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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 undergo chemical modifications that trigger changes in the charge, structure, and conformation of the proteins. Phosphorylation, acetylation, glycosylation, nitrosylation, ubiquitination, lipidation, methylation, and proteolysis are various protein modifications that regulate protein activity. Such modifications are usually enzyme-driven.
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The addition or removal of phosphate groups from proteins is the most common chemical modification that regulates cellular processes. These modifications can affect the structure, activity, stability, and localization of proteins within cells as well as their interactions with other proteins.
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Sequence-based machine learning method for predicting the effects of phosphorylation on protein-protein interactions.

Xiaokun Hong1, Jiyang Lv1, Zhengxin Li1

  • 1State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.

International Journal of Biological Macromolecules
|June 8, 2023
PubMed
Summary

This study introduces PhosPPI, a novel AI tool predicting how protein phosphorylation affects protein-protein interactions (PPI). PhosPPI offers a user-friendly, accurate method to identify functional phosphorylation sites, aiding disease mechanism research and drug development.

Keywords:
Machine learningPhosphorylationProtein-protein interactionSequence-based model

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Area of Science:

  • Biochemistry
  • Molecular Biology
  • Bioinformatics

Background:

  • Protein phosphorylation is a key regulatory mechanism in cell signaling pathways.
  • Protein-protein interactions (PPI) are crucial for signal transduction.
  • Dysregulated phosphorylation in PPI is linked to diseases like cancer and Alzheimer's.

Purpose of the Study:

  • To develop a high-accuracy, user-friendly artificial intelligence (AI) method for predicting the effect of phosphorylation on PPI.
  • To address the limitations of experimental methods in identifying phosphorylation-regulated PPI.

Main Methods:

  • Proposed a novel sequence-based machine learning method named PhosPPI.
  • Evaluated PhosPPI's performance against existing predictive methods (Betts, HawkDock, FoldX).

Main Results:

  • PhosPPI demonstrated superior identification performance, including accuracy and AUC.
  • Achieved better predictive accuracy compared to competing methods.

Conclusions:

  • PhosPPI is an effective tool for identifying functional phosphorylation sites impacting PPI.
  • The tool facilitates research into phosphorylation-associated disease mechanisms and drug development.
  • PhosPPI is freely available as a web server for public use.