MethylQuant: A Tool for Sensitive Validation of Enzyme-Mediated Protein Methylation Sites from Heavy-Methyl SILAC Data

Abstract

The study of post-translational methylation is hampered by the fact that large-scale LC-MS/MS experiments produce high methylpeptide false discovery rates (FDRs). The use of heavy-methyl stable isotope labeling by amino acids in cell culture (heavy-methyl SILAC) can drastically reduce these FDRs; however, this approach is limited by a lack of heavy-methyl SILAC compatible software. To fill this gap, we recently developed MethylQuant. Here, using an updated version of MethylQuant, we demonstrate its methylpeptide validation and quantification capabilities and provide guidelines for its best use. Using reference heavy-methyl SILAC data sets, we show that MethylQuant predicts with statistical significance the true or false positive status of methylpeptides in samples of varying complexity, degree of methylpeptide enrichment, and heavy to light mixing ratios. We introduce methylpeptide confidence indicators, MethylQuant Confidence and MethylQuant Score, and demonstrate their strong performance in complex samples characterized by a lack of methylpeptide enrichment. For these challenging data sets, MethylQuant identifies 882 of 1165 true positive methylpeptide spectrum matches (i.e., >75% sensitivity) at high specificity (<2% FDR) and achieves near-perfect specificity at 41% sensitivity. We also demonstrate that MethylQuant produces high accuracy relative quantification data that are tolerant of interference from coeluting peptide ions. Together MethylQuant's capabilities provide a path toward routine, accurate characterizations of the methylproteome using heavy-methyl SILAC.

ProMetheusDB: An In-Depth Analysis of the High-Quality Human Methyl-proteome

“…After training the model, we observed that the dRT parameter of the doublets was the least important for discriminating true and false peptide pairs; this is in contrast with the first iteration of hmSEEKER, where dRT and ME were the main predictors of methyl-peptide doublets, and the H/L ratio parameter was introduced later. This choice of features also differentiates our ML model from the one adopted by MethylQuant ( 25 ), which scores methyl-peptide pairs based on the isotope distribution and elution profile correlation of the two peaks. Moreover, while we set the initial cutoffs with the aim of minimizing the number of false positives, the predictions produced by the ML model allowed us to find a compromise between false positives and false negatives, by recovering 768 false negatives versus the introduction of only 100 new false positives, thus keeping the FDR of hmSEEKER at 2.4% (precision = 0.976).…”

ProMetheusDB: an in-depth analysis of the high-quality human methyl-proteome

“…After training the model, we observed that the dRT parameter of the doublets was the least important for discriminating true and false peptide pairs; this is in contrast with the first iteration of hmSEEKER, where dRT and ME were the main predictors of methyl-peptide doublets and the H/L ratio parameter was introduced later. This choice of features also differentiates our ML model from the one adopted by MethylQuant (Tay et al, 2018), which scores methyl-peptide pairs based on the isotope distribution and elution profile correlation of the two peaks.…”

“…Common peptide search engines, such as Andromeda or Mascot, can handle isotopic labelling of amino acids (as in traditional SILAC) but cannot be used when the label is encoded by a variable modification. To address this problem, our and M. Wilkins’s group developed two computation tools, tailored to process hmSILAC MS data for the identification of peptides methylated in vivo named hmSEEKER and MethylQuant, respectively (Massignani et al, 2019; Tay et al, 2018). Both tools have been successfully employed to expand the high-confidence annotation of the methyl-proteome in Homo Sapiens and S. cerevisiae , while tightly controlling the number of false-positive identifications (Geoghegan et al, 2015; Hamey et al, 2021; Musiani et al, 2019; Musiani et al, 2020).…”

Inhibiting Arginine Methylation as a Tool to Investigate Cross-Talk with Methylation and Acetylation Post-Translational Modifications in a Glioblastoma Cell Line

“…Although the α-ADMA, α-SDMA and α-Lys acetylation antibodies that we have used are state-of-the art in the field [26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46], mass spectrometry-based proteomics approaches would complement our experiments and enable the identification of the proteins and mechanisms involved in PTM cross-talks. In particular, approaches to labelling Arg residues modified by methylation with 14 C and 13 CD 3 have been developed [47,48], and would prove very useful to identify the specific proteins undergoing methylation in further investigations of ADMA–SDMA cross-talk. In this respect, the development of tools for studying PTM cross-talk is critical and this Special Issue of Proteomes will undoubtedly contribute to it.…”