In Silico Functional and Structural Analysis of STAT4 Variants of Uncertain Significance

Background: The STAT4 gene plays a key role in immune regulation and is associated with susceptibility to autoimmune diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). Objectives: The objective of this study is to analyze variants of uncertain significance (VUSs) in STAT4 using bioinformatics tools to predict their functional and structural impact. Methods: A total of 48,295 variants of the STAT4 gene (ENSG00000138378) were retrieved from the Ensembl database. A tiered filtering approach was used to assess VUS pathogenicity, integrating in silico prediction tools such as SIFT, PolyPhen, MutPred2, and Align-GVGD, as well as structural modeling platforms including Chimera, ModRefiner, Missense3D, HOPE, and DynaMut2. Results: Eighty missense VUSs were identified; of these, 13 were prioritized based on concordant signals across multiple computational predictors. These variants showed significant alterations in the physicochemical properties of the protein, including changes in hydrophobicity and disruption of hydrogen bonding. Notably, the rs140675301 (Glu128Val) variant lies within a conserved loop, and in silico analyses suggest that this mutation may alter kinase specificity regarding the phosphorylation of serine 130. Conclusions: The integrative use of the bioinformatic tools employed represents a valuable preliminary step prior to undertaking more complex and resource-intensive functional studies. This complementary strategy strengthens the interpretative framework for VUS, guiding subsequent experimental validation and supporting a structured assessment of variant relevance, particularly in the context of immune-related genes such as STAT4.