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ZFP-CanPred: Predicting the effect of mutations in zinc-finger proteins in cancers using protein language models.

Amit Phogat¹, Sowmya Ramaswamy Krishnan¹, Medha Pandey¹

¹Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036 India.

Methods (San Diego, Calif.)

|February 5, 2025

Summary

This study introduces ZFP-CanPred, a deep learning tool that accurately predicts cancer-driving mutations in zinc-finger proteins (ZNFs). It outperforms existing methods, aiding in understanding cancer development and therapies.

Keywords:

Cancer Driver Mutations Neural network Neutral Protein language model ZFP-CanPred Zinc-fingers

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Area of Science:

Genomics
Computational Biology
Cancer Research

Background:

Zinc-finger proteins (ZNFs) are vital transcription factors involved in numerous cellular functions.
Missense mutations in ZNFs can disrupt protein-DNA interactions, contributing to cancer development.

Purpose of the Study:

To develop ZFP-CanPred, a novel deep learning model for predicting cancer-associated driver mutations in ZNFs.
To differentiate between cancer-causing and neutral mutations in ZNFs.

Main Methods:

Utilized protein language models (PLMs) to derive representations from the structural neighborhood of mutated sites.
Trained a deep learning model (ZFP-CanPred) on these representations.
Evaluated performance on an independent test set and compared with 11 existing prediction tools.

Main Results:

ZFP-CanPred achieved high performance with 0.72 accuracy, 0.79 F1-score, and 0.74 AUC on an independent test set.
Outperformed 11 other prediction tools, including generic and cancer-specific methods, with the highest AU-ROC of 0.74.
Demonstrated balanced specificity and sensitivity, addressing limitations of current mutation prediction tools.

Conclusions:

ZFP-CanPred is a superior tool for predicting cancer-associated driver mutations in ZNFs.
The model aids in understanding oncogenic processes and developing targeted cancer therapies.
The study provides valuable insights into the role of ZNF mutations in cancer.