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Conserved Binding Sites

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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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Updated: Aug 18, 2025

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

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

Published on: January 26, 2024

Predictive modeling of moonlighting DNA-binding proteins.

Dana Mary Varghese¹, Ruth Nussinov^2,3, Shandar Ahmad¹

¹School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India.

NAR Genomics and Bioinformatics

|December 7, 2022

Summary

This summary is machine-generated.

Researchers developed a machine learning model to identify human moonlighting DNA-binding proteins (mDBPs). This model accurately distinguishes mDBPs from other DNA-binding proteins (DBPs), aiding in the discovery of novel multifunctional proteins.

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Area of Science:

Proteomics
Bioinformatics
Genomics

Background:

Moonlighting proteins are single polypeptides with multiple functions, often discovered serendipitously.
Understanding these multifunctional proteins is crucial for advancing biological and medical sciences.
Current methods for identifying moonlighting proteins are largely reactive and lack predictive power.

Purpose of the Study:

To develop a predictive model for identifying human moonlighting DNA-binding proteins (mDBPs) from first principles.
To distinguish mDBPs from other DNA-binding proteins (oDBPs) based on sequence, structure, evolutionary, and expression profiles.
To discover and annotate novel mDBPs and understand their diverse functions.

Main Methods:

Utilized sequence, predicted structure, evolutionary profiles, and global gene expression data.
Developed a machine learning model to classify mDBPs from oDBPs.
Validated predictions with existing literature and proposed new candidates.

Main Results:

Achieved high accuracy (74% AUC of ROC) in discriminating mDBPs from oDBPs.
Identified several novel predicted mDBPs with literature support.
Highlighted potential new mDBP candidates with currently unknown functions.

Conclusions:

First-principles prediction is a viable strategy for identifying moonlighting proteins.
The developed model significantly advances the understanding and annotation of human mDBPs.
This approach can be scaled to identify moonlighting proteins across different functional classes and organisms.