Protein complexes in cells by AI ‐assisted structural proteomics

Abstract: Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate‐based approach to systematically model novel protein assemblies. Here, we use a combination of in‐cell crosslinking mass spectrometry and co‐fractionation mass spectrometry (CoFrac‐MS) to identify protein–protein interactions in the model Gram‐positive bacterium Bacillus subtilis. We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the SubtiWiki database with AlphaFold‐Multimer and, after controlling for the false‐positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase, and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein–protein interactions inside intact cells, provides structural insight into their interaction interfaces, and is applicable to genetically intractable organisms, including pathogenic bacteria.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Protein complexes in cells by AI ‐assisted structural proteomics ; day:23 ; month:02 ; year:2023 ; extent:20 ; Molecular systems biology ; (23.02.2023) (gesamt 20)

Beteiligte, Orts- und Zeitangaben

• Urheber: O'Reilly, Francis J. ; Graziadei, Andrea ; Forbrig, Christian ; Bremenkamp, Rica ; Charles, Kristine ; Lenz, Swantje ; Elfmann, Christoph ; Fischer, Lutz ; Stülke, Jörg ; Rappsilber, Juri

Weitere Informationen

• DOI: 10.15252/msb.202311544
• URN: urn:nbn:de:101:1-2023022314141984035852
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 14.08.2025, 10:49 MESZ