Protein interactions in human pathogens revealed through deep learning.

Journal: Nature microbiology
PMID: 39294458

Abstract

Identification of bacterial protein-protein interactions and predicting the structures of these complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here we developed RoseTTAFold2-Lite, a rapid deep learning model that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1,923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer-membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.

Authors

  • Ian R Humphreys
    Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, Washington, USA.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Minkyung Baek
    Department of Biochemistry and Institute for Protein Design, University of Washington, Washington, WA, USA.
  • Yaxi Wang
  • Aditya Krishnakumar
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Jimin Pei
    Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Ivan Anishchenko
    Computational Biology Program, The University of Kansas, Lawrence, Kansas.
  • Catherine A Tower
    Department of Microbiology, University of Washington, Seattle, WA, USA.
  • Blake A Jackson
    Department of Microbiology, University of Washington, Seattle, WA, USA.
  • Thulasi Warrier
    Department of Molecular Biology and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA.
  • Deborah T Hung
    Department of Molecular Biology and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA.
  • S Brook Peterson
    Department of Microbiology, University of Washington, Seattle, WA, USA.
  • Joseph D Mougous
    Department of Microbiology, University of Washington, Seattle, WA, USA.
  • Qian Cong
    Department of Biochemistry, Seattle, WA 98105, USA.
  • David Baker
    Department of Biochemistry, University of Washington, Seattle, Washington.

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