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Ligand Binding Sites

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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...

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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.
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...

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Related Experiment Video

Updated: Jun 3, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Improving the Reliability of Language Model-Predicted Structures as Docking Targets through Geometric Graph Learning.

Chao Shen^1,2,3, Xiaoqi Han^1,4, Heng Cai¹

¹Hangzhou Carbonsilicon AI Technology Company Limited, Hangzhou 310018, Zhejiang, China.

Journal of Medicinal Chemistry

|January 9, 2025

Summary

This summary is machine-generated.

We introduce CarsiDock-Flex, a new AI method for flexible protein-ligand binding. This approach refines predicted protein structures to accurately model binding poses, improving drug discovery potential.

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Area of Science:

Computational biology
Artificial intelligence in drug discovery

Background:

Modeling protein-ligand interactions is crucial for drug discovery.
Existing methods struggle with protein flexibility.
AI techniques show promise but require further development.

Purpose of the Study:

To develop a novel, flexible docking paradigm for accurate protein-ligand binding pose prediction.
To improve the modeling of protein flexibility in docking simulations.

Main Methods:

Developed CarsiDock-Flex, a two-step flexible docking approach.
Utilized CarsiInduce, an equivariant deep learning model, to refine ESMFold-predicted protein pockets.
Integrated CarsiInduce with the CarsiDock algorithm for redocking ligands.

Main Results:

CarsiInduce effectively guides predicted protein pockets to holo-like conformations.
CarsiDock-Flex achieves superior docking accuracy, even for novel protein sequences.
Demonstrated the capability to model protein flexibility in ligand binding.

Conclusions:

CarsiDock-Flex offers a novel solution for flexible protein-ligand binding modeling.
The approach enhances the understanding of protein-ligand interactions considering flexibility.
Paves the way for more accurate virtual screening and drug design.