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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Related Experiment Video

Updated: Jun 9, 2025

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays
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PreMLS: The undersampling technique based on ClusterCentroids to predict multiple lysine sites.

Yun Zuo1, Xingze Fang1, Jiayong Wan1

  • 1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.

Plos Computational Biology
|October 22, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational method to predict multiple simultaneous post-translational lysine modifications (K-PTMs). The developed model accurately identifies these complex modifications, aiding biological research and drug discovery.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Proteomics

Background:

  • Post-translational modifications (PTMs) at lysine residues are crucial for protein function and physiological processes.
  • Existing research often focuses on single lysine PTMs, neglecting concurrent modifications and leading to data imbalance issues.

Purpose of the Study:

  • To develop a classification system for predicting concurrent multiple modifications at a single lysine residue.
  • To address the challenge of class imbalance in predicting multiple lysine PTMs.

Main Methods:

  • Utilized a multi-label position-specific triad amino acid propensity algorithm for feature encoding.
  • Introduced PreMLS, a novel undersampling algorithm (ClusterCentroids based on MiniBatchKmeans) to handle class imbalance.
  • Constructed a convolutional neural network for biological sequence analysis to predict multiple lysine modification sites.

Main Results:

  • The developed convolutional neural network model significantly outperformed existing methods (iMul-kSite, predML-Site) in predicting multiple lysine modification sites.
  • The model demonstrated high accuracy through five-fold cross-validation and independent testing.
  • An open-access predictive script was created for enhanced accessibility.

Conclusions:

  • The study provides a valuable tool for prioritizing potential lysine modification sites for further biological assays.
  • The findings advance the understanding of complex PTMs and support drug development efforts.
  • Accurate prediction of concurrent K-PTMs is essential for comprehensive biological research.