Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico...

Area of Science:

• Genetics
• Bioinformatics
• Molecular Biology

Background:

• Mutations in the PITX2 gene are linked to genetic disorders like Axenfeld-Rieger syndrome.
• Accurate assessment of PITX2 variant pathogenicity is essential for clinical diagnosis and research.

Purpose of the Study:

• To evaluate the reliability of various bioinformatics tools in predicting the pathogenicity of PITX2 missense variants.
• To compare bioinformatics predictions with structural and functional impacts of PITX2 variants.

Main Methods:

• Compared results from MutPred, Provean, and PMUT with characterized PITX2 missense variants.
• Applied selected tools to uncharacterized PITX2 variants.
• Utilized molecular modeling to predict structural changes caused by PITX2 variants.
• Assessed protein stability prediction programs, including CUPSAT, against molecular modeling data.

Main Results:

• MutPred, Provean, and PMUT demonstrated high performance (>93% sensitivity and specificity) for 18 characterized PITX2 variants.
• 12 out of 13 uncharacterized variants were predicted as deleterious by these tools.
• Molecular modeling identified specific variants (L54Q, F58L, etc.) that alter PITX2 structure.
• CUPSAT proved most reliable for predicting the impact of missense mutations on PITX2 stability.

Conclusions:

• MutPred, Provean, PMUT, molecular modeling, and CUPSAT are reliable tools for predicting PITX2 missense variant pathogenicity.
• These tools are likely applicable to other homeodomain transcription factors.
• Findings aid in the genetic analysis of PITX2-related disorders.