Comprehensive in-Silico Characterization of Deleterious IDH2 Variants and Their Structural and Functional Implications in Acute Myeloid Leukemia: In-Silico for Deleterious IDH2 Variants and Functional Implications in AML

Afia Muhammad Akram; Huma Nosheen; Shanza Chaudhary; Hafsa Zaheer; Zafar Iqbal

doi:10.54393/fbt.v6i2.238

Authors

Afia Muhammad Akram Department of Zoology, University of Education, Lahore, Pakistan
Huma Nosheen Department of Zoology, University of Education, Lahore, Pakistan
Shanza Chaudhary Department of Zoology, University of Education, Lahore, Pakistan
Hafsa Zaheer Department of Zoology, University of Education, Lahore, Pakistan
Zafar Iqbal InnovaCore Center for Research and Biotechnology, Texas, United States of America

DOI:

https://doi.org/10.54393/fbt.v6i2.238

Keywords:

Isocitrate Dehydrogenase 2, Leukemia, Acute Myeloid Leukemia, In silico, Bioinformatics

Abstract

Mutations in the isocitrate dehydrogenase 2 (IDH2) gene are frequently implicated in the pathogenesis of acute myeloid leukemia (AML). Objectives: To comprehensively evaluate the structural and functional consequences of deleterious single nucleotide polymorphisms (SNPs) in the IDH2 gene using an integrative in silico approach. Methods: SNP data were retrieved from public databases, and multiple bioinformatics tools were employed to predict pathogenic variants, Structural stability, conformational changes, Conservation analysis, RNA folding alterations, and protein-protein interactions. Results: A total of 10 high-confidence deleterious SNPs were identified, predominantly located in evolutionarily conserved regions. These variants significantly affected protein stability, increased hydrophobicity, disrupted structural conformation, and reduced protein-protein interaction affinity. RNA folding analysis further suggested altered transcript stability. Conclusions: Collectively, these findings highlight the potential of IDH2 variants as molecular markers in AML and provide insights into their structural and functional implications. This study underscores the importance of integrative computational approaches in prioritizing clinically relevant mutations and may facilitate future experimental and therapeutic investigations.

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