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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

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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...

Patch Clamp

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Many fundamental cell functions such as muscle contraction and nerve transmission rely on the electrical signals produced by the movement of positively and negatively charged ions across the cell membrane. One competent method to record current flowing across the whole cell or single ion channel is the patch-clamp technique.
In this method, a glass micropipette containing electrolyte solution is tightly sealed against a small portion of the cell membrane. As a result, a patch of the cell...

Protein-protein Interfaces

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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...

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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: Apr 21, 2026

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

Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.

Xiaolei Zhu¹, Yi Xiong¹, Daisuke Kihara²

¹Department of Biological Science, Purdue University, West Lafayette, IN 47906, USA and Department of Computer Science, Purdue University, West Lafayette, IN 47906, USA.

Bioinformatics (Oxford, England)

|November 1, 2014

Summary

This summary is machine-generated.

Patch-Surfer2.0 predicts protein binding ligands by analyzing local surface patches. This computational method enhances drug design and understanding of protein function, outperforming existing approaches.

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Area of Science:

Computational biology
Structural bioinformatics
Drug discovery

Background:

Ligand binding is crucial for protein function, impacting biological processes.
Predicting ligand binding aids in understanding protein roles and potential drug interactions.
Accurate prediction is vital for drug design and assessing side effects.

Purpose of the Study:

To introduce Patch-Surfer2.0, a novel computational method for predicting protein binding ligands.
To improve upon existing ligand prediction techniques through enhanced pocket representation.
To facilitate drug design and the study of protein-ligand interactions.

Main Methods:

Utilizes local surface patches with physicochemical properties for pocket representation.
Employs 3D Zernike Descriptors for efficient mathematical representation of patches.
Incorporates a geodesic distance histogram to capture approximate patch positions.

Main Results:

Patch-Surfer2.0 identifies binding pockets with similar ligands even without global shape similarity.
The method demonstrates superior performance compared to existing computational approaches.
A comprehensive database of ligand binding pockets was developed for querying.

Conclusions:

Patch-Surfer2.0 offers a robust and improved method for binding ligand prediction.
The approach advances the understanding of protein function and aids in drug development.
The computational tool is publicly available for research use.