한빛사논문
- 조회848
Ian R. Humphreys 1,2,13, Jing Zhang 3,4,5,13, Minkyung Baek 6,13,*, Yaxi Wang 7,13, Aditya Krishnakumar 1,2, Jimin Pei 3,4,5, Ivan Anishchenko 1,2, Catherine A. Tower 7, Blake A. Jackson 7, Thulasi Warrier 8,9,10, Deborah T. Hung 8,9,10, S. Brook Peterson 7, Joseph D. Mougous 7,11,12, Qian Cong 3,4,5,* & David Baker 1,2,11,*
1Department of Biochemistry, University of Washington, Seattle, WA, USA.
2Institute for Protein Design, University of Washington, Seattle, WA, USA.
3Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA.
4Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
5Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
6Department of Biological Sciences, Seoul National University, Seoul, South Korea.
7Department of Microbiology, University of Washington, Seattle, WA, USA.
8Department of Molecular Biology and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA.
9Department of Genetics, Harvard Medical School, Boston, MA, USA.
10Broad Institute of MIT and Harvard, Cambridge, MA, USA.
11Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
12Microbial Interactions and Microbiome Center, University of Washington, Seattle, WA, USA.
13These authors contributed equally: Ian R. Humphreys, Jing Zhang, Minkyung Baek, Yaxi Wang.
*Corresponding authors: correspondence to Qian Cong, David Baker or Minkyung Baek
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.
논문정보
- Research article
- 2024년 09월 (BRIC 등록일 2024-09-20)
- 국내(교신)+국외 연구진
- 생명과학 > 구조생물 및 생물물리학
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