Predicting Functional Impacts of Amino Acid Substitutions in Exon 21 of the ATP7B Gene for Wilson Disease Diagnosis

Background: Diagnosing illnesses with overlapping clinical symptoms shows challenges, necessitating precise identification of genetic variations underlying pathogenesis. Here, we focus on Wilson disease, an autosomal recessive disorder characterized by copper accumulation due to mutations in the ATP7B gene. Method: To predict the functional impact of single amino acid substitutions (SAASs) in exon 21 of ATP7B, we employed bioinformatics tools, including SIFT, PolyPhen2, and Provean. Our study, conducted on thirty Iraqi Wilson disease patients, identified missense mutations associated with disease manifestation. Result: Bioinformatics analyses revealed nine potentially deleterious non-synonymous SNPs in exon 21. Functional modifications were predicted more accurately by all programs, indicating their utility in identifying pathogenic variants. Conclusion: Our findings underscore the utility of computational methods in high-throughput SAAS annotation, offering insights for diagnostic screening and therapeutic strategies. Furthermore, our study expands the spectrum of ATP7B mutations implicated in Wilson disease onset, underscoring the role of bioinformatics in elucidating genotype-phenotype correlations and advancing precision medicine.