Characterization of the Interaction between Arginine Methyltransferase Hmt1 and Its Substrate Npl3: Use of Multiple Cross-Linkers, Mass Spectrometric Approaches, and Software Platforms.
Daniela-Lee SmithMichael GötzeTara K BartolecGene Hart-SmithMarc R WilkinsPublished in: Analytical chemistry (2018)
This study investigated the enzyme-substrate interaction between Saccharomyces cerevisiae arginine methyltransferase Hmt1p and nucleolar protein Npl3p, using chemical cross linking/mass spectrometry (XL/MS). We show that XL/MS can capture transient interprotein interactions that occur during the process of methylation, involving a disordered region in Npl3p with tandem SRGG repeats, and we confirm that Hmt1p and Npl3p exist as homomultimers. Additionally, the study investigated the interdependencies between variables of an XL/MS experiment that lead to the identification of identical or different cross-linked peptides. We report that there are substantial benefits, in terms of biologically relevant cross-links identified, that result from the use of two mass-spectrometry-cleavable cross-linkers [disuccinimido sulfoxide (DSSO) and disuccinimido dibutyric urea (DSBU)], two fragmentation approaches [collision-induced dissociation and electron-transfer dissociation (CID+ETD)] and stepped high-energy collision dissociation (HCD)], and two programs (MeroX and XlinkX). We also show that there are specific combinations of XL/MS methods that are more successful than others for the two proteins investigated here; these are explored in detail in the text. Data are available via ProteomeXchange with identifier PXD008348.
Keyphrases
- mass spectrometry
- electron transfer
- liquid chromatography
- multiple sclerosis
- ms ms
- saccharomyces cerevisiae
- amino acid
- high performance liquid chromatography
- gas chromatography
- capillary electrophoresis
- high resolution
- nitric oxide
- atomic force microscopy
- public health
- randomized controlled trial
- machine learning
- small molecule
- protein protein
- electronic health record
- drug induced
- brain injury
- simultaneous determination
- deep learning
- bioinformatics analysis
- single molecule