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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: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Machine learning on protein-protein interaction prediction: models, challenges and trends.

Tao Tang1, Xiaocai Zhang2, Yuansheng Liu3

  • 1School of Mordern Posts, Nanjing University of Posts and Telecommunications, 9 Wenyuan Rd, Qixia District, 210023 Jiangsu, China.

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Summary

This review surveys machine learning methods for predicting protein-protein interactions (PPIs), addressing limitations of experimental techniques. It highlights trends and future directions, including using predicted protein structures to enhance PPI prediction accuracy.

Keywords:
computational PPI predictiondeep learningmachine learningprotein–protein interaction

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine Learning

Background:

  • Experimental protein-protein interaction (PPI) detection is costly and prone to errors.
  • Advanced high-throughput technologies generate vast protein data, enabling computational approaches.
  • Machine learning (ML) models show significant promise for accurate PPI prediction.

Approach:

  • This paper provides a comprehensive survey of recent ML-based PPI prediction methods.
  • It details the ML models and protein data representation techniques used.
  • The review analyzes trends and potential improvements in the field.

Key Points:

  • ML models offer an efficient alternative to experimental PPI detection.
  • Effective protein data representation is crucial for ML model performance.
  • Future directions include integrating predicted protein structures into ML models.

Conclusions:

  • This survey serves as a guide for advancing ML-based PPI prediction.
  • Computational methods are essential for overcoming experimental limitations in PPI detection.
  • Integrating structural data can further enhance the accuracy and scope of PPI prediction.