analysis

AbstractBackgroundLi–Fraumeni syndrome (LFS) is a cancer–prone conditions caused by a germline mutation of the TP53 gene on chromosome 17p13.1. It has an autosomal dominant pattern of inheritance with high penetrance.PurposeThe aim of this study is to identify the high-risk pathogenic nsSNPs in PT53 gene that could be involved in the pathogenesis of Li–Fraumeni syndrome.MethodsThe nsSNPs in the human PT53 gene retrieved from NCBI, were analyzed for their functional and structural consequences using various in silico tools to predict the pathogenicity of each SNP. SIFT, Polyphen, PROVEAN, SNAP2, SNPs&Go, PHD-SNP, and P-Mut were chosen to study the functional inference while I-Mutant 3.0, and MUPro tools were used to test the impact of amino acid substitutions on protein stability by calculating ΔΔG value. The effects of the mutations on 3D structure of the PT53 protein were predicted using RaptorX and visualized by UCSF Chimera.ResultsA total of 845 PT53 nsSNPs were analyzed. Out of 7 nsSNPs of PT53 three of them (T118L, C242S, and I251N) were found high-risk pathogenic.ConclusionIn this study, out of 7 predicted high-risk pathogenic nsSNPs, three high-risk pathogenic nsSNPs of PT53 gene were identified, which could be used as diagnostic marker for this gene. The combination of sequence-based and structure-based approaches is highly effective for pointing pathogenic regions.

scholarly journals In silico analysis of consequences of non-synonymous SNPs of Slc11a2 gene in Indian bovines

Genomics Data ◽  
2015 ◽  
Vol 5 ◽  
pp. 72-79 ◽  
Author(s):  
Shreya M. Patel ◽  
Prakash G. Koringa ◽  
Bhaskar B. Reddy ◽  
Neelam M. Nathani ◽  
Chaitanya G. Joshi
Keyword(s):  
In Silico ◽  
In Silico Analysis ◽  
Synonymous Snps ◽  
Silico Analysis

A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

2020 ◽  
Vol 47 (6) ◽  
pp. 398-408
Author(s):  
Sonam Tulsyan ◽  
Showket Hussain ◽  
Balraj Mittal ◽  
Sundeep Singh Saluja ◽  
Pranay Tanwar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gallbladder Carcinoma ◽  
In Silico ◽  
In Silico Analysis ◽  
Transcriptomic Profile ◽  
Silico Analysis
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