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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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Related Experiment Video

Updated: Nov 1, 2025

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

Mining for Ligandable Cavities in RNA.

Jingru Xie¹, Aaron T Frank^2,3

¹Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, United States.

ACS Medicinal Chemistry Letters

|June 18, 2021

Summary

This summary is machine-generated.

Machine learning models can now distinguish between ligandable and non-ligandable binding cavities in RNA structures. This advancement aids in identifying potential drug targets for RNA-based therapeutics.

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Last Updated: Nov 1, 2025

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

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Published on: August 9, 2019

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

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An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA

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Published on: February 17, 2023

Area of Science:

Biochemistry
Computational Biology
Drug Discovery

Background:

Identifying ligand binding cavities is crucial for structure-based drug screening.
Distinguishing truly ligandable cavities from decoys, especially in RNA targets, remains a significant challenge.

Purpose of the Study:

To develop and validate machine learning classifiers for identifying ligandable binding cavities in RNA.
To assess the utility of these classifiers in structure-based virtual screening of RNA targets.

Main Methods:

Training machine learning classifiers to differentiate between ligandable RNA cavities and decoy cavities.
Applying the trained classifiers to independent test sets and modeled RNA structures.

Main Results:

Classifiers achieved an Area Under the Curve (AUC) > 0.83 in distinguishing ligandable cavities from decoys.
Application to HIV-1 TAR element RNA models identified conformers with high ligandability scores resembling known holo-structures.

Conclusions:

The developed machine learning classifiers are effective tools for identifying ligandable RNA cavities.
These classifiers can facilitate structure-based virtual screening efforts for RNA drug targets.