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Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation

Zhengyi Li, Menglu Li, Lida Zhu, Wen Zhang

TL;DR

Multigranularity structure-aware representation learning is designed to learn neighborhood structure representations at the amino acid, atom, and whole protein granularity from AlphaFold predicted structures, followed by utilizing contrastive learning to optimize the structure representations.

Abstract

Protein post-translational modification (PTM) site prediction is a fundamental task in bioinformatics. Several computational methods have been developed to predict PTM sites. However, existing methods ignore the structure information and merely utilize protein sequences. Furthermore, designing a more fine-grained structure representation learning method is urgently needed as PTM is a biological event that occurs at the atom granularity. In this paper, we propose a PTM site prediction method by Coupling of Multi-Granularity structure and Multi-Scale sequence representation, PTM-CMGMS for brevity. Specifically, multigranularity structure-aware representation learning is designed to learn neighborhood structure representations at the amino acid, atom, and whole protein granularity from AlphaFold predicted structures, followed by utilizing contrastive learning to optimize the structure representations.Additionally, multi-scale sequence representation learning is used to extract context sequence information, and motif generated by aligning all context sequences of PTM sites assists the prediction. Extensive experiments on three datasets show that PTM-CMGMS outperforms the state-of-the-art methods.

Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation

TL;DR

Multigranularity structure-aware representation learning is designed to learn neighborhood structure representations at the amino acid, atom, and whole protein granularity from AlphaFold predicted structures, followed by utilizing contrastive learning to optimize the structure representations.

Abstract

Protein post-translational modification (PTM) site prediction is a fundamental task in bioinformatics. Several computational methods have been developed to predict PTM sites. However, existing methods ignore the structure information and merely utilize protein sequences. Furthermore, designing a more fine-grained structure representation learning method is urgently needed as PTM is a biological event that occurs at the atom granularity. In this paper, we propose a PTM site prediction method by Coupling of Multi-Granularity structure and Multi-Scale sequence representation, PTM-CMGMS for brevity. Specifically, multigranularity structure-aware representation learning is designed to learn neighborhood structure representations at the amino acid, atom, and whole protein granularity from AlphaFold predicted structures, followed by utilizing contrastive learning to optimize the structure representations.Additionally, multi-scale sequence representation learning is used to extract context sequence information, and motif generated by aligning all context sequences of PTM sites assists the prediction. Extensive experiments on three datasets show that PTM-CMGMS outperforms the state-of-the-art methods.
Paper Structure (23 sections, 14 equations, 5 figures, 2 tables)

This paper contains 23 sections, 14 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Problem Formulation
  5. Multi-granularity Structure-aware Representation Learning
  6. Amino acid granularity.
  7. Atom granularity.
  8. Whole protein granularity.
  9. Representation optimization.
  10. Multi-scale Sequence Representation Learning
  11. Context sequence information extraction.
  12. Motif-based information extraction.
  13. Model Training
  14. Experiments
  15. Experimental Settings
  16. ...and 8 more sections

Figures (5)

  • Figure 1: The example of lysine crotonylation, which occurs on the side chain nitrogen atom marked in red. The other atoms of lysine are marked in yellow.
  • Figure 2: The overview of our proposed PTM-CMGMS.
  • Figure 3: Results of PTM-CMGMS and its variants on three datasets.
  • Figure 4: Results of various context sequence information extraction architectures on the Crotonylation dataset.
  • Figure 5: Results of PTM-CMGMS and Adapt-Kcr in dealing with PTM sites with different numbers of non-local contact residues on the Crotonylation dataset.