In silico SNP Analysis and 3D Structure Prediction of Human ERG Proto-Oncogene

Area of Science:

• Genetics
• Molecular Biology
• Bioinformatics

Background:

• Single-nucleotide polymorphisms (SNPs) are key genetic variations influencing disease susceptibility and offering insights into gene-disease associations.
• The human ERG gene is an oncogene implicated in various cellular processes including proliferation, development, and apoptosis, making it a focus for disease research.

Purpose of the Study:

• To perform an in silico analysis of missense SNPs in the human ERG gene.
• To identify deleterious SNPs and assess their impact on protein stability.
• To provide detailed information on ERG gene missense SNPs for potential applications in disease detection and drug discovery.

Main Methods:

• Data retrieval from dbSNP for human ERG gene SNPs.
• Selection and analysis of 377 missense SNPs using five predictive tools (SIFT, PolyPhen-2, Condel, PHD-SNP, SNPs&GO).
• Assessment of protein stability for deleterious SNPs using iStable, I-Mutant, and MuPro, followed by 3D structure visualization.

Main Results:

• Out of 103,738 total SNPs, 377 missense SNPs were analyzed.
• Twenty-six missense SNPs were predicted as deleterious by all five tools.
• Twenty-three of these SNPs were found to decrease protein stability, with specific sites (T180, R302, S356, Y452) identified as clinically significant due to post-translational modifications.

Conclusions:

• The in silico analysis identified critical missense SNPs in the ERG gene affecting protein stability.
• These findings contribute valuable information for understanding ERG-related genetic disorders and cancers.
• The study highlights the potential significance of ERG gene SNP analysis in genetic disease detection and pharmaceutical development.