Background: X linked severe combined immunodeficiency (X-SCID) is a lifethreatening disorder. It is due to mutation of the interleukin two receptor gamma-chain (IL2RG) gene. Nonsynonymous SNPs (nsSNPs) are the most common polymorphism, known to be deleterious or disease-causing variations because they alter protein sequence, structure, and function. Objective: is to reveal the effect of harmful SNPs in the function and structure of IL2RG protein. Method: Data on IL2RG was investigated from dbSNP/NCBI database. Prediction of damaging effect was done using sift, polyphen, provean and SNAP2.more software were used for more analysis: phd-snp, and and go, Pmut, Imutant.modeling was done using chimera and project hope. Gene interaction was done by gene mania.3UTR prediction was done using polymiRTS software. Result: The in-silico prediction identified 1479 SNPs within IL2RG gene out of which 253 were coding SNPs, 50 took place in the miRNA 3 UTR, 21 occurred in 5 UTR region and 921 occurred in intronic regions. a total of 12 missense nsSNPs were found to be damaging by both a sequence homology-based tool (SIFT) and a structural homology-based method (3UTR region were predicted to disrupt miRNAs binding sites and affect the gene expression. Conclusions: Computational analysis of SNPs has become a very valuable tool in order to discriminate neutral SNPs from damaging SNPs. This study revealed 5 novel nsSNPs in the IL2RG gene by using different software and 21 SNPs in 3UTR. These SNPs could be considered as important candidates in causing diseases related to IL2RG mutation and could be used as diagnostic markers.Keywords: X linked severe combined immunodeficiency (X-SCID), interleukin 2 receptor gamma-chain (IL2RG), single nucleotide polymorphism (SNP), nonsynonymous Single Nucleotide Polymorphisms (nsSNPs), bioinformatics.

The fungus Candida albicans is an opportunistic pathogen that causes a wide range of infections. It's the primary cause of candidiasis and the fourth most common cause of nosocomial infection. In addition, disseminated invasive candidiasis which is a major complication of the disease has an estimated mortality rate of 40%-60% even with the use of antifungal drugs. Over the last decades, several different anti-Candida vaccines have been suggested with different strategies for immunization against candidiasis such as, live-attenuated fungi, recombinant proteins, and glycoconjugates but none has been approved by the FDA, yet. This study aims to introduce a new possible vaccine for C. albicans through analyzing peptides of its pyruvate kinase (PK) protein as an immunogenic stimulant computationally.A total number of 28 C. albicans, pyruvate kinase proteins were obtained from NCBI on the 9 th of February 2019 and were subjected to multiple sequence alignment using Bioedit for conservancy. The main analytical tool was IEDB, Chimera for homology modelling, and MOE for docking.Among the tested peptides, fifteen promising T-cell peptides were predicted. Five peptides were more important than the others (HMIFASFIR, YRGVYPFIY, AVAAVSAAY, LRWAVSEAV, and IFASFIRTA) They show high Binding Affinity to MHC molecules, low binding energy required indicating more stable bonds, and their ideal length of nine peptides. (PTRAEVSDV) peptide is the most promising linear B-cell peptide due to its physiochemical parameters and optimal length (nine amino acids). It's highly recommended to have these five strong candidates in future in vivo and in vitro analysis studies.Keywords: candida albicans, immunoinformatics, multi-epitope, peptide-based vaccine, pyruvate kinase, vaccine design Non-linear B-cell epitopes 1.3.2.1 ElliPro Antibody epitopes prediction:It's a tool that works using the PDB ID of the protein, providing minimum score of (0.5) and maximum distance of (6) to predict non-linear peptides. It provides 3D model of the clustered peptides with the result. 42 (available at: http://tools.iedb.org/ellipro/) T-cell epitopes prediction:1.4.2 Binding to MHC class I prediction tool: