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Proteome-wide Prediction of Lysine Methylation Leads to Identification of H2BK43 Methylation and Outlines the Potential Methyllysine Proteome

  • Kyle K. Biggar
  • , Francois Charih
  • , Huadong Liu
  • , Yasser B. Ruiz-Blanco
  • , Leanne Stalker
  • , Anand Chopra
  • , Justin Connolly
  • , Hemanta Adhikary
  • , Kristin Frensemier
  • , Matthew Hoekstra
  • , Marek Galka
  • , Qi Fang
  • , Christopher Wynder
  • , William L. Stanford
  • , James R. Green
  • , Shawn S.C. Li
  • Carleton University
  • Western University
  • University of Duisburg-Essen
  • University of Guelph
  • University of Ottawa

Research output: Contribution to journal › Article › peer-review

25 Scopus citations

Abstract

Protein Lys methylation plays a critical role in numerous cellular processes, but it is challenging to identify Lys methylation in a systematic manner. Here we present an approach combining in silico prediction with targeted mass spectrometry (MS) to identify Lys methylation (Kme) sites at the proteome level. We develop MethylSight, a program that predicts Kme events solely on the physicochemical properties of residues surrounding the putative methylation sites, which then requires validation by targeted MS. Using this approach, we identify 70 new histone Kme marks with a 90% validation rate. H2BK43me2, which undergoes dynamic changes during stem cell differentiation, is found to be a substrate of KDM5b. Furthermore, MethylSight predicts that Lys methylation is a prevalent post-translational modification in the human proteome. Our work provides a useful resource for guiding systematic exploration of the role of Lys methylation in human health and disease.

Original languageEnglish
Article number107896
JournalCell Reports
Volume32
Issue number2
DOIs
StatePublished - 14 Jul 2020
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • KDM5b
  • histone H1
  • histone H2B
  • histone marks
  • lysine methylation
  • machine learning
  • methyllysine proteome
  • non-histone methylation

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