ROWVA: A Structure-Based Metric for Predicting the Pathogenicity of Protein Variants Using Alphafold2

Area of Science:

• Genomics
• Protein Structure Prediction
• Computational Biology

Background:

• The p53 protein is a crucial tumor suppressor that functions as a tetramer.
• Malignant variants in p53's tetramer-forming domain can lead to dysfunction.
• Identifying the pathogenicity of newly discovered p53 variants is essential.

Purpose of the Study:

• To evaluate AlphaFold2's capability in assessing the functional impacts of p53 variants.
• To utilize predicted 3D structural information for variant pathogenicity assessment.

Main Methods:

• Performed 3D structural predictions for p53 variants using AlphaFold2.
• Analyzed correlations between AlphaFold2-derived scores and known functional scores (e.g., protein stability, pathogenicity).
• Compared root-mean-square deviation between wild-type and variant structures.

Main Results:

• AlphaFold2-derived scores showed a high correlation with functional scores.
• Root-mean-square deviation between predicted wild-type and variant structures correlated well with functional impact.
• Demonstrated AlphaFold2's utility in predicting p53 variant pathogenicity.

Conclusions:

• AlphaFold2 can effectively predict the functional consequences of p53 variants.
• This computational approach aids in evaluating variants of uncertain significance.
• Facilitates a deeper understanding of p53-related dysfunction in cancer.