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Integrating Artificial Intelligence and Bioinformatics Methods to Identify Disruptive STAT1 Variants Impacting
Ebtihal Kamal1, Lamis A Kaddam2, Mehad Ahmed1
1Department of Basic Medical Sciences, College of Medicine, Prince Sattam bin Abdulaziz University, Al Kharj 16278, Saudi Arabia.
This study identifies six harmful single-nucleotide polymorphisms (SNPs) in the Signal Transducer and Activator of Transcription 1 (STAT1) gene, predicting their impact on protein structure and function. These findings offer a cost-effective alternative to experimental methods for identifying disease-associated STAT1 variants.
Area of Science:
- Genetics and Molecular Biology
- Bioinformatics and Computational Biology
- Immunology
Background:
- The Signal Transducer and Activator of Transcription 1 (STAT1) gene is crucial for the JAK-STAT signaling pathway, regulating immune responses, cell growth, and apoptosis.
- Dysfunctional STAT1 is linked to various immune system disorders.
Purpose of the Study:
- To identify disease-susceptible single-nucleotide polymorphisms (SNPs) in the STAT1 gene.
- To predict the structural and functional consequences of STAT1 mutations using computational tools.
Main Methods:
- Utilized multiple in silico tools (SIFT, Polyphen v2, PROVEAN, etc.) to identify deleterious non-synonymous SNPs (nsSNPs) in STAT1.
- Assessed the impact of nsSNPs on protein stability, pathogenicity, and structure using tools like I-Mutant, Alpha-Missense, and AlphaFold.
- Analyzed structural variations, protein-protein interactions, and conservation patterns.
Main Results:
- Identified six deleterious nsSNPs in STAT1: R602W, I648T, V642D, L600P, I578N, and W504C.
- Predicted significant effects of these nsSNPs on STAT1 protein structure, function, and stability.
Conclusions:
- Bioinformatics and AI-driven approaches can effectively pinpoint pathogenic STAT1 SNPs.
- This study provides a comprehensive analysis of STAT1 variants, offering a time- and cost-effective alternative to experimental methods.
