Biology subjects

Abdelmageed, M. I.

Publications and source records attributed to Abdelmageed, M. I..

3 recordsLinked to original sources

bioRxiv2020

Immunoinformatic design of multi epitopes peptide-based universal cancer vaccine using matrix metalloproteinase-9 protein as a target

BackgroundCancer remains a major public health hazard despite the extensive research over the years on cancer diagnostic and treatment, this is mainly due to the complex pathophysiology and genetic makeup of cancer. A new approach toward cancer treatment is the use of cancer vaccine, yet the different molecular bases of cancers reduce the effectiveness of this approach. In this work we aim to use matrix metalloproteinase-9 protein (MMP9) which is essential molecule in the survival and metastasis of all type of cancer as a target for universal cancer vaccine design. Methodreference sequence of matrix metalloproteinase-9 protein was obtained from NCBI databases along with the related sequence, which is then checked for conservation using BioEdit, furthermore the B cell and T cell related peptide were analyzed using IEDB website. The best candidate peptide were then visualized using chimera software. ResultThree Peptides found to be good candidate for interactions with B cells (SLPE, RLYT, and PALPR), while ten peptides found as a good target for interactions with MHC1 (YRYGYTRVA, YGYTRVAEM, YLYRYGYTR, WRFDVKAQM, ALWSAVTPL, LLLQKQLSL, LIADKWPAL, KLFGFCPTR, MYPMYRFTE, FLIADKWPA) with world combined coverage of 94.77%. In addition, ten peptides were also found as a good candidates for interactions with MHC2 (KMLLFSGRRLWRFDV, GRGKMLLFSGRRLWR, RGKMLLFSGRRLWRF, GKMLLFSGRRLWRFD, TFTRVYSRDADIVIQ, AVIDDAFARAFALWS, FARAFALWSAVTPLT, MLLFSGRRLWRFDVK, GNQLYLFKDGKYWRF, NQLYLFKDGKYWRFS), with world combined coverage of 90.67%. CONCLUSION23 peptide-based vaccine was designed for use as a universal cancer vaccine which has a high world population coverage for MHC1(94.77%) and MHC2 (90.67%) related alleles.

immunology↗

bioRxiv2020

Design of multi epitope-based peptide vaccine against E protein of human 2019-nCoV: An immunoinformatics approach

BackgroundNew endemic disease has been spread across Wuhan City, China on December 2019. Within few weeks, the World Health Organization (WHO) announced a novel coronavirus designated as coronavirus disease 2019 (COVID-19). In late January 2020, WHO declared the outbreak of a "public-health emergency of international concern" due to the rapid and increasing spread of the disease worldwide. Currently, there is no vaccine or approved treatment for this emerging infection; thus the objective of this study is to design a multi epitope peptide vaccine against COVID-19 using immunoinformatics approach. MethodSeveral techniques facilitating the combination of immunoinformatics approach and comparative genomic approach were used in order to determine the potential peptides for designing the T cell epitopes-based peptide vaccine using the envelope protein of 2019-nCoV as a target. ResultsExtensive mutations, insertion and deletion were discovered with comparative sequencing in COVID-19 strain. Additionally, ten peptides binding to MHC class I and MHC class II were found to be promising candidates for vaccine design with adequate world population coverage of 88.5% and 99.99%, respectively. ConclusionT cell epitopes-based peptide vaccine was designed for COVID-19 using envelope protein as an immunogenic target. Nevertheless, the proposed vaccine is rapidly needed to be validated clinically in order to ensure its safety, immunogenic profile and to help on stopping this epidemic before it leads to devastating global outbreaks.

bioinformatics↗

bioRxiv2019

Extensive In Silico Analysis of ATL1 Gene: Discovered Five Mutations that may Cause Hereditary Spastic Paraplegia Type 3A

BACKGROUNDHereditary spastic paraplegia type 3A (SPG3A) is a neurodegenerative disease inherited type of Hereditary spastic paraplegia (HSP). It is the second most frequent type of HSP; which Characterized by muscle stiffness with paraplegia and early-onset of symptoms. This is the first translational bioinformatics analysis in a coding region of ATL1 gene which aims to categorize nsSNPs to be used as genomic biomarkers; also it may play a key role in pharmacogenomics by evaluating drug response for this disabling disease.\n\nMETHODSThe raw data of ATL1 gene were retrieved from dbSNP database, and then run into numerous computational analysis tools. Additionally; we submitted the common six deleterious outcomes from the previous functional analysis tools to I-mutant 3.0, and MUPro respectively, to investigate their effect on structural level. The 3D structure of ATL1 was predicted by RaptorX and modeled using UCSF Chimera to compare the differences between the native and the mutant amino acids.\n\nRESULTSFive nsSNPs out of 249 were classified as the most deleterious (rs746927118, rs979765709, rs119476049, rs864622269, rs1242753115).\n\nCONCLUSIONSIn this study the impact of nsSNPs in the ATL1 gene was investigated by various bioinformatics tools, that revealed five nsSNPs (V67F, T120I, R217Q, R495W and G504E) are deleterious SNPs, which have a functional impact on ATL1 protein; and therefore, can be used as genomic biomarkers specifically before 4 years old; also it may play a key role in pharmacogenomics by evaluating drug response for this disabling disease.

bioinformatics↗