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Identification of Post-translational Modifications of Plant Protein Complexes
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Capsule network for protein post-translational modification site prediction.

Duolin Wang1,2, Yanchun Liang2,3, Dong Xu1,2

  • 1Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.

Bioinformatics (Oxford, England)
|December 7, 2018
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Summary

We developed a Capsule Network (CapsNet) to accurately predict protein post-translational modification (PTM) sites. This deep learning approach excels with small datasets and enhances understanding of PTM recognition mechanisms.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Protein post-translational modifications (PTMs) are crucial for cellular functions.
  • Existing computational PTM site prediction methods require improvement in accuracy.
  • Deep learning architectures offer potential for enhanced PTM prediction.

Purpose of the Study:

  • To develop and evaluate a Capsule Network (CapsNet) for predicting various protein PTM sites.
  • To assess CapsNet's performance against existing methods, particularly with limited training data.
  • To explore the utility of CapsNet's internal features for motif detection and kinase substrate discrimination.

Main Methods:

  • Implementation of a Capsule Network (CapsNet) architecture for PTM site prediction.
  • Training and testing CapsNet on diverse PTM datasets.
  • Comparison of CapsNet performance against baseline models (e.g., MusiteDeep) and other tools.
  • Analysis of capsule length for confidence estimation and internal capsule features for motif discovery.

Main Results:

  • CapsNet demonstrated superior prediction accuracy compared to baseline methods for multiple PTM types.
  • The model effectively utilizes small training datasets.
  • Capsule length provided reliable confidence estimates for PTM predictions.
  • Internal capsule features served as effective motif detectors for phosphorylation sites without kinase-specific labels.
  • CapsNet generated robust representations for distinguishing kinase substrates across different families.

Conclusions:

  • CapsNet is a powerful tool for accurate protein PTM site prediction, especially with limited data.
  • The study provides insights into PTM recognition mechanisms.
  • CapsNet shows promise for broader applications in bioinformatics.