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protPheMut: An Interpretable Machine Learning Tool for Classification of Cancer and Neurodevelopmental Disorders in

Jingran Wang¹, Miao Yang¹, Chang Zong¹

¹MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-Infective Medicine, Department of Bioinformatics and Computational Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China.

Journal of Chemical Information and Modeling

|July 28, 2025

Summary

This summary is machine-generated.

New tool protPheMut predicts if protein mutations cause cancer or neurodevelopmental disorders (NDDs). It uses interpretable machine learning and network dynamics for high accuracy in personalized medicine.

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Area of Science:

Genomics and Bioinformatics
Computational Biology
Personalized Medicine

Background:

Missense mutations in single proteins can result in diverse phenotypes, including cancers and neurodevelopmental disorders (NDDs).
Existing tools struggle to link specific mutations to distinct phenotypes, hindering personalized medicine applications.
Oncoproteins like PI3Kα, PTEN, and RAS harbor mutations associated with various cancers and NDDs.

Purpose of the Study:

To develop a novel computational tool, protPheMut, for predicting whether missense mutations in a protein lead to cancer or NDDs.
To leverage interpretable machine learning and SHAP explanations for transparent and accurate phenotype prediction.
To integrate diverse biophysical and network dynamics signatures for enhanced mutation effect analysis.

Main Methods:

Developed protPheMut, a machine learning model integrating biophysical and network dynamics features.
Employed SHAP (SHapley Additive exPlanations) for model interpretability and feature importance analysis.
Validated protPheMut using cross-validation and an independent test set, including case studies on PI3Kα and PTEN mutations.

Main Results:

protPheMut achieved high accuracy in discriminating cancer- vs. NDDs-related mutations (AUCROC 0.9118 in cross-validation, 0.8925 on test set).
Demonstrated superior performance compared to seven other tools in predicting phenotypic effects for PI3Kα and PTEN mutations.
Achieved AUROC of 0.8501 for PI3Kα mutations (cancer/Cowden syndrome) and 0.9349 for PTEN mutations (cancer/PHTS/HCPS).

Conclusions:

protPheMut accurately predicts disease phenotypes (cancer/NDDs) arising from missense mutations.
Interpretable machine learning, particularly SHAP explanations, highlights the importance of network and dynamic features in phenotype classification.
The tool offers a valuable resource for personalized medicine by linking specific mutations to distinct disease outcomes.