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Xlink-identifier: an automated data analysis platform for confident identifications of chemically cross-linked peptides using tandem mass spectrometry
Authors:Du Xiuxia  Chowdhury Saiful M  Manes Nathan P  Wu Si  Mayer M Uljana  Adkins Joshua N  Anderson Gordon A  Smith Richard D
Institution:Department of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina 28023, USA. xiuxia.du@uncc.edu
Abstract:Chemical cross-linking combined with mass spectrometry provides a powerful method for identifying protein-protein interactions and probing the structure of protein complexes. A number of strategies have been reported that take advantage of the high sensitivity and high resolution of modern mass spectrometers. Approaches typically include synthesis of novel cross-linking compounds, and/or isotopic labeling of the cross-linking reagent and/or protein, and label-free methods. We report Xlink-Identifier, a comprehensive data analysis platform that has been developed to support label-free analyses. It can identify interpeptide, intrapeptide, and deadend cross-links as well as underivatized peptides. The software streamlines data preprocessing, peptide scoring, and visualization and provides an overall data analysis strategy for studying protein-protein interactions and protein structure using mass spectrometry. The software has been evaluated using a custom synthesized cross-linking reagent that features an enrichment tag. Xlink-Identifier offers the potential to perform large-scale identifications of protein-protein interactions using tandem mass spectrometry.
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