Non-Synonymous Snps in The Human Erbb2 Gene: Insilico Prediction and Functional Analysis - Govardhan, T; Shalini, G Baby; Kumar VM, Hemanth; Augustine, Alfred J; S Adiga, Usha; J, Kavya; Brahmaiah, Jyoti; T, Amulya (Journal of Neonatal Surgery, 2025)
Introduction: ERBB2 is altered by mutations and amplifications in a variety of cancer types. A small number of drugs have been approved by the Food and Drug Administration (FDA) to treat ERBB2 variants. Determining the expected impact of gene mutations and the protein-protein interactions involved in various cancer types was the aim of the in silico study, which evaluated the ERBB2 gene using bioinformatics techniques. Methods: The single nucleotide polymorphisms (SNPs) of the ERBB2 gene were investigated in silico using the accession IDs and FASTA amino acid sequences obtained from the National Center for Biotechnology Information (NCBI). Scale-Invariant Feature Transform (SIFT), Polymorphism Phenotyping version 2 (Polyphen-2), Combined Annotation Dependent Depletion (CADD) score, meta-analytic logistic regression (MetaLR), and mutation assessor were among the bioinformatics tools utilized for the study. Protein-protein interactions were evaluated using a string database. Results: 8298 SNPs are examined. According to SIFT analysis of the SNPs in the ERBB2 gene, 35.7% of the mutations were acceptable and 63.3% were detrimental. The Polyphen study found that 54.1% of SNPs were dangerous mutations and 45.1% were benign. According on metaLR analysis, 63.5% of the SNPs were tolerated, while 36.1% had detrimental mutations. A total of 10.8%, 33.4%, 35%, and 20.3% of the mutations were classified as high, low, medium, and neutral respectively, based on the mutation assessor tool. Conclusion: The current in silico investigation aims to elucidate the significance of ERBB2 gene variants and their correlation with various disease states. According to our study majority of the SNPswere found to be deleterious. Future studies on pathological conditions connected to ERBB 2 may consider this analysis of detrimental nsSNPs. This study may have significant implications for precision medicine as well.