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Mining lysine post-translational modification sites by integrating protein language model representations with

Mengqi Luo1, Xiaohong Zhu2, Chen Bai2

  • 1Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.

Proceedings of the National Academy of Sciences of the United States of America
|February 9, 2026
PubMed
Summary

We developed a deep learning framework to identify lysine post-translational modification (PTM) sites by integrating protein sequence and structural data. This computational tool aids in understanding protein regulation and function.

Keywords:
deep learninglysine PTM site miningmolecular dynamics (MD) simulationsprotein language modelstructural information

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology and Bioinformatics
  • Structural Biology

Background:

  • Lysine (Lys/K) residues are crucial for post-translational modifications (PTMs) due to their versatile ε-amino groups, regulating diverse cellular functions.
  • Identifying modified lysine sites computationally requires models that integrate sequence and structural data, minimizing the need for domain-specific feature engineering.

Purpose of the Study:

  • To propose a unified deep learning framework for identifying lysine PTM sites.
  • To enable consistent application across multiple lysine PTM types using a shared modeling strategy.
  • To integrate sequence representations from protein language models with atom-level 3D structural features.

Main Methods:

  • Developed a deep learning framework integrating protein language model-derived sequence representations and atom-level 3D structural features.
  • Applied a shared modeling strategy for consistent prediction across various lysine PTM types.
  • Utilized all-atom molecular dynamics simulations to evaluate the functional relevance of predicted PTM sites on human C-type lectin domain family 12 member A (hCLEC12A).

Main Results:

  • The deep learning framework successfully identified potential PTM sites on hCLEC12A.
  • Molecular dynamics simulations revealed that predicted lysine residues impact the stability and binding of the hCLEC12A-antibody 50C1 complex.
  • The model demonstrates consistent applicability to multiple lysine PTM types.

Conclusions:

  • Presented an integrative computational framework for efficient lysine PTM site mining and functional analysis.
  • The framework effectively combines sequence and structural information for PTM prediction.
  • Predicted lysine modifications play a significant role in protein complex stability and binding interactions.