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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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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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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Protein-protein interaction prediction methods: from docking-based to AI-based approaches.

Yuko Tsuchiya1, Yu Yamamori1, Kentaro Tomii1

  • 1Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan.

Biophysical Reviews
|December 26, 2022
PubMed
Summary

This review explores computational methods for predicting protein-protein interactions (PPIs), including antibody-antigen complexes. It highlights the importance of these predictions for understanding cellular functions and advancing structural biology.

Keywords:
AlphaFoldAntibody–antigen interactionDocking simulationEpitope predictionProfile–profile comparisonProtein–protein interaction

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Area of Science:

  • Structural biology
  • Computational biology
  • Immunology

Background:

  • Protein-protein interactions (PPIs) are crucial for diverse cellular functions, including signaling and complex formation.
  • Advances in cryo-electron microscopy (cryo-EM) have improved protein complex structure determination.
  • However, many complex structures remain unresolved, necessitating computational approaches.

Purpose of the Study:

  • To provide a comprehensive overview of computational methods for predicting PPIs.
  • To discuss the application of these methods to antibody-antigen interactions.
  • To offer historical and future perspectives on PPI prediction.

Main Methods:

  • Review of existing computational algorithms for PPI prediction.
  • Inclusion of novel computational strategies developed by the authors.
  • Analysis of methods applicable to various protein complexes, including antibody-antigen systems.

Main Results:

  • A wide array of computational tools and techniques for PPI estimation are available.
  • These methods offer valuable insights where experimental structural data is lacking.
  • The review covers both established and emerging computational approaches.

Conclusions:

  • Computational methods are essential for complementing experimental structural data in PPI research.
  • Accurate PPI prediction aids in understanding cellular mechanisms and disease pathways.
  • Continued development of computational tools will enhance our ability to predict and analyze protein interactions.