Protein interactions in human pathogens revealed through deep learning

Ian R Humphreys; Jing Zhang; Minkyung Baek; Yaxi Wang; Aditya Krishnakumar; Jimin Pei; Ivan Anishchenko; Catherine A Tower; Blake A Jackson; Thulasi Warrier; Deborah T Hung; S Brook Peterson; Joseph D Mougous; Qian Cong; David Baker

doi:10.1038/s41564-024-01791-x

Protein interactions in human pathogens revealed through deep learning

Nat Microbiol. 2024 Sep 18. doi: 10.1038/s41564-024-01791-x. Online ahead of print.

Authors

Ian R Humphreys^#^{1

2}, Jing Zhang^#^{3

4

5}, Minkyung Baek^#⁶, Yaxi Wang^#⁷, Aditya Krishnakumar^{1

2}, Jimin Pei^{3

4

5}, Ivan Anishchenko^{1

2}, Catherine A Tower⁷, Blake A Jackson⁷, Thulasi Warrier^{8

9

10}, Deborah T Hung^{8

9

10}, S Brook Peterson⁷, Joseph D Mougous^{7

11

12}, Qian Cong^{13

14

15}, David Baker^{16

17

18}

Affiliations

¹ Department of Biochemistry, University of Washington, Seattle, WA, USA.
² Institute for Protein Design, University of Washington, Seattle, WA, USA.
³ Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA.
⁴ Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
⁵ Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
⁶ Department of Biological Sciences, Seoul National University, Seoul, South Korea. minkbaek@snu.ac.kr.
⁷ Department of Microbiology, University of Washington, Seattle, WA, USA.
⁸ Department of Molecular Biology and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA.
⁹ Department of Genetics, Harvard Medical School, Boston, MA, USA.
¹⁰ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹¹ Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
¹² Microbial Interactions and Microbiome Center, University of Washington, Seattle, WA, USA.
¹³ Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA. qian.cong@utsouthwestern.edu.
¹⁴ Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA. qian.cong@utsouthwestern.edu.
¹⁵ Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA. qian.cong@utsouthwestern.edu.
¹⁶ Department of Biochemistry, University of Washington, Seattle, WA, USA. dabaker@uw.edu.
¹⁷ Institute for Protein Design, University of Washington, Seattle, WA, USA. dabaker@uw.edu.
¹⁸ Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA. dabaker@uw.edu.

^# Contributed equally.

PMID: 39294458
DOI: 10.1038/s41564-024-01791-x

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.