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Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling
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Protein complexes in cells by AI-assisted structural proteomics.

Francis J O'Reilly1, Andrea Graziadei1, Christian Forbrig1

  • 1Chair of Bioanalytics, Technische Universität Berlin, Berlin, Germany.

Molecular Systems Biology
|February 23, 2023
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Summary
This summary is machine-generated.

This study reveals novel protein interactions within intact bacterial cells using advanced mass spectrometry and AI-driven structure prediction. The findings offer new insights into cellular machinery and protein complex structures.

Keywords:
AlphaFold-Multimercrosslinking mass spectrometryprotein-protein interactionspyruvate dehydrogenaseuncharacterized proteins

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Area of Science:

  • Molecular Biology
  • Structural Biology
  • Biochemistry

Background:

  • Artificial intelligence is revolutionizing molecular biology by enabling accurate modeling of protein structures and complexes.
  • Experimental data can guide systematic modeling of novel protein assemblies.
  • Understanding protein-protein interactions is crucial for deciphering cellular functions.

Purpose of the Study:

  • To identify and structurally model protein-protein interactions in Bacillus subtilis using a combination of experimental and computational methods.
  • To investigate protein interactions within intact cells, overcoming limitations of traditional cell lysis methods.
  • To validate AI-driven structure predictions using experimental crosslinking mass spectrometry data.

Main Methods:

  • In-cell crosslinking mass spectrometry (XL-MS) to capture protein interactions before cell lysis.
  • Co-fractionation mass spectrometry (CoFrac-MS) for identifying protein complexes.
  • AlphaFold-Multimer for predicting the structures of protein-protein interactions.
  • Bioinformatic analysis to control for false-positive predictions and validate results.

Main Results:

  • XL-MS identified protein interactions often lost upon cell lysis.
  • AlphaFold-Multimer predicted structures for 153 dimeric and 14 trimeric protein assemblies.
  • Crosslinking MS data independently validated AlphaFold predictions and scoring.
  • Novel interactors for key cellular machineries (ribosome, RNA polymerase, pyruvate dehydrogenase) were identified and validated.

Conclusions:

  • The combined approach successfully uncovered protein-protein interactions within intact bacterial cells, providing structural insights.
  • The study validated the utility of AI in predicting protein complex structures and identified novel functional interactions.
  • This methodology is applicable to genetically intractable organisms, including pathogenic bacteria, for studying protein interactions.