1. Xilmass: A New Approach toward the Identification of Cross-Linked Peptides
- Author
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Anastassios Economou, Elien Vandermarliere, Friedel Drepper, Kris Gevaert, Şule Yılmaz, Bettina Warscheid, Maša Černič, Lennart Martens, and Niels Hulstaert
- Subjects
0301 basic medicine ,chemistry.chemical_classification ,Chemistry ,Succinimides ,Peptide ,Computational biology ,Tandem mass spectrometry ,Proteomics ,Mass spectrometry ,Combinatorial chemistry ,Analytical Chemistry ,Protein–protein interaction ,03 medical and health sciences ,030104 developmental biology ,Cross-Linking Reagents ,Structural biology ,Fragment (logic) ,Calmodulin ,Tandem Mass Spectrometry ,Humans ,Plectin ,Databases, Protein ,Function (biology) ,Algorithms - Abstract
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 two new concepts: (i) the cross-linked peptides are represented in the search database such that the cross-linking sites are explicitly encoded, and (ii) the scoring function derived from the Andromeda algorithm was adapted to score against a theoretical tandem mass spectrometry (MS/MS) spectrum that contains the peaks from all possible fragment ions of a cross-linked peptide pair. The performance of Xilmass was evaluated against the recently published Kojak and the popular pLink algorithms on a calmodulin-plectin complex data set, as well as three additional, published data sets. The results show that Xilmass typically had the highest number of identified distinct cross-linked sites and also the highest number of predicted cross-linked sites.
- Published
- 2016