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Related Experiment Video

Updated: Sep 1, 2025

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays
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MLysPRED: graph-based multi-view clustering and multi-dimensional normal distribution resampling techniques to

Yun Zuo1, Yue Hong1, Xiangxiang Zeng2

  • 1Department of Computer Science, Xiamen University, Xiamen 361005, China.

Briefings in Bioinformatics
|August 11, 2022
PubMed
Summary

This study introduces MLysPRED, a novel multi-label computational model for predicting multiple lysine posttranslational modifications (K-PTMs) in proteins. MLysPRED significantly advances K-PTM prediction accuracy and offers a user-friendly web server for researchers.

Keywords:
multi-label prediction modelmultiple K-PTMsresampling techniquessequence encoding

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology and Bioinformatics
  • Proteomics

Background:

  • Lysine posttranslational modifications (K-PTMs) are crucial regulatory events in proteins, impacting biological functions and disease.
  • Existing computational methods primarily predict single K-PTM types, limiting comprehensive analysis.
  • There is a need for advanced tools capable of predicting multiple K-PTMs simultaneously.

Purpose of the Study:

  • To develop a novel multi-label computational model, MLysPRED, for predicting multiple K-PTMs on lysine residues in human proteins.
  • To address the limitations of single-class K-PTM prediction methods.
  • To provide a valuable resource for basic research and drug development.

Main Methods:

  • Development of MLysPRED, a multi-label prediction model utilizing features from human protein sequences.
  • Introduction of three novel multi-label sequence encoding algorithms (MLDBPB, MLPSDAAP, MLPSTAAP) combined with encoding strategies (CHHAA, DR, Kmer).
  • Implementation of a multidimensional normal distribution oversampling technique, graph-based multi-view clustering under-sampling, and a multi-label nearest neighbor algorithm for classification.

Main Results:

  • MLysPRED achieved high performance metrics on independent datasets: Aiming 92.21%, Coverage 94.98%, Accuracy 89.63%, Absolute-True 81.46%, and Absolute-False 0.0682.
  • Comparative analysis demonstrated MLysPRED's superiority over five existing K-PTM predictors.
  • The model shows significant promise for predicting multiple K-PTMs.

Conclusions:

  • MLysPRED represents a significant advancement in computational prediction of multiple lysine posttranslational modifications.
  • The developed model offers improved accuracy and capability for analyzing complex K-PTM patterns.
  • A user-friendly web server is available for experimental scientists, facilitating broader application and research.