In silico analysis of squalene synthase in Fabaceae family using bioinformatics tools

BackgroundHyperornithinemia-hyperammonemia-homocitrullinuria (HHH) syndrome is an autosomal recessive inborn error of the urea cycle. It is caused by mutations in the SLC25A15 gene that codes the mitochondrial ornithine transporter. The aim of this study is to detect and identify the pathogenic SNPs in SLC25A15 gene through a combination set of bioinformatics tools and their effect on the structure and function of the protein.MethodsThe deleterious SNPs in SLC25A15 are detected by various bioinformatics tools, with addition to identifying their effects on the structure and function of this gene.Results20 deleterious SNPs out 287of were found to have their own damaging effects on the structure and function of the SLC25A15 gene.ConclusionThis study is the first in silico analysis of SLC25A15 using a selection of bioinformatics tools to detect functional and structural effects of deleterious SNPs. Finding the pathogenic SNPs is a promising start to innovate new, useful SNP diagnostic markers for medical testing and for safer novel therapies specifically targeting mutant SLC25A15.

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In silico analysis ofIDH3Agene revealed Novel mutations associated with Retinitis Pigmentosa

10.1101/554196 ◽

2019 ◽

Author(s):

Thwayba A. Mahmoud ◽

Abdelrahman H. Abdelmoneim ◽

Naseem S. Murshed ◽

Zainab O. Mohammed ◽

Dina T. Ahmed ◽

...

Keyword(s):

Retinitis Pigmentosa ◽

In Silico ◽

In Silico Analysis ◽

Photoreceptor Cells ◽

Structure And Function ◽

Novel Mutations ◽

Inherited Disorders ◽

Bioinformatics Tools ◽

And Function ◽

Silico Analysis

AbstractBackgroundRetinitis Pigmentosa (RP) refers to a group of inherited disorders characterized by the death of photoreceptor cells leading to blindness. The aim of this study is to identify the pathogenic SNPs in the IDH3A gene and their effect on the structure and function of the protein.Methodwe used different bioinformatics tools to predict the effect of each SNP on the structure and function of the protein.Result20 deleterious SNPs out of 178 were found to have a damaging effect on the protein structure and function.Conclusionthis is the first in silico analysis of IDH3A gene and 20 novel mutations were found using different bioinformatics tools, and they could be used as diagnostic markers for Retinitis Pigmentosa.

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In Silico Analysis and Modeling of Novel Pathogenic Single Nucleotide Polymorphisms (SNPs) in Human CD40LG Gene

10.1101/552596 ◽

2019 ◽

Cited By ~ 1

Author(s):

Abdelrahman H. Abdelmoneim ◽

Mujahed I. Mustafa ◽

Thwayba A. Mahmoud ◽

Naseem S. Murshed ◽

Mohamed A. Hassan

Keyword(s):

In Silico ◽

Elevated Serum ◽

In Silico Analysis ◽

Structure And Function ◽

Immunoglobulin M ◽

Hyper Igm Syndrome ◽

Bioinformatics Tools ◽

Hyper Igm ◽

And Function ◽

Silico Analysis

Abstract:Background:The X-linked hyper-immunoglobulin M syndrome (XHIGM) is a rare, inherited immune deficiency disorder. It is more common in males. Characterized by elevated serum IgM levels and low to undetectable levels of serum IgG, IgA and IgE. Hyper-IgM syndrome is caused by mutations in the CD40LG gene. Located in human Xq26. CD40LG acts as an immune modulator in activated T cells.Method:We used different bioinformatics tools to predict the effect of each SNP on the structure and function of the protein.Result:8 novel SNPs out of 233 were found to have most deleterious effect on the protein structure and function. While modeling of nsSNPs was studied by Project HOPE software.Conclusion:Better understanding of Hyper-IgM syndrome caused by mutations in CD40LG gene was achieved using in silico analysis. This is the first in silico functional analysis of CD40LG gene and 8 novel mutations were found using different bioinformatics tools, and they could be used as diagnostic markers for hyper-IgM syndrome. These 8 novel SNPs may be important candidates for the cause of different types of human diseases by CD40LG gene.

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

Seminars in Oncology ◽

10.1053/j.seminoncol.2020.02.012 ◽

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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In Silico Analysis of Single Nucleotide Polymorphism in Human Prothrombin Gene

10.1055/s-0039-1680245 ◽

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

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

Current Medicinal Chemistry ◽

10.2174/0929867327666200727114410 ◽

2020 ◽

Vol 27 (38) ◽

pp. 6523-6535 ◽

Cited By ~ 3

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.

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