Immunoinformatics design of multiepitopes peptide-based universal cancer vaccine using matrix metalloproteinase-9 protein as a target.

A new approach toward cancer therapy is the use of cancer vaccine, yet the different molecular bases of cancers, reduce the effectiveness of this approach. In this article, we aim to use matrix metalloproteinase-9 protein (MMP9) which is an essential molecule in the survival and metastasis of all types of cancers as a target for universal cancer vaccine design. The reference sequence of MMP9 protein was obtained from NCBI databases. Furthermore, the B-cell and T cell-related peptides were analyzed using the IEDB website and other related soft wares. The best candidate peptides were then visualized using chimera software. Three peptides were found to be good candidates for interactions with B cells (SLPE, RLYT, and PALPR), while 10 peptides were found as good targets for interactions with MHC1 and another 10 peptides founded suitable for interactions with MHC2 with population coverages of 94.77 and 90.67%, respectively. Finally, the immune response simulation and molecular docking were done using the C-IMMSIM simulator and AutoDock Vina to confirm the effectiveness of the proposed vaccine. By the end of this project: twenty-three 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.

Design of a Multiepitope-Based Peptide Vaccine against the E Protein of Human COVID-19: An Immunoinformatics Approach.

BACKGROUND: A new endemic disease has spread across Wuhan City, China, in 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 multiepitope peptide vaccine against COVID-19 using an immunoinformatics approach. METHOD: Several techniques facilitating the combination of the immunoinformatics approach and comparative genomic approach were used in order to determine the potential peptides for designing the T-cell epitope-based peptide vaccine using the envelope protein of 2019-nCoV as a target. RESULTS: Extensive mutations, insertion, and deletion were discovered with comparative sequencing in the 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. CONCLUSION: The T-cell epitope-based peptide vaccine was designed for COVID-19 using the envelope protein as an immunogenic target. Nevertheless, the proposed vaccine rapidly needs to be validated clinically in order to ensure its safety and immunogenic profile to help stop this epidemic before it leads to devastating global outbreaks.

Extensive In Silico Analysis of ATL1 Gene : Discovered Five Mutations That May Cause Hereditary Spastic Paraplegia Type 3A.

BACKGROUND: Hereditary 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 is characterized by progressive bilateral and mostly symmetric spasticity and weakness of the legs. SPG3A gene mutations and the phenotype-genotype correlations have not yet been recognized. The aim of this work was to categorize the most damaging SNPs in ATL1 gene and to predict their impact on the functional and structural levels by several computational analysis tools. METHODS: The 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 the 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. RESULTS: Five nsSNPs out of 249 were classified as the most deleterious (rs746927118, rs979765709, rs119476049, rs864622269, and rs1242753115). CONCLUSIONS: In this study, the impact of nsSNPs in the ATL1 gene was investigated by various in silico 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 of age; also, it may play a key role in pharmacogenomics by evaluating drug response for this disabling disease.