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Ligand Binding Sites

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

Ligand Binding Sites

Ligand Binding Sites

Conserved Binding Sites

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

The Equilibrium Binding Constant and Binding Strength

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:

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Related Experiment Video

Updated: Dec 4, 2025

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

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

Published on: February 17, 2023

RLDOCK: A New Method for Predicting RNA-Ligand Interactions.

Li-Zhen Sun^1,2, Yangwei Jiang², Yuanzhe Zhou²

¹Department of Applied Physics, Zhejiang University of Technology, Hangzhou 310023, China.

Journal of Chemical Theory and Computation

|October 23, 2020

Summary

RLDOCK accurately predicts ligand-RNA binding sites and poses using an energy-based scoring function and multistep screening. This computational method aids RNA-targeted drug design by enabling exhaustive binding site scanning and efficient pose prediction.

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Last Updated: Dec 4, 2025

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

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Published on: February 17, 2023

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

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Published on: September 28, 2017

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

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Published on: July 9, 2021

Area of Science:

Computational chemistry
Structural biology
Drug discovery

Background:

Accurate prediction of ligand-RNA interactions is crucial for developing RNA-targeted drugs.
Existing methods face challenges in comprehensively identifying all potential binding sites and poses.

Purpose of the Study:

To introduce RLDOCK, a novel computational method for predicting ligand-RNA binding sites and poses.
To enhance the accuracy and efficiency of ligand-RNA docking predictions.

Main Methods:

RLDOCK utilizes an energy-based scoring function for exhaustive binding site sampling with flexible ligand conformations.
A novel multistep screening algorithm improves computational efficiency: grid-based energy maps for initial ranking, followed by coarse-grained and refined energy functions for pose prediction.
The method considers geometric and energetic scores for ligand-RNA pair analysis.

Main Results:

RLDOCK successfully predicted ligand poses within 1.0, 2.0, and 3.0 Å RMSD for 27.8%, 58.3%, and 69.6% of test cases, respectively, for the top three poses.
The method demonstrated the ability to identify multiple alternative or coexisting binding sites.
Validation was performed on 230 RNA-ligand-bound structures.

Conclusions:

RLDOCK offers a significant advancement in predicting ligand-RNA binding sites and poses.
The computational method provides a robust framework for RNA-targeted drug design.
Future work may incorporate RNA conformational ensembles and metal-ion effects for a more comprehensive prediction framework.