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Updated: Feb 27, 2026

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iMulti-HumPhos: a multi-label classifier for identifying human phosphorylated proteins using multiple kernel learning

Md Al Mehedi Hasan1, Shamim Ahmad1, Md Khademul Islam Molla1

  • 1Department of Computer Science & Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh. mehedi_ru@yahoo.com shamim_cst@yahoo.com khademul.cse@ru.ac.bd.

Molecular Biosystems
|July 7, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces iMulti-HumPhos, a new computational tool for identifying multi-label phosphorylated proteins. It accurately predicts protein phosphorylation, aiding basic research and drug development.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Bioinformatics

Background:

  • Protein phosphorylation regulates protein structure and function, making identification of phosphorylated proteins crucial for research and drug development.
  • Existing computational methods primarily focus on identifying phosphorylation sites within known phosphorylated proteins, leaving a gap in predicting whether an uncharacterized protein is phosphorylated.
  • Phosphorylation occurs at serine, threonine, and tyrosine residues, necessitating multi-label prediction capabilities.

Purpose of the Study:

  • To develop a novel computational tool, iMulti-HumPhos, for predicting multi-label phosphorylated proteins.
  • To address the limitations of existing methods in identifying uncharacterized phosphorylated proteins.
  • To provide a user-friendly web server for accessing the developed prediction tool.

Main Methods:

  • Feature extraction from protein sequences into three distinct sets.
  • Development of individual kernels for each feature set, combined using multiple kernel learning.
  • Construction of a multi-label predictor utilizing support vector machines (SVMs) trained with a combined kernel.
  • Balancing skewed training data using the Different Error Costs method.

Main Results:

  • The iMulti-HumPhos predictor demonstrated significantly superior performance compared to the existing Multi-iPPseEvo predictor.
  • The developed computational tool effectively predicts multi-label phosphorylated proteins.
  • The system successfully balanced the effects of skewed training datasets.

Conclusions:

  • iMulti-HumPhos offers an improved computational approach for identifying multi-label phosphorylated proteins.
  • The tool has practical implications for basic biological research and pharmaceutical development.
  • A web server is available, facilitating broader accessibility and application of the iMulti-HumPhos predictor.