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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...

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:

Protein Organization

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....

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...

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Related Experiment Video

Updated: Jul 6, 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

Structure-based, deep-learning models for protein-ligand binding affinity prediction.

Debby D Wang¹, Wenhui Wu^2,3, Ran Wang^4,5,6

¹School of Science and Technology, Hong Kong Metropolitan University, 81 Chung Hau Sreet, Ho Man Tin, Hong Kong, China.

Journal of Cheminformatics

|January 4, 2024

Summary

This summary is machine-generated.

Deep learning shows promise for predicting protein-ligand binding affinity, a key challenge in molecular structural science. This review examines deep learning methods, offering insights for structure-based drug discovery.

Keywords:

Binding affinity prediction Deep learning Interpretability Molecular representation Structure-based drug discovery

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

Area of Science:

Molecular structural science
Computational chemistry
Biophysics

Background:

Deep learning has advanced molecular structural science, exemplified by the AlphaFold series.
Predicting protein-ligand binding affinity is a critical challenge requiring sophisticated computational methods.

Purpose of the Study:

To review mainstream structure-based deep learning approaches for protein-ligand binding affinity prediction.
To assess the readiness of deep learning for this complex problem.
To provide a uniform basis for comparing different deep learning models.

Main Methods:

Focus on molecular representations, learning architectures, and model interpretability in deep learning models.
Generated a taxonomy of existing deep learning models.
Evaluated representative models on a uniform basis to enable valid comparisons.

Main Results:

Identified and categorized mainstream structure-based deep learning approaches.
Provided a comparative analysis of model advantages and shortcomings.
Highlighted the need for standardized evaluation protocols.

Conclusions:

Deep learning holds significant potential for advancing protein-ligand binding affinity prediction.
The review offers a framework for understanding and comparing deep learning models in this field.
Findings can benefit structure-based drug discovery and related research areas.