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Allosteric Proteins-ATCase

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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.
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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Machine learning approaches in predicting allosteric sites.

Francho Nerín-Fonz¹, Zoe Cournia¹

¹Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephesiou, Athens 11527, Greece; Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.

Current Opinion in Structural Biology

|February 14, 2024

Summary

This summary is machine-generated.

This review covers machine learning models for predicting allosteric sites, crucial for drug discovery. Future AI advancements, including protein language models, promise enhanced allosteric modulator development.

Keywords:

Allostery Drug design Machine learning Protein binding sites

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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

Area of Science:

Biochemistry and Molecular Biology
Computational Chemistry
Pharmacology

Background:

Allosteric regulation controls cellular processes through modulators binding to distal protein sites.
Allosteric modulators offer advantages over orthosteric ones, driving computational drug discovery efforts.
Identifying allosteric sites is key for developing novel therapeutics.

Purpose of the Study:

To review existing machine learning (ML) models for predicting allosteric sites.
To discuss the potential of artificial intelligence (AI) in advancing allosteric drug discovery.
To highlight future perspectives, including the role of protein language models.

Main Methods:

Literature review of computational approaches for allosteric site identification.
Analysis of machine learning model applications in predicting allosteric binding sites.
Exploration of emerging AI technologies relevant to allosteric modulation.

Main Results:

Several ML models have been developed for predicting allosteric sites.
These models leverage computational strategies to identify potential drug targets.
The review synthesizes current methodologies and their successes.

Conclusions:

Machine learning models are valuable tools for allosteric site prediction.
Artificial intelligence, particularly protein language models, offers promising future directions.
Advancements in computational methods will accelerate allosteric drug discovery.