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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

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:

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...

Ligand Binding and Linkage

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked. In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...

Ligand Binding and Linkage

Ligand Binding and Linkage

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Related Experiment Video

Updated: Nov 26, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

ITScore-NL: An Iterative Knowledge-Based Scoring Function for Nucleic Acid-Ligand Interactions.

Yuyu Feng¹, Sheng-You Huang¹

¹School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China.

Journal of Chemical Information and Modeling

|December 9, 2020

Summary

This summary is machine-generated.

Researchers developed ITScore-NL, a novel scoring function for predicting nucleic acid-ligand interactions. This computational tool accurately identifies binding modes and affinities, crucial for drug discovery and understanding cellular processes.

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Area of Science:

Computational biology
Structural biology
Drug discovery

Background:

Nucleic acid-ligand complexes are vital for gene regulation and therapeutic intervention development.
Experimental structure determination is costly and challenging, necessitating computational approaches.
Accurate scoring functions are critical for molecular docking but lag for nucleic acid-ligand interactions due to limited data.

Purpose of the Study:

To develop a robust scoring function for nucleic acid-ligand interactions.
To improve the accuracy of binding mode and affinity predictions in computational drug design.
To address the limitations of existing scoring functions in nucleic acid-ligand complex analysis.

Main Methods:

Developed ITScore-NL, an iterative knowledge-based scoring function.
Incorporated statistical mechanics, stacking potentials, and electrostatic potentials.
Evaluated performance on three diverse test sets against 12 state-of-the-art scoring functions.

Main Results:

ITScore-NL significantly outperformed 12 other scoring functions.
Achieved near-native pose prediction (rmsd ≤ 1.5 Å) in 71.43% of cases.
Demonstrated good binding affinity prediction correlation (R = 0.64) on a large dataset.

Conclusions:

ITScore-NL shows high accuracy in predicting nucleic acid-ligand binding modes and affinities.
Explicitly including stacking and electrostatic potentials is essential for accurate scoring.
The developed function aids in understanding molecular interactions for therapeutic development.