International Journal of Computational Bioinformatics and In Silico Modeling
ABSTRACT: Bacteriophage ϕSa3mw uses Staphylococcus aureus as a host and contributes significantly to the pathogenesis of this bacterium. There are five virulence genes and fifty four non-virulence genes in ϕSa3mw. The growth of ϕSa3mw in the host is facilitated by the latter type of genes. To determine whether the two types of ϕSa3mw genes differ at the sequence level, we have investigated and compared the codon and amino acid usages in their protein coding sequences using various computational tools. Our data reveal that the frequencies of seventeen synonymous codons in the virulence genes are significantly different from those of the corresponding codons in the non-virulence genes. Of the significant codons, four codons are prevalent, whereas, the others are either absent or present infinitesimally in the virulence genes. While the over-presented codons are employed to encode glycine, lysine, threonine and tyrosine, the absent and under-represented codons are used for encoding arginine, asparagine, cysteine, histidine, isoleucine, leucine, proline, and valine. Analyses of amino acid usage data reveal that the extents of arginine, cysteine, methionine, and tryptophan are significantly less, whereas, those of lysine, threonine and tyrosine are considerably higher in the virulence proteins. The possible causes of the altered amino acid usage in the virulence proteins have been discussed at length.
KeyWords: Staphylococcus aureus, Phage, Virulence gene, Codon usage, Amino acid usage.
How to cite: Keya Sau et. al. In silico analyses of the virulence and non-virulence genes of a bacteriophage infecting Staphylococcus aureus. Int J Comput Bioinfo In Silico Model. 4(5) 2015: 709-714
ABSTRACT: The availability of plant genome data has stimulated In silico homology modelling of plant proteins which are structurally unknown. In this study physico-chemical and functional characterization of 14-3-3 protein in Cajanus cajan, Cicer arietinum, Pisum sativum and Lens culinaris were determined. Self-optimized prediction with alignment was used to calculate the secondary structural features of protein sequences. The 3-D structure was determined by SWISS-MODEL. Stereochemical properties and quality of the modelled structures were then assessed by Ramchandran plot analysis validated with PROCHECK. The modelled structures could be used as a foundation for functional analysis of experimentally derived crystal structures.
KeyWords: Legumes, 14-3-3s, Homology Modelling, Ramachandran plot, SWISS-MODEL.
How to cite: Sabeen Fatma et. al. In silico Based Analysis and Homology Model Generation of 14-3-3 in Four Legume Species. Int J Comput Bioinfo In Silico Model. 4(5) 2015: 715-723
|Mohamed A. Taha*, Sundos Ahmed, Radowan Elnair, Enas Basher, Amro Abdelghani, Abdel-moneim Mohamed Ali, Mohamed D. Dafaalla, Musaab M. alfaki, Mohamed A. Abdelrahim, Abdelmohaymin A. Abdalla, Musab I. Mohammed, Mohamed Elsheikh, Abbasher Hussein and Mohamed Hassan
||Mohamed A. Taha et. al. International Journal of Computational Bioinformatics and In Silico Modeling 4(5) 2015: 724-734
ABSTRACT: Mutations in BMPR2 gene are seen in about 15% of sporadic cases and about 40% of familial cases of PPH. We have studied non-synonymous SNPs in BMPR2-002 (ENST00000374574). Non-synonymous dsSNPs were identified using NCBI-database. Then advanced bioinformatics analysis was used to determine the functionality of each SNP in the coding region. Out of 323 SNPs which were found in the coding region, only 7 were found to be damaging in both SIFT and polyphen. 53 SNPs in 3UTR region were found to disrupt miRNA binding sites, whereas 55 SNPs were found to create new ones. Certain SNPs affect binding sites of certain MicroRNAs that have been linked to hepatic cancer and prostate cancer. BMPR2 gene interactions with other genes were identified and classified according to multiple parameters (physical interaction, co-localization, co-expression, pathway and prediction). BMPR2 is an important regulator in BMP pathway which affects cellular growth. Certain SNPs were found to affect BMPR2 structure hence function for better correlation with clinical cases.
KeyWords: Primary pulmonary hypertension gene; PPH1; BMPR2; in silico analysis; computational analysis; SNPs.
How to cite: Mohamed A. Taha et. al. In silico analysis of non-synonymous Single nucleotide polymorphisms of BMBR2 (PPH1) gene and demonstration of gene’s network interactions. Int J Comput Bioinfo In Silico Model. 4(5) 2015: 724-734
ABSTRACT: Nephrotic syndrome is a nonspecific kidney disorder characterized by a number of signs including proteinuria, hypoalbuminemia and edema. It is characterized by an increase in permeability of the capillary walls of the glomerulus leading to the presence of high levels of protein in the urine. PLCE1 belongs to the phospholipase family that catalyzes the hydrolysis of polyphosphoinositides and the generation of second messengers necessary for renal function, mutations of this gene result in Nephrotic syndrome type 3. Analysis of the genetic variation that can alter the expression and the function of the PLCE1 gene was done using computational methods. Genomic analysis of PLCE1 was initiated By Sift and Polyphen-2 servers and yielded 62 mutations to be damaging, the mutant amino acids biophysical characteristics and multiple sequence alignment were demonstrated to be affecting the protein function using Align-GVGD and Panther platforms.38 mutations affected protein function deleteriously. Gene regulations were demonstrated through Polymirts server studying 3’ UTR region, 18 derived alleles could create a new miRNA binding sites. Computational methods yield accurate results which can be a basis of diagnosis of steroid resistant nephrotic syndrome type 3.
KeyWords: Nephrotic syndrome type 3; Steroid resistant Nephrotic Syndrome; NPHS3 nsSNPs; Single nucleotide polymorphism (SNP); PLCE1 gene; Protein analysis, 3'UTR SNPs.
How to cite: Khalid El Khalid et. al. Nephrotic Syndrome type 3 from Genotype to Phenotype PLCE1 gene computational study. Int J Comput Bioinfo In Silico Model. 4(5) 2015: 735-742