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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...

Conserved Binding Sites

Conserved Binding Sites

Allosteric Proteins-ATCase

Allosteric Proteins-ATCase

Binding sites linkages can regulate a protein's function. For example, enzyme activity is often regulated through a feedback mechanism where the end product of the biochemical process serves as an inhibitor.
Aspartate transcarbamoylase (ATCase) is a cytosolic enzyme that catalyzes the condensation of L-aspartate and carbamoyl phosphate to N-carbamoyl-L-aspartate. This reaction is the first step in pyrimidine biosynthesis. UTP and CTP, the end products of the pyrimidine synthesis...

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

Ligand Binding Sites

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: Mar 16, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

A Random Forest Model for Predicting Allosteric and Functional Sites on Proteins.

Ava S-Y Chen^1,2, Nicholas J Westwood^3,4, Paul Brear^5,6

¹Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, Purdie Building, University of St Andrews, North Haugh, St Andrews, Scotland KY16 9ST, UK.

Molecular Informatics

|August 6, 2016

Summary

We developed a machine learning model to accurately identify allosteric sites in proteins. This computational method aids in drug discovery by classifying protein binding sites.

Keywords:

Allosteric site Cheminformatics Drug Design Machine Learning Random Forest

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Area of Science:

Computational Biology
Structural Bioinformatics
Machine Learning in Biochemistry

Background:

Allosteric sites regulate protein function through non-active site ligand binding.
Identifying allosteric sites is crucial for developing targeted therapeutics.
Current methods for allosteric site identification can be limited.

Purpose of the Study:

To develop a novel computational method for identifying allosteric sites.
To classify protein binding sites as allosteric, regular, or orthosteric using machine learning.

Main Methods:

A Random Forest machine learning approach was employed.
The model was trained and tested on protein structures with bound ligands.
Forty-three structural descriptors per complex were derived to characterize binding sites.

Main Results:

The developed classification model accurately assigned protein cavities into three categories.
The method demonstrated effectiveness in identifying allosteric sites.
Validation on an unseen dataset, including the ligand CHES, confirmed the model's robustness.

Conclusions:

The computational method provides an effective means for identifying allosteric sites.
This approach can significantly aid in the discovery of novel allosteric modulators.
The machine learning model offers a scalable solution for analyzing protein-ligand interactions.