# Modelling protein complexes with crosslinking mass spectrometry and deep learning

Article · Stahl K, Warneke R, Demann L, Bremenkamp R, Hormes B, Brock O, Stülke J, Rappsilber J

- Archive id: doi:10.1038/s41467-024-51771-2
- Published: 2024-09-09
- Venue: Nature communications
- DOI: https://doi.org/10.1038/s41467-024-51771-2
- PMID: 39251624
- PMCID: PMC11383924
- Citations: 90
- Source URL: https://doi.org/10.1038/s41467-024-51771-2
- PDF: https://europepmc.org/articles/PMC11383924/pdf
- Provenance: europepmc

## Abstract

Scarcity of structural and evolutionary information on protein complexes poses a challenge to deep learning-based structure modelling. We integrate experimental distance restraints obtained by crosslinking mass spectrometry (MS) into AlphaFold-Multimer, by extending AlphaLink to protein complexes. Integrating crosslinking MS data substantially improves modelling performance on challenging targets, by helping to identify interfaces, focusing sampling, and improving model selection. This extends to single crosslinks from whole-cell crosslinking MS, opening the possibility of whole-cell structural investigations driven by experimental data. We demonstrate this by revealing the molecular basis of iron homoeostasis in Bacillus subtilis.

_Abstract reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [AI Protein Design & Structure Prediction](https://space-kitty.com/topics/protein-design)

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