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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 Folding

Protein Folding

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: Jan 8, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Flexible protein-ligand docking with diffusion-based side-chain packing.

Runze Zhang^1,2, Xinyu Jiang^1,2, Duanhua Cao^1,3

¹Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.

Proceedings of the National Academy of Sciences of the United States of America

|December 24, 2025

Summary

This summary is machine-generated.

PackDock, a new framework, uses AI and physics to model protein flexibility and interactions, improving drug discovery by identifying potent compounds and revealing crucial molecular changes.

Keywords:

machine learning molecular docking protein structure prediction

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Area of Science:

Computational biology
Structural biology
Drug discovery

Background:

Protein flexibility is critical for biological function and drug design, but traditional methods struggle to capture it.
Accurately modeling protein-ligand interactions requires accounting for dynamic conformational changes.

Purpose of the Study:

To introduce PackDock, a novel framework integrating deep learning and physics-based modeling for protein-ligand interaction analysis.
To address the limitations of static models by capturing protein flexibility and ligand-induced conformational changes.

Main Methods:

PackDock utilizes diffusion models (PackPocket) to sample diverse binding pocket conformations.
Validation involved side-chain packing, redocking, and cross-docking experiments.
The framework integrates deep learning with physics-based simulations.

Main Results:

PackDock successfully addressed protein flexibility challenges in various computational experiments.
The framework identified novel nanomolar affinity compounds with unique scaffolds for a target protein.
Key amino acid conformational changes induced by ligand binding were elucidated.

Conclusions:

PackDock offers a powerful approach to model protein-ligand interactions, accurately representing protein dynamics.
The framework enhances understanding of molecular recognition mechanisms.
PackDock provides valuable perspectives for advancing basic biological research and drug discovery efforts.