Abstracts

Cross-linking mass spectrometry maps the structural topology of protein complexes by usingthe distance between linked residues as spatial constraints, complementing other structuralbiology techniques. However, the identification of cross-linked peptides scales poorly with thenumber of proteins analyzed. Our lab has previously developed MS-cleavable cross-linkers toenable the separation of cross-linked peptides prior to sequencing, enabling peptide identifica-tion using standard peptide search databases. We describe the design and implementation ofplatform and application named XLTools for the automated identification of MS-cleavablecross-linked peptides. XLTools supports open and proprietary data formats and commonpeptide search databases, facilitating its integration into existing workflows. Furthermore,we developed peak-picking and validation algorithms to enable the accurate quantitation ofcross-linked peptides in complex samples. We demonstrate the application of XLTools tothe quantitative analysis of the 26S proteasome cross-linked in vivo and in vitro.