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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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iDLDDG: predicting protein stability changes from missense mutations in DNA-binding proteins using integrated deep

Xuan Yu¹, Fang Ge^2,3, Dong-Jun Yu³

¹Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong SAR(HKG), 999077, China.

Briefings in Bioinformatics

|February 13, 2026

Summary

This summary is machine-generated.

Predicting missense mutations in DNA-binding proteins is crucial for understanding disease. Our new deep learning framework, iDLDDG, accurately differentiates effects on double-stranded and single-stranded DNA-binding proteins, improving mutation prediction.

Keywords:

DNA-binding protein bioinformatics deep learning missense mutation protein–DNA interaction

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Genomics and Bioinformatics
Molecular Biology
Computational Biology

Background:

Accurate prediction of missense mutations' impact on protein-DNA binding affinity is vital for disease mechanism research and therapeutic development.
Existing models often fail to account for the distinct characteristics of mutations in double-stranded DNA-binding proteins (DSBs) and single-stranded DNA-binding proteins (SSBs).

Purpose of the Study:

To develop a computational framework for accurately predicting the effects of missense mutations on protein-DNA binding affinity in both DSBs and SSBs.
To establish a method that rigorously differentiates mutation mechanisms between DSBs and SSBs.

Main Methods:

Constructed a comprehensive dataset from diverse sources.
Developed iDLDDG, a deep learning framework integrating sequence-based embeddings (ESM2, ProtTrans, ESM1v) with multi-scale structural and evolutionary information.
Employed an entropy-based algorithm to identify 181 optimal residues for modeling biophysical constraints, enhancing predictive accuracy and efficiency.

Main Results:

iDLDDG achieved state-of-the-art performance, with a 10-fold cross-validation Pearson Correlation Coefficient (PCC) of 0.755 on the MPD276 dataset.
Achieved a PCC of 0.632 on independent test sets covering both DSBs and SSBs, significantly outperforming existing methods.
Demonstrated the framework's ability to differentiate mutation mechanisms between DSBs and SSBs.

Conclusions:

iDLDDG provides a foundation for high-accuracy prediction of pathological mutations in DNA-binding proteins.
This work establishes the first computational framework capable of rigorously distinguishing DSB and SSB mutation mechanisms.
The enhanced predictive accuracy and computational efficiency support large-scale assessments of mutation effects in DNA-binding proteins.