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Related Concept Videos

Allosteric Proteins-ATCase01:19

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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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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 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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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...
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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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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.
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Combining structural and coevolution information to unveil allosteric sites.

Giuseppina La Sala1, Christopher Pfleger2, Helena Käck3

  • 1Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca Gothenburg Sweden giuseppina.lasala@astrazeneca.com andrey.frolov@astrazeneca.com.

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Summary

Predicting allosteric sites in proteins is crucial for drug discovery. A new computational model integrates multiple data types to accurately identify hidden allosteric pockets, aiding pharmaceutical research.

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Area of Science:

  • Biochemistry and Structural Biology
  • Computational Drug Discovery
  • Pharmacology

Background:

  • Allosteric regulation is vital in biomolecules, making allosteric site prediction a key challenge in pharmaceutical research.
  • Computational methods have advanced in characterizing allosteric coupling, but identifying allosteric sites remains difficult.

Purpose of the Study:

  • To develop and validate a novel structure-based computational model for identifying potentially hidden allosteric sites in proteins.
  • To enhance drug discovery efforts by providing a reliable method for pinpointing druggable allosteric pockets.

Main Methods:

  • Integration of local binding site information, coevolutionary data, and dynamic allostery information.
  • Development of a structure-based three-parameter model for allosteric site prediction.
  • Validation of the model on five known allosteric proteins: LFA-1, p38-α, GR, MAT2A, and BCKDK.

Main Results:

  • The model successfully ranked all known allosteric pockets within the top three positions for the tested proteins.
  • A novel druggable site in MAT2A was identified and confirmed by X-ray crystallography and Surface Plasmon Resonance (SPR).
  • A previously unknown druggable allosteric site in BCKDK was validated through biochemical assays and X-ray crystallography.

Conclusions:

  • The developed three-parameter model effectively identifies hidden allosteric sites in protein structures.
  • This computational approach holds significant potential for accelerating drug discovery by locating novel druggable allosteric pockets.