xiVIEW: Visualisation of Crosslinking Mass Spectrometry Data

• Published in: Journal of molecular biology 2024-09, Vol.436 (17), p.168656, Article 168656
• Main Authors: Combe, Colin W., Graham, Martin, Kolbowski, Lars, Fischer, Lutz, Rappsilber, Juri
• Format: Article
• Language: English
• Subjects: Cross-Linking Reagents - chemistry; data analysis; Databases, Protein; Mass Spectrometry - methods; Protein Interaction Mapping - methods; Protein Interaction Maps; protein networks; Proteins - chemistry; Software; structures; User-Computer Interface
• ISSN: 0022-2836; 1089-8638; 1089-8638
• DOI: 10.1016/j.jmb.2024.168656

[Display omitted] •Provides accessible crosslink maps to translate data into insights.•Responds to the crosslinking MS field’s call to improve accessibility of its data.•Reads the mzIdentML 1.2.0 proteomics identification data file standard.•Incorporates coordinated views of annotated spectra, 2D networks and 3D structures. Crosslinking mass spectrometry (MS) has emerged as an important technique for elucidating the in-solution structures of protein complexes and the topology of protein–protein interaction networks. However, the expanding user community lacked an integrated visualisation tool that helped them make use of the crosslinking data for investigating biological mechanisms. We addressed this need by developing xiVIEW, a web-based application designed to streamline crosslinking MS data analysis, which we present here. xiVIEW provides a user-friendly interface for accessing coordinated views of mass spectrometric data, network visualisation, annotations extracted from trusted repositories like UniProtKB, and available 3D structures. In accordance with recent recommendations from the crosslinking MS community, xiVIEW (i) provides a standards compliant parser to improve data integration and (ii) offers accessible visualisation tools. By promoting the adoption of standard file formats and providing a comprehensive visualisation platform, xiVIEW empowers both experimentalists and modellers alike to pursue their respective research interests. We anticipate that xiVIEW will advance crosslinking MS-inspired research, and facilitate broader and more effective investigations into complex biological systems.