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Related Experiment Video

Updated: Apr 8, 2026

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

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Published on: August 20, 2019

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

Taiki Furutani¹, Yuka Okusha², Hiroki Nagami²

¹Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.

|April 7, 2026

Summary

AlphaFold2 accurately predicts the functional impact of p53 variants using 3D structural data. This tool helps assess pathogenicity for variants of uncertain significance, improving tumor suppressor protein analysis.

Keywords:

3D protein structural prediction AlphaFold2 p53 tumor suppressor variants of uncertain significance

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

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.