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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 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...

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...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Covalently Linked Protein Regulators

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....

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Related Experiment Video

Updated: Jul 17, 2025

Covalent Fragment Screening Using the Quantitative Irreversible Tethering Assay

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Published on: February 28, 2025

Machine Learning Models to Interrogate Proteomewide Covalent Ligandabilities Directed at Cysteines.

Ruibin Liu¹, Joseph Clayton^1,2, Mingzhe Shen¹

¹Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201, USA.

Biorxiv : the Preprint Server for Biology

|September 4, 2023

Summary

Machine learning models accurately identify covalently ligandable sites, accelerating targeted covalent inhibitor design. This work expands the druggable proteome by predicting cysteine reactivity for novel drug discovery.

Keywords:

AlphaFold Covalent drug discovery database kinases machine learning protein structures

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Area of Science:

Computational chemistry and structural biology
Drug discovery and medicinal chemistry
Bioinformatics and machine learning

Background:

Targeted covalent inhibitors offer a promising avenue for drug development, but identifying suitable ligandable sites remains a challenge.
Expanding the druggable proteome requires novel methods to identify potential drug targets.
Machine learning (ML) approaches have the potential to accelerate the identification of these sites.

Conclusions:

ML models, trained on curated data, can reliably identify covalently ligandable cysteines.
The developed tools (LigCys3D database and DeepCys web server) will aid researchers in covalent drug discovery.
This work represents a significant step towards ML-driven annotation of the human proteome for next-generation drug discovery.