Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria

Journal Article · 28 December 2022 · eLife

DOI: https://doi.org/10.7554/elife.82885 · OSTI ID:1907007

Gao, Mu  ^[1]; An, Davi Nakajima ^[1]; Skolnick, Jeffrey  ^[1]

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1. Georgia Institute of Technology, Atlanta, GA (United States)

To reach their final destinations, outer membrane proteins (OMPs) of gram-negative bacteria undertake an eventful journey beginning in the cytosol. Multiple molecular machines, chaperones, proteases, and other enzymes facilitate the translocation and assembly of OMPs. These helpers usually associate, often transiently, forming large protein assemblies. They are not well understood due to experimental challenges in capturing and characterizing protein-protein interactions (PPIs), especially transient ones. Using AF2Complex, we introduce a high-throughput, deep learning pipeline to identify PPIs within the Escherichia coli cell envelope and apply it to several proteins from an OMP biogenesis pathway. Among the top confident hits obtained from screening ~1500 envelope proteins, we find not only expected interactions but also unexpected ones with profound implications. Subsequently, we predict atomic structures for these protein complexes. These structures, typically of high confidence, explain experimental observations and lead to mechanistic hypotheses for how a chaperone assists a nascent, precursor OMP emerging from a translocon, how another chaperone prevents it from aggregating and docks to a β-barrel assembly port, and how a protease performs quality control. This work presents a general strategy for investigating biological pathways by using structural insights gained from deep learning-based predictions.

• Research Organization: Georgia Institute of Technology, Atlanta, GA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Institutes Health (NIH)
• Grant/Contract Number: SC0021303; NIH R35GM118039
• OSTI ID: 1907007
• Alternate ID(s): OSTI ID: 1907010; OSTI ID: 2421876
• Journal Information: eLife, Vol. 11; ISSN 2050-084X
• Publisher: eLife Sciences Publications, Ltd.Copyright Statement
• Country of Publication: United States
• Language: English