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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...

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 1, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Machine-learning accelerated structure search for ligand-protected clusters.

Lincan Fang¹, Jarno Laakso¹, Patrick Rinke¹

¹Department of Applied Physics, Aalto University, 00076 AALTO, Espoo, Finland.

The Journal of Chemical Physics

|March 1, 2024

Summary

This summary is machine-generated.

Machine learning accelerates the search for low-energy structures in ligand-protected clusters. For gold clusters protected by cysteine ligands, specific hydrogen bonds in cysteine influence structural and electronic properties.

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Area of Science:

Computational Chemistry
Materials Science
Machine Learning

Background:

Determining low-energy structures of ligand-protected clusters is computationally intensive.
The vast conformational space poses a significant challenge for accurate structure prediction.

Purpose of the Study:

To develop and apply a machine learning method to accelerate the search for low-energy structures of ligand-protected clusters.
To investigate the structural and electronic properties of gold clusters protected by cysteine ligands.

Main Methods:

Utilized a kernel rigid regression based machine learning approach.
Employed the Au25(Cys)18 cluster as a model system for method validation.

Main Results:

Successfully accelerated the search for low-energy structures.
Identified specific hydrogen bond configurations in cysteine as key features of low-energy structures.
Demonstrated that ligand layer configurations impact cluster properties.

Conclusions:

The machine learning method is effective for predicting low-energy structures of ligand-protected clusters.
Hydrogen bonding in cysteine ligands plays a crucial role in the stability and structure of gold clusters.
Ligand arrangement significantly influences the electronic and structural characteristics of these nanomaterials.