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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.
Protein kinases
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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Phosphorylation01:02

Phosphorylation

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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.
During phosphorylation, protein kinases transfer the terminal phosphate group of ATP to specific amino acid side chains of substrate proteins. Serine, threonine, and tyrosine are the most commonly...
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Updated: Aug 5, 2025

Oligopeptide Competition Assay for Phosphorylation Site Determination
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ScerePhoSite: An interpretable method for identifying fungal phosphorylation sites in proteins using sequence-based

Chao Wang1, Qiang Yang2

  • 1Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China; School of Software Engineering, Chengdu University of Information Technology, Chengdu, China.

Computers in Biology and Medicine
|March 26, 2023
PubMed
Summary

ScerePhoSite is a new machine learning tool for identifying fungal phosphorylation sites. This bioinformatics method improves upon existing tools, aiding in the study of fungal cell signaling and protein function.

Keywords:
BioinformaticsFeature selectionFungalMachine learningProtein phosphorylation site

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Area of Science:

  • Bioinformatics
  • Molecular Biology
  • Mycology

Background:

  • Protein phosphorylation is crucial for cellular processes and signal transduction.
  • Existing in silico tools for phosphorylation site identification are limited for fungal species.
  • This limitation hinders functional studies of fungal phosphorylation.

Purpose of the Study:

  • To develop a machine learning method, ScerePhoSite, for accurate fungal phosphorylation site identification.
  • To enhance the investigation of phosphorylation in fungi.

Main Methods:

  • Utilized hybrid physicochemical features to represent sequence fragments.
  • Employed LGB-based feature importance and sequential forward search for optimal feature selection.
  • Investigated feature contributions using SHAP values.

Main Results:

  • ScerePhoSite demonstrated superior performance compared to existing tools.
  • The method exhibited robust and balanced prediction capabilities.
  • Specific feature impacts on model performance were elucidated.

Conclusions:

  • ScerePhoSite is a valuable bioinformatics tool for pre-screening fungal phosphorylation sites.
  • This tool facilitates functional understanding of phosphorylation modifications in fungi.
  • Complements experimental approaches in fungal research.