A Novel Amino Acid Substitution, Fibrinogen Bβp.Pro234Leu, Associated with Hypofibrinogenemia Causing Impairment of Fibrinogen Assembly and Secretion

We identified a novel heterozygous variant, Bβp.Pro234Leu (fibrinogen Tokorozawa), which was suspected to be associated with hypofibrinogenemia. Therefore, we analyzed the assembly and secretion of this fibrinogen using Chinese hamster ovary (CHO) cells. To determine the impact on the synthesis and secretion of fibrinogen of the Bβp.P234L and γp.G242E substitutions, we established recombinant variant fibrinogen-producing CHO cell lines. Synthesis and secretion analyses were performed using an enzyme-linked immunosorbent assay (ELISA) and immunoblotting analysis with the established cell lines. In addition, we performed fibrin polymerization using purified plasma fibrinogen and in-silico analysis. Both Bβp.P234L and γp.G242E impaired the secretion and synthesis of fibrinogen. Moreover, immunoblotting analysis elucidated the mobility migration of the Bβγ complex in Bβp.P234L. On the other hand, the fibrin polymerization of fibrinogen Tokorozawa was similar to that of normal fibrinogen. In-silico analysis revealed that the Bβp.P234 residue is located in the contact region between the Bβ and γ chains and contacts γp.G242 residue. The present study demonstrated that the Bβp.P234L substitution resulted in hypofibrinogenemia by decreasing the assembly and secretion of fibrinogen. Therefore, there is a possibility that substitutions in the contact region between the Bβ and γ chains impact the assembly and secretion of fibrinogen.

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In silico analysis of CDC73 gene revealing 11 novel SNPs associated with Jaw Tumor Syndrome

10.1101/729764 ◽

2019 ◽

Author(s):

Abdelrahman H. Abdelmoneim ◽

Alaa I. Mohammed ◽

Esraa O. Gadim ◽

Mayada A.Mohammed ◽

Sara H. Hamza ◽

...

Keyword(s):

In Silico ◽

Autosomal Dominant ◽

In Silico Analysis ◽

Autosomal Dominant Disorder ◽

Variable Expression ◽

Back Ground ◽

And Function ◽

Molecular Aberrations ◽

The Impact ◽

Silico Analysis

AbstractBack groundhyperparathyroidism-jaw tumor (HPT-JT) is an autosomal dominant disorder with variable expression, with an estimated prevalence of 6.7 per 1,000 population. Genetic testing for predisposing CDC73 (HRPT2) mutations has been an important clinical advance, aimed at early detection and/or treatment to prevent advanced disease. The aim of this study is to assess the effect of SNPs on CDC73 structure and function using different bioinformatics tools.MethodComputational analysis using eight different in-silico tools including SIFT, PROVEAN, PolyPhen-2, SNAP2, PhD-SNP, SNPs&GO, PMut and Imutant were used to identify the impact on the structure and/or function of CDC73 gene that might be causing jaw tumour.ResultsFrom (733) SNPs identified in the CDC73 gene we found that only Eleven were deleterious to the function and structure of protein and expected to cause syndrome.ConclusionEleven substantial genetic/molecular aberrations in CDC73 gene were identified that could serve as actionable targets for chemotherapeutic intervention in patients whose disease is no longer surgically curable.

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In silico Analysis of Transcription Factors Associated to Differentially Expressed Genes in Irradiated Glioblastoma Cell Lines

Evolution of the Molecular Biology of Brain Tumors and the Therapeutic Implications ◽

10.5772/53299 ◽

2013 ◽

Author(s):

P. R. D. V. Godoy ◽

S. S. ◽

F. S. ◽

E. A. ◽

G. A. S. Passos ◽

...

Keyword(s):

Transcription Factors ◽

Cell Lines ◽

Differentially Expressed Genes ◽

In Silico ◽

In Silico Analysis ◽

Differentially Expressed ◽

Glioblastoma Cell ◽

Factors Associated ◽

Silico Analysis ◽

Glioblastoma Cell Lines

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

10.1101/2020.12.04.411835 ◽

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.

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In silico analysis of CDC73 gene revealing 11 novel SNPs with possible association to Hyperparathyroidism-Jaw Tumor syndrome

The EuroBiotech Journal ◽

10.2478/ebtj-2020-0008 ◽

2020 ◽

Vol 4 (2) ◽

pp. 67-81

Author(s):

Abdelrahman H. Abdelmoneim ◽

Alaa I. Mohammed ◽

Esraa O. Gadim ◽

Mayada Alhibir Mohammed ◽

Sara H. Hamza ◽

...

Keyword(s):

In Silico ◽

Autosomal Dominant ◽

In Silico Analysis ◽

Autosomal Dominant Disorder ◽

Variable Expression ◽

Project Hope ◽

Molecular Aberrations ◽

Structural Levels ◽

The Impact ◽

Silico Analysis

AbstractHyperparathyroidism-Jaw Tumor (HPT-JT) is an autosomal dominant disorder with variable expression, with an estimated prevalence of 6.7 per 1,000 population. Genetic testing for predisposing CDC73 (HRPT2) mutations has been an important clinical advance, aimed at early detection and/or treatment to prevent advanced disease. The aim of this study is to assess the most deleterious SNPs mutations on CDC73 gene and to predict their influence on the functional and structural levels using different bioinformatics tools. Method: Computational analysis using twelve different in-silico tools including SIFT, PROVEAN, PolyPhen-2, SNAP2, PhD-SNP, SNPs&GO, P-Mut, I-Mutant ,Project Hope, Chimera, COSMIC and dbSNP Short Genetic Variations were used to identify the impact of mutations in CDC73 gene that might be causing jaw tumor. Results: From (733) SNPs identified in the CDC73 gene we found that only Eleven SNPs (G49C, L63P, L64P, D90H, R222G, W231R, P360S, R441C, R441H, R504S and R504H) has deleterious effect on the function and structure of protein and expected to cause the syndrome. Conclusion: Eleven substantial genetic/molecular aberrations in CDC73 gene identified that could serve as diagnostic markers for hyperparathyroidism-jaw tumor (HPT-JT).

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In Silico Analysis of the Impact of Transmural Myocardial Infarction on Cardiac Mechanical Dynamics for the 17 AHA Segments

Functional Imaging and Modeling of the Heart - Lecture Notes in Computer Science ◽

10.1007/978-3-642-21028-0_30 ◽

2011 ◽

pp. 241-249

Author(s):

T. Fritz ◽

O. Jarrousse ◽

D. U. J. Keller ◽

G. Seemann ◽

O. Dössel

Keyword(s):

Myocardial Infarction ◽

In Silico ◽

In Silico Analysis ◽

Mechanical Dynamics ◽

The Impact ◽

Silico Analysis

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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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