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Related Concept Videos

Allosteric Proteins-ATCase01:19

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.
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...
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Ligand Binding and Linkage00:49

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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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Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

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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...
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Ligand Binding Sites02:40

Ligand Binding Sites

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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...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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.
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Protein-protein Interfaces02:04

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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Updated: Jun 13, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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DeepAllo: allosteric site prediction using protein language model (pLM) with multitask learning.

Moaaz Khokhar1,2, Ozlem Keskin3, Attila Gursoy1,2

  • 1Department of Computer Engineering, Koç University, 34450 Istanbul, Turkey.

Bioinformatics (Oxford, England)
|May 15, 2025
PubMed
Summary
This summary is machine-generated.

DeepAllo improves allosteric site prediction by combining protein language models with pocket features. This novel approach enhances drug development by accurately identifying key allosteric pockets.

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Area of Science:

  • Computational Biology
  • Drug Discovery
  • Bioinformatics

Background:

  • Allostery, crucial for protein function, presents a significant challenge in drug development.
  • Identifying allosteric pockets is complex, with Machine Learning offering promising prediction strategies.

Purpose of the Study:

  • To develop an advanced method for predicting allosteric pockets.
  • To enhance the accuracy and performance of allosteric site identification in proteins.

Main Methods:

  • Fine-tuning a protein language model (pLM) on the AlloSteric Database (ASD) using Multitask Learning.
  • Integrating pLM features with FPocket features to train XGBoost and AutoML models.
  • Visualizing the pLM's attention mechanism for interpretability.

Main Results:

  • DeepAllo achieves an 89.66% F1 score and 90.5% top-3 prediction accuracy for allosteric pockets.
  • The approach outperforms previous methods in allosteric site prediction.
  • A case study validated the method's efficacy on known allosteric proteins.

Conclusions:

  • DeepAllo represents a significant advancement in predicting allosteric pockets.
  • The combination of pLMs and pocket features offers superior performance.
  • This method has strong implications for accelerating drug discovery and development.