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Related Concept Videos

Protein Kinases and Phosphatases02:54

Protein Kinases and Phosphatases

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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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Many proteins in the cell are regulated by phosphorylation, the addition of a phosphate group. A family of enzymes called kinases...
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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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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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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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GraphPhos: Predict Protein-Phosphorylation Sites Based on Graph Neural Networks.

Zeyu Wang1, Xiaoli Yang1, Songye Gao1

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China.

International Journal of Molecular Sciences
|February 13, 2025
PubMed
Summary

This study introduces GraphPhos, a novel graph neural network model for predicting protein phosphorylation sites. GraphPhos enhances prediction accuracy by integrating sequence and structural protein features.

Keywords:
graph neural networkpost-translational modificationprediction of protein-phosphorylation sites

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Protein phosphorylation is a crucial post-translational modification regulating numerous cellular processes.
  • Accurate identification of phosphorylation sites is essential for understanding protein function and signaling pathways.

Purpose of the Study:

  • To develop an advanced computational model for predicting protein phosphorylation sites.
  • To improve the accuracy and efficiency of phosphorylation site identification using integrated features.

Main Methods:

  • Developed GraphPhos, a graph neural network (GNN) model for phosphorylation site prediction.
  • Integrated sequence-derived features (manual extraction, pre-trained language models) with structure-derived features (secondary structure contact maps).
  • Applied GNNs to the entire protein sequence and its contact graph for comprehensive site prediction.

Main Results:

  • GraphPhos demonstrated significant improvements in prediction accuracy for serine (≥8%), threonine (≥15%), and tyrosine (≥12%) sites.
  • Achieved an average accuracy improvement of 7% compared to models predicting individual amino acid categories.
  • The model effectively utilizes both sequence and structural information for enhanced prediction.

Conclusions:

  • GraphPhos represents a powerful new tool for identifying protein phosphorylation sites.
  • The integration of sequence and structure features within a GNN framework offers superior prediction performance.
  • This approach advances research in protein phosphorylation and its associated biological functions.