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Updated: Apr 11, 2026

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

Causal Prediction of TP53 Variant Pathogenicity Using a Perturbation-Informed Protein Language Model.

Huiying Chen¹, Yang Zhao¹, Boqiang Hu¹

¹The Key Laboratory of Pancreatic Diseases of Zhejiang Province, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|April 9, 2026

Summary

This summary is machine-generated.

CaVepP53 accurately predicts the functional impact of TP53 gene mutations. This TP53-specific model outperforms general predictors, aiding precision medicine and cancer research.

Keywords:

TP53 causal pathogenic mutations perturbation protein language models variant effect prediction

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Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Predicting variant functional impact is vital for disease understanding, especially for cancer genes like TP53.
Current variant effect prediction (VEP) models struggle with missense mutations due to their non-gene-specific nature.

Purpose of the Study:

To develop CaVepP53, a TP53-specific VEP model.
To improve the accuracy and interpretability of missense mutation classification for TP53.

Main Methods:

Fine-tuned a TP53-specific predictor using perturbation-based experimental variants.
Integrated protein language models with experimental functional data.
Quantified pathogenicity using Euclidean distances and logistic transformation for confidence scores.

Main Results:

CaVepP53 significantly outperformed general VEP models (AlphaMissense, PrimateAI-3D) in accuracy, precision, and F1-score.
Experimental validation confirmed CaVepP53's robustness, identifying novel functional variants.
The framework was extended to other cancer-related genes (VHL, ATM, BRCA1, RAD51C, BAP1).

Conclusions:

CaVepP53 provides accurate and interpretable VEP for TP53, overcoming limitations of existing models.
The gene-specific VEP framework shows potential for precision medicine applications.
This approach enhances understanding of cancer-related gene mutations.