TY - JOUR
T1 - Protein complexes in cells by AI-assisted structural proteomics
AU - O'Reilly, Francis J.
AU - Graziadei, Andrea
AU - Forbrig, Christian
AU - Bremenkamp, Rica
AU - Charles, Kristine
AU - Lenz, Swantje
AU - Elfmann, Christoph
AU - Fischer, Lutz
AU - Stülke, Jörg
AU - Rappsilber, Juri
N1 - Funding Information:
We thank Dr. Panagiotis Kastritis and Dr. Steven Johnson for critical reading of the manuscript. We are grateful to Lily Rose for the help with some two‐hybrid analyses. This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2008 – 390540038 – UniSysCat and project 426290502 and, in part, by the Wellcome Trust (Grant number 203149). For the purpose of open access, the authors have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. This article was prepared while FOR was employed at Technische Universität Berlin. The opinions expressed in this article are FOR's own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. Open Access funding enabled and organized by Projekt DEAL.
Funding Information:
We thank Dr. Panagiotis Kastritis and Dr. Steven Johnson for critical reading of the manuscript. We are grateful to Lily Rose for the help with some two-hybrid analyses. This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2008 – 390540038 – UniSysCat and project 426290502 and, in part, by the Wellcome Trust (Grant number 203149). For the purpose of open access, the authors have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. This article was prepared while FOR was employed at Technische Universität Berlin. The opinions expressed in this article are FOR's own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. Open Access funding enabled and organized by Projekt DEAL.
Publisher Copyright:
© 2023 The Authors. Published under the terms of the CC BY 4.0 license.
PY - 2023/4/12
Y1 - 2023/4/12
N2 - 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.
AB - 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.
KW - alphaFold-multimer
KW - crosslinking mass spectrometry
KW - protein–protein interactions
KW - pyruvate dehydrogenase
KW - uncharacterized proteins
U2 - 10.15252/msb.202311544
DO - 10.15252/msb.202311544
M3 - Article
C2 - 36815589
AN - SCOPUS:85148580144
SN - 1744-4292
VL - 19
SP - e11544
JO - Molecular Systems Biology
JF - Molecular Systems Biology
IS - 4
M1 - e11544
ER -