Dataset Information

Xilmass: a new approach towards the identification of cross-linked peptides

ABSTRACT: Chemical cross-linking coupled with mass spectrometry plays an important role in unravelling protein interactions, especially weak and transient interactions. Moreover, cross-linking complements several structure determination approaches such as cryo-EM. Although several computational approaches are available for the annotation of spectra obtained from cross-linked peptides, there remains room for improvement. Here, we present Xilmass, a novel algorithm to identify cross-linked peptides that introduces two new concepts: (i) the cross-linked peptides are represented in a novel way in the search database that explicitly encodes cross-linking sites and (ii) the scoring function from the Andromeda algorithm was adapted to score against a theoretical MS spectrum that contains the peaks from all the possible fragment ions of a cross-linked peptide pair. The performance of Xilmass was subsequently evaluated against the recently published Kojak and the popular pLink algorithms on a data set that contains calmodulin-plectin.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Lennart Martens

PROVIDER: MSV000080758 | MassIVE | Wed Mar 29 07:19:00 BST 2017

SECONDARY ACCESSION(S): PXD003880

REPOSITORIES: MassIVE

ACCESS DATA

Publications

Xilmass: A New Approach toward the Identification of Cross-Linked Peptides.

Yılmaz Şule Ş Drepper Friedel F Hulstaert Niels N Černič Maša M Gevaert Kris K Economou Anastassios A Warscheid Bettina B Martens Lennart L Vandermarliere Elien E

Analytical chemistry 20160928 20

Chemical cross-linking coupled with mass spectrometry plays an important role in unravelling protein interactions, especially weak and transient ones. Moreover, cross-linking complements several structural determination approaches such as cryo-EM. Although several computational approaches are available for the annotation of spectra obtained from cross-linked peptides, there remains room for improvement. Here, we present Xilmass, a novel algorithm to identify cross-linked peptides that introduces ...[more]

PMID: 27642655

Similar Datasets

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:RNA-binding proteins (RBPs) are intimately involved in all aspects of RNA processing and regulation and are linked to neurodegenerative diseases and cancer. Therefore, understanding the relationship between RBPs and their RNA targets is critical for a broader understanding of post-transcriptional regulation in normal and disease processes. The majority of approaches to study RNA-protein interactions focus on single RBPs, however there are many hundreds RBPs encoded in the human genome, and each cell type expresses a different catalog of these regulatory molecules, greatly limiting the ability of these single RBP approaches to capture the global landscape of RNA-protein interactions. We and others have developed approaches to globally catalog regions of mRNAs that are bound by proteins in an unbiased manner. Here we describe a detailed protocol for performing our RNase-mediated protein footprint sequencing approach, termed protein interaction profile sequencing (PIP-seq). In this protocol, RNA-protein interactions are stabilized by cross-linking, and un-bound regions are digested with RNases, leaving only the protein-bound regions intact. To control for RNase insensitive regions, proteins are first denatured and then RNP complexes are subject to RNase treatment. After high-throughput sequencing of remaining fragments, peak calling is performed to identify protein protected sites (PPSs). We describe the application of this protocol to a human embryonic kidney cell line and perform basic quality control, reproducibility and benchmarking analyses. Finally, we describe the landscape of protein-interactions in HEK293T cells, underscoring the value of this approach. Future applications of this method to study the dynamics of RNA-protein interactions in developmental processes will help to uncover the role of RBPs in post-transcriptional regulation. Protein interaction profile sequencing (PIP-seq) in HEK293T cells. Three replicates of formaldehyde cross-linked PIP-seq are described

Dataset Information

Xilmass: a new approach towards the identification of cross-linked peptides

Publications

Xilmass: A New Approach toward the Identification of Cross-Linked Peptides.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure