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Related Concept Videos

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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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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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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Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
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Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.

François Charih1,2,3, James R Green1,3, Kyle K Biggar2,3

  • 1Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada.

Cells
|September 26, 2025
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Summary
This summary is machine-generated.

This review highlights sequence-based protein-protein interaction (PPI) prediction methods. These computational approaches are crucial for identifying disease targets and advancing drug discovery.

Keywords:
biologicsdrug discoverydrug target identificationprotein language modelsprotein–protein interaction predictionsequence-based models

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Drug discovery

Background:

  • Aberrant protein-protein interactions (PPIs) are implicated in numerous human diseases.
  • Targeting these harmful interactions presents a promising therapeutic strategy.
  • Computational methods, particularly deep learning, are advancing PPI prediction.

Purpose of the Study:

  • To review state-of-the-art sequence-based PPI prediction methods.
  • To explore the impact of PPI prediction on target identification and drug discovery.
  • To emphasize rigorous model assessment in PPI prediction.

Main Methods:

  • Overview of data sources and curation techniques for PPI prediction.
  • Survey of traditional similarity-based and deep learning-based PPI predictors.
  • Emphasis on transformer architectures in deep learning for PPI prediction.

Main Results:

  • Sequence-based PPI prediction offers a viable alternative to structure-based methods.
  • Examples of PPI prediction in proteomics, target identification, and therapeutic design are provided.
  • The review underscores the importance of data quality and model assessment.

Conclusions:

  • Sequence-based PPI prediction is a broadly applicable tool in biomedical research.
  • These methods significantly contribute to identifying therapeutic targets and designing novel drugs.
  • Advancements in computational approaches are revolutionizing disease treatment strategies.