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Related Concept Videos

Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
Allosteric Regulation01:08

Allosteric Regulation

Allosteric regulation of enzymes occurs when the binding of an effector molecule to a site that is different from the active site causes a change in the enzymatic activity. This alternate site is called an allosteric site, and an enzyme can contain more than one of these sites. Allosteric regulation can either be positive or negative, resulting in an increase or decrease in enzyme activity. Most enzymes that display allosteric regulation are metabolic enzymes involved in the degradation or...
Allosteric Regulation01:08

Allosteric Regulation

Allosteric regulation of enzymes occurs when the binding of an effector molecule to a site that is different from the active site causes a change in the enzymatic activity. This alternate site is called an allosteric site, and an enzyme can contain more than one of these sites. Allosteric regulation can either be positive or negative, resulting in an increase or decrease in enzyme activity. Most enzymes that display allosteric regulation are metabolic enzymes involved in the degradation or...
Allosteric Proteins-ATCase01:19

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 pathway,...

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Related Experiment Video

Updated: Jun 5, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

AE-PocketMiner Uses Attention to Simultaneously Predict Cryptic Pockets and Their Allosteric Coupling.

Si Zhang, Prajna Mishra, Devin Kelly

    Biorxiv : the Preprint Server for Biology
    |June 4, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces AE-PocketMiner, an AI tool that identifies cryptic pockets and their allosteric effects for drug discovery. This method expands the druggable proteome by predicting challenging protein targets.

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    Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

    Published on: January 26, 2024

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    Last Updated: Jun 5, 2026

    Mapping Dysfunctional Protein-Protein Interactions in Disease
    09:39

    Mapping Dysfunctional Protein-Protein Interactions in Disease

    Published on: October 24, 2025

    Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
    06:50

    Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

    Published on: January 26, 2024

    Area of Science:

    • Biochemistry
    • Computational Biology
    • Drug Discovery

    Background:

    • Cryptic pockets are transient protein sites crucial for drug targeting.
    • Identifying and understanding allosteric regulation of these pockets is challenging.
    • Current methods struggle to predict cryptic pocket locations and functional relevance.

    Purpose of the Study:

    • To develop an AI-driven method for simultaneous prediction of cryptic pockets and their allosteric coupling.
    • To enhance the identification and targeting of cryptic pockets for expanding the druggable proteome.
    • To provide a framework for drug discovery, including pocket identification, prioritization, and assay design.

    Main Methods:

    • Introduction of attention enabled (AE-)PocketMiner, an artificial intelligence (AI) method.
    • Utilizing a graph neural network with an attention mechanism.
    • Predicting cryptic pocket locations and allosteric coupling from a single protein structure.

    Main Results:

    • AE-PocketMiner outperforms existing methods in identifying cryptic pockets.
    • The method successfully recapitulates known allosteric interactions.
    • Experimental validation confirmed newly predicted cryptic pockets and their allosteric control mechanisms.

    Conclusions:

    • AE-PocketMiner offers a powerful framework for identifying and prioritizing cryptic pockets.
    • The AI tool aids in designing assays for drug discovery targeting previously inaccessible proteins.
    • This approach significantly expands the potential of the druggable proteome.