In Silico Analysis of Neutralizing Antibody Epitopes on Hepatitis C Virus Surface Glycoproteins as a Distinct Peptide Library Designing

2021 ◽  
Author(s):  
Raziyeh Zareh-Khoshchehreh ◽  
Taravat Bamdad ◽  
Seyed Shahriar Arab ◽  
Mahdi Behdani ◽  
Mahmoud Biglar
Keyword(s):  
Hepatitis C Virus ◽  
Hepatitis C ◽  
In Silico ◽  
In Silico Analysis ◽  
Neutralizing Antibody ◽  
Peptide Library ◽  
Virus Surface ◽  
Antibody Epitopes ◽  
Silico Analysis ◽  
Distinct Peptide

scholarly journals The associations between Toll-like receptor 4 gene polymorphisms and hepatitis C virus infection: a systematic review and meta-analysis

2019 ◽  
Vol 39 (2) ◽  
Author(s):  
Narttaya Chaiwiang ◽  
Teera Poyomtip
Keyword(s):  
Hepatocellular Carcinoma ◽  
Systematic Review ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
In Silico ◽  
Meta Analysis ◽  
In Silico Analysis ◽  
Hcv Infection ◽  
Toll Like Receptor ◽  
Silico Analysis

Abstract Background and objective: The hepatitis C virus (HCV) is able to cause a life-threatening disease relating to lethal hepatocellular carcinoma. Previous, Toll-like receptor polymorphisms were proposed as promising biomarker for HCV-related hepatocellular carcinoma and disease progression. This study aimed to summarize the association of TLR4 polymorphisms and HCV infection through meta-analysis. Methods: We applied a systematic review and meta-analysis performed by using PubMed, EMBASE and Web of Science searches. The Modified Newcastle-Ottawa scale was used for quality assessment. The odd-ratio (OR) and 95% confidence interval (CI) were calculated to assess the association. In silico analysis was applied for proposing the function as microRNA (miRNA) of non-coding polymorphism. Finally, the miRNA target was predicted and annotated to suggest the possible relationship between polymorphism and HCV infection. Results: Our meta-analysis incorporated seven studies involving rs4986791, rs4986790 and rs2149356. No association exists between rs4986791 and HCV infection. However, the heterozygous model (AG vs GG) of rs4986790 significantly associates with HCV infection (OR = 0.33, 95% CI = 0.21–0.49, P<0.0001). Moreover, the rs2149356 TG genotype also associates with HCV infection in the over-dominant model (TG vs TT+TG: OR = 0.54, 95% CI = 0.40–0.75). In silico analysis of rs2149356G allele showed that this mutation is siRNA, which targets the set of genes, especially in the autophagy pathway. Conclusion: We demonstrated that rs4986790 and rs2149356 are associated with HCV infection.

New neutralizing antibody epitopes in hepatitis C virus envelope glycoproteins are revealed by dissecting peptide recognition profiles

Vaccine ◽  
2011 ◽  
Vol 30 (1) ◽  
pp. 69-77 ◽  
Author(s):  
Alla Kachko ◽  
Galina Kochneva ◽  
Galina Sivolobova ◽  
Antonina Grazhdantseva ◽  
Tatyana Lupan ◽  
...  
Keyword(s):  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Neutralizing Antibody ◽  
Envelope Glycoproteins ◽  
Virus Envelope ◽  
Peptide Recognition ◽  
Antibody Epitopes

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

In Silico Analysis of Single Nucleotide Polymorphism in Human Prothrombin Gene

2019 ◽  
Author(s):  
I. Farah ◽  
A. El-Mubark ◽  
M. Osman ◽  
A. Soliman ◽  
F. Ali ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphism ◽  
In Silico ◽  
In Silico Analysis ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Prothrombin Gene ◽  
Silico Analysis

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki
Keyword(s):  
Data Analysis ◽  
Virtual Screening ◽  
In Silico ◽  
Model Development ◽  
In Silico Analysis ◽  
Material Design ◽  
Efficient Solutions ◽  
Biological Data ◽  
Cost Efficient ◽  
Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

In-Silico Analysis of Chromone Containing Sulfonamide Derivatives as Human Carbonic Anhydrase Inhibitors

2013 ◽  
Vol 9 (4) ◽  
pp. 608-616 ◽  
Author(s):  
Zaheer Ul-Haq ◽  
Saman Usmani ◽  
Uzma Mahmood ◽  
Mariya al-Rashida ◽  
Ghulam Abbas
Keyword(s):  
Carbonic Anhydrase ◽  
In Silico ◽  
In Silico Analysis ◽  
Carbonic Anhydrase Inhibitors ◽  
Human Carbonic Anhydrase ◽  
Silico Analysis
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