Biology subjects

Elhag, M.

Publications and source records attributed to Elhag, M..

5 recordsLinked to original sources

bioRxiv2019

Thirty two novel nsSNPs May effect on HEXA protein Leading to Tay-Sachs disease (TSD) Using a Computational Approach

BackgroundGenetic polymorphisms in the HEXA gene are associated with a neurodegenerative disorder called Tay-Sachs disease (TSD) (GM2 gangliosidosis type 1). This study aimed to predict the possible pathogenic SNPs of this gene and their impact on the protein using different bioinformatics tools.\n\nMethodsSNPs retrieved from the NCBI database were analyzed using several bioinformatics tools. The different algorithms collectively predicted the effect of single nucleotide substitution on both structure and function of the hexosaminidase A protein.\n\nResultsFifty nine mutations were found to be highly damaging to the structure and function of the HEXA gene protein.\n\nConclusionAccording to this study, thirty two novel nsSNP in HEXA are predicted to have possible role in Tay-Saches Disease using different bioinformatics tools. Our findings could help in genetic study and diagnosis of Tay-Saches Disease.

bioinformatics↗

bioRxiv2019

Immunoinformatics Prediction of Epitope Based Peptide Vaccine Against Schistosoma Mansoni Fructose Bisphosphate Aldolase Protein

Schistosoma Mansoni represents an important tropical disease that can cause schistosomiasis mostly in Africa and Middle East with high mortality rates. Moreover, no vaccine against it exists. This study predicts an effective epitope-based vaccine against Fructose 1,6 Bisphosphate Aldolase (FBA) enzyme of Schistosoma Mansoni using immunoinformatics approaches. FBA is important for production of energy required for different schistosome activities and survival. The sequences were retrieved from NCBI and several prediction tests were conducted to analyze possible epitopes for B-cell, T-cell MHC class I and II. Tertiary structure of the most promising epitopes was obtained. Two epitopes showed high binding affinity for B-cells, while four epitopes showed high binding affinity for MHCI and MHCII. The results were promising to formulate a vaccine with more than 99.5% population coverage. We hope that these promising epitopes serves as a preventive measure for the disease in the future and recommend invivo and invitro studies.

bioinformatics↗

bioRxiv2019

Immunoinformatics Prediction of Epitope Based Peptide Vaccine Against Mycobacterium Tuberculosis PPE65 family Protein

IntroductionTuberculosis (TB) is a serious disease with varying rates of mortality and morbidity among infected individuals which estimates for approximately two million deaths/year. The number of deaths could increase by 60% if left untreated. It mainly affects immune-compromised individuals and people of third world, due to poverty, low health standards, and inadequate medical care. It has varying range of manifestations that is affected by the host immune system response, the strain causing the infection, its virulence, and transmissibility.\n\nMaterials and methodsA total of 1750 Mycobacterium Tuberculosis PPE65 family protein strains were retrieved from National Center for Biotechnology Information (NCBI) database on March 2019 and several tools were used for the analysis of the T- and B-cell peptides and homology modelling.\n\nResults and conclusionFour strong epitope candidates had been predicted in this study for having good binding affinity to HLA alleles, good global population coverage percentages. These peptides are YAGPGSGPM, AELDASVAM, GRAFNNFAAPRYGFK and a single B-cell peptide YAGP.\n\nThis study uses immunoinformatics approach for the design of peptide based vaccines for M. tuberculosis. Peptide based vaccines are safer, more stable and less hazardous/allergenic when compared to conventional vaccines. In addition, peptide vaccines are less labouring, time consuming and cost efficient. The only weakness is the need to introduce an adjuvant to increase immunogenic stimulation of the vaccine recipient.

bioinformatics↗

bioRxiv2019

Design of Epitope Based Peptide Vaccine Against Pseudomonas Aeruginosa Fructose Bisphosphate Aldolase Protein using Immunoinformatics

Pseudomonas aeruginosa is common pathogen that is responsible of serious illnesses hospital acquired infection as ventilator associated pneumonia and various sepsis syndrome. Also it is a multidrug resistant pathogen recognized for its ubiquity, its intrinsically advanced antibiotic resistant mechanisms. generally affects the immuonocompromised but can also infect the immunocompetent as in hot tub folliculitis. There is no vaccine against it available till now. This study predicts an effective epitope-based vaccine against Fructose bisphosphate aladolase (FBA) of Pseudomonas aeruginosa using immunoinformatics tools. The sequences were obtained from NCBI and prediction tests took place to analyze possible epitopes for B and T cells. Three B cell epitopes passed the antigenicity, accessibility and hydrophilicity tests. Six MHC I epitopes were the most promising, while four from MHC II. Nineteen epitopes were shared between MHC I and II. For the population coverage, the epitopes covered 95.62% of the alleles worldwide excluding certain MHC II alleles. We recommend invivo and invitro studies to prove its effectiveness.

bioinformatics↗

bioRxiv2019

Immunoinformatics Prediction of Epitope Based Peptide Vaccine Against Listeria Monocytogenes Fructose Bisphosphate Aldolase Protein

Listeria Monocytogenes represents an important food-borne pathogen worldwide that can cause life-threatening listeriosis disease especially in pregnant women, fetuses, elderly people, and immuno-compromised individuals with high mortality rates. Moreover, no vaccine against it exists. This study predicts an effective epitope-based vaccine against Fructose 1,6 Bisphosphate Aldolase (FBA) enzyme of Listeria Monocytogenes using immunoinformatics approaches. The sequences were retrieved from NCBI and several prediction tests were conducted to analyze possible epitopes for B-cell, T-cell MHC class I and II. 3D structure of the promising epitopes was obtained. Two epitopes showed high binding affinity for B-cells, while four epitopes showed high binding affinity for MHCI and MHCII. The results were promising to formulate a vaccine with more than 98% population coverage. We hope that these promising epitopes serves as a preventive measure for the disease in the future and recommend invivo and invitro studies.

bioinformatics↗