UC Irvine

Cross-linking mass spectrometry maps the structural topology of protein complexes by using

the distance between linked residues as spatial constraints, complementing other structural

biology techniques. However, the identification of cross-linked peptides scales poorly with the

number of proteins analyzed. Our lab has previously developed MS-cleavable cross-linkers to

enable the separation of cross-linked peptides prior to sequencing, enabling peptide identifica-

tion using standard peptide search databases. We describe the design and implementation of

platform and application named XLTools for the automated identification of MS-cleavable

cross-linked peptides. XLTools supports open and proprietary data formats and common

peptide search databases, facilitating its integration into existing workflows. Furthermore,

we developed peak-picking and validation algorithms to enable the accurate quantitation of

cross-linked peptides in complex samples. We demonstrate the application of XLTools to

the quantitative analysis of the 26S proteasome cross-linked in vivo and in vitro.