| Literature DB >> 32668242 |
Kyle K Biggar1, Francois Charih2, Huadong Liu3, Yasser B Ruiz-Blanco4, Leanne Stalker5, Anand Chopra1, Justin Connolly1, Hemanta Adhikary1, Kristin Frensemier1, Matthew Hoekstra1, Marek Galka3, Qi Fang3, Christopher Wynder3, William L Stanford6, James R Green7, Shawn S-C Li8.
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.Entities:
Keywords: KDM5b; histone H1; histone H2B; histone marks; lysine methylation; machine learning; methyllysine proteome; non-histone methylation
Mesh:
Substances:
Year: 2020 PMID: 32668242 DOI: 10.1016/j.celrep.2020.107896
Source DB: PubMed Journal: Cell Rep Impact factor: 9.423