Project description:Protein phosphorylation is vital for the regulation of cellular signaling. Isobaric tag-based proteomic techniques, such as tandem mass tags (TMT), can measure the relative phosphorylation states of peptides in a multiplexed format. However, the overall low stoichiometry of protein phosphorylation constrains the analytical depth of phosphopeptide analysis by mass spectrometry, thereby requiring robust and sensitive workflows. Here we evaluate and optimize high-Field Asymmetric waveform Ion Mobility Spectrometry (FAIMS) coupled to Orbitrap Tribrid mass spectrometers for the analysis of TMT10plex-labeled phosphopeptides. We determined that using FAIMS-SPS-MS3 with three compensation voltages (CV) in a single method minimizes inter-CV overlap and maximizes peptide coverage (e.g., CV=-40V/-60V/-80V) and that consecutive analyses using CID-MSA and HCD fragmentation at the MS2 stage increases the depth of phosphorylation analysis.
Project description:High-resolution MS/MS spectra of peptides can be deisotoped to identify monoisotopic masses of peptide fragments. The use of such masses should improve protein identification rates. However, deisotoping is not universally used and its benefits have not been fully explored. Here, we developed MS2-Deisotoper, a tool for use prior to database search, to identify monoisotopic peaks in centroided MS/MS spectra. MS2-Deisotoper works by comparing the mass and relative intensity of each peptide fragment peak to every other peak of greater mass, and by applying a set of rules concerning mass and intensity differences. After comprehensive parameter optimisation, we show that MS2-Deisotoper could improve the number of peptide spectrum matches (PSMs) identified by up to 8.2% and proteins by up to 2.8%. It was effective with SILAC and non-SILAC MS/MS data. The identification of unique peptide sequences was also improved, increasing the number of human proteoforms by 3.7%. Detailed investigation of results showed that deisotoping increases Mascot ion scores, improves FDR estimation for PSMs and leads to greater protein sequence coverage. At a peptide level, we found that the efficacy of deisotoping was affected by peptide mass and charge. MS2-Deisotoper can be used via a user interface or as a command-line tool.
Project description:Over the past few decades cross-linking mass spectrometry (XLMS) has become a powerful tool for identification of protein-protein interactions and for gaining insight into the structures of proteins in living cells, tissues, and organelles. The development of new crosslinkers, enrichment strategies and data acquisition methods led to the establishment of numerous new software tools specifically for the analysis and interpretation of cross-linking data. We previously published one of these tools called MS Annika, a cross-linking search engine which can accurately identify cross-linked peptides in MS2 spectra from a variety of different MS-cleavable crosslinkers. In this publication we present an updated MS Annika and a new search algorithm that additionally supports processing of data from MS2-MS3-based approaches and identification of peptides from MS3 spectra. In the new MS2-MS3 search algorithm, MS3 spectra are matched to their corresponding precursor doublet peak in the MS2 scan to identify the crosslink modification and the monoisotopic peptide mass. This information is then used to adjust the MS3 spectra for search with MS Amanda, our in-house developed peptide search engine, to identify the cross-linked peptides. Peptides that are identified in the MS2 scan and one or more of the associated product MS3 scans are re-scored with a novel scoring function to reflect the increased confidence. Finally, the detected cross-links are validated by estimating the false discovery rate (FDR) using a target-decoy approach. We evaluated the MS3-search-capabilities of MS Annika on five different datasets covering a variety of experimental approaches and compared it to XlinkX and MaXLinker, two other cross-linking search engines that support MS3 crosslink identification. Three of the datasets were benchmark datasets of synthetic peptides that allow calculation of an experimentally validated FDR, and we show that MS Annika detects up to 4 times more true unique crosslinks than MaXLinker and up to 35% more than XlinkX while simultaneously yielding less false positive hits and therefore a more accurate FDR than the other two search engines. Additionally, for the other two datasets we could show that MS Annika finds between 74% to 2.5 times more crosslinks at 1% estimated FDR and reveals protein-protein interactions that are not detected by either XlinkX or MaXLinker.
Project description:Despite the overwhelming information about sRNAs, one of the biggest challenges in the sRNA field is characterizing sRNA targetomes. Thus, we develop a novel method to identify RNAs that interact with a specific sRNA, regardless of the type of regulation (positive or negative) or targets (mRNA, tRNA, sRNA). This method is called MAPS: MS2 affinity purification coupled with RNA sequencing. As proof of principle, we identified RNAs bound to RybB, a well-characterized E. coli sRNA. Identification of RNAs co-purified with MS2-RybB in a rne131 ΔrybB strain. RybB (without MS2) was used as control
Project description:Despite the overwhelming information about sRNAs, one of the biggest challenges in the sRNA field is characterizing sRNA targetomes. Thus, we develop a novel method to identify RNAs that interact with a specific sRNA, regardless of the type of regulation (positive or negative) or targets (mRNA, tRNA, sRNA). This method is called MAPS: MS2 affinity purification coupled with RNA sequencing. As proof of principle, we identified RNAs bound to RyhB, a well-characterized E. coli sRNA. Identification of RNAs co-purified with MS2-RyhB in a rne131 ?ryhB strain. RyhB (without MS2) was used as control
Project description:Comprehensive mass spectrometry (MS)-based proteomics is now feasible, but reproducible and multiplexed quantification remains challenging especially for analysis of post-translational modifications (PTMs), such as phosphorylation. Here we compared the most popular quantification techniques for phosphoproteomics in context of cell-signaling studies: label-free quantification (LFQ), stable isotope labeling by amino acids in cell culture (SILAC) and MS2- and MS3-measured tandem mass tags (TMT). In a mixed species comparison with fixed phosphopeptide-ratios, we found LFQ and SILAC to be the most accurate techniques. MS2-based TMT suffered from substantial ratio compression, which MS3-based TMT could partly rescue. However, when analyzing phosphoproteome changes in the DNA damage response (DDR), we found that MS3-based TMT was outperformed by MS2-based TMT as it identified most significantly regulated phosphopeptides due to its higher precision and higher number of identifications. Finally, we show that the high accuracy of MS3-based TMT is crucial for determination of phosphorylation site stoichiometry using a novel multiplexing-dependent algorithm.
Project description:In this project we want to compare ovarian cell cancer treated or non treated with cis-platinum. As acquisition strategy we have used a Real Time Search MS3 method in an Orbitrap Eclipse. The acquisition cycle began with an MS1 scanwhere the most intense ions were selected for fragmentation in the ion trap using CID. MS2 spectra were searched in real time with data acquisition using the sp-human database. MS2 spectra with an Xcorr greater than or equal to 1 and less than 10 ppm precursor mas error, triggered the submission of an MS3 spectrum to the instrument. MS3 spectrum, were collected using the multinotch MS3-based TMT method, in a way were ten MS2 fragment ions were captured in the MS3 precursor population using isolation waveforms with multiple frequency notches
Project description:We developed TMMF strategy (Targeted MS strategy combined with Multi-Fragmentation) which provided plenty information of targeted glycopeptides based on complementary MS2 spectra from HCD, ETD and CID in a single MS run.