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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Related Experiment Video

Updated: Jun 26, 2025

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

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Published on: January 26, 2024

DeepPPAPredMut: deep ensemble method for predicting the binding affinity change in protein-protein complexes upon

Rahul Nikam¹, Sherlyn Jemimah^1,2, M Michael Gromiha^1,3

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

Bioinformatics (Oxford, England)

|May 8, 2024

Summary

This summary is machine-generated.

We developed a deep ensemble model to predict how mutations alter protein-protein binding affinity. This tool accurately forecasts binding changes, aiding disease research and drug discovery.

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Area of Science:

Computational Biology
Biochemistry
Genomics

Background:

Protein-protein interactions are crucial for cellular functions.
Mutations disrupting these interactions can cause diseases.
Accurate prediction of binding affinity changes is needed.

Purpose of the Study:

To develop a computational tool for predicting mutation-induced changes in protein-protein binding affinity.
To leverage deep learning and structural features for enhanced prediction accuracy.

Main Methods:

Developed a deep ensemble model integrating protein sequences, structure-based features, and functional classes.
Utilized experimental affinity data for training and validation.
Performed rigorous testing including Leave-One-Out Complex (LOOC) cross-validation.

Main Results:

Achieved high performance on training data (correlation=0.97, MAE=0.35 kcal/mol).
Demonstrated robust generalization on test data (correlation=0.72, MAE=0.83 kcal/mol).
LOOC cross-validation showed strong performance (correlation=0.83, MAE=0.51 kcal/mol).

Conclusions:

The developed deep ensemble model accurately predicts mutation effects on protein binding affinity.
The tool offers a valuable resource for understanding disease mechanisms and guiding therapeutic strategies.
The model's consistent performance across validation sets highlights its reliability.