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Modelling protein complexes with crosslinking mass spectrometry and deep learning

Affiliations
  • 1Technische Universität Berlin, Chair of Bioanalytics, Berlin, Germany.
  • 2Georg-August-Universität Göttingen, Department of General Microbiology, Institute for Microbiology & Genetics, GZMB, Göttingen, Germany.
  • 3Technische Universität Berlin, Robotics and Biology Laboratory, Berlin, Germany.
  • 4Science of Intelligence, Research Cluster of Excellence, Berlin, Germany.
  • 5Georg-August-Universität Göttingen, Department of General Microbiology, Institute for Microbiology & Genetics, GZMB, Göttingen, Germany. jstuelk@gwdg.de.
  • 6Technische Universität Berlin, Chair of Bioanalytics, Berlin, Germany. juri.rappsilber@tu-berlin.de.
  • 7Si-M/”Der Simulierte Mensch”, a Science Framework of Technische Universität Berlin and Charité – Universitätsmedizin Berlin, Berlin, Germany. juri.rappsilber@tu-berlin.de.
  • 8Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK. juri.rappsilber@tu-berlin.de.

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September 9, 2024

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.

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