Improved in situ characterization of protein complex dynamics at scale with thermal proximity co-aggregation

Siyuan Sun, Zhenxiang Zheng, Jun Wang, Fengming Li, An He, Kunjia Lai, Shuang Zhang, Jia-Hong Lu, Ruijun Tian and Chris Soon Heng Tan ()
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Siyuan Sun: Southern University of Science and Technology
Zhenxiang Zheng: Southern University of Science and Technology
Jun Wang: Southern University of Science and Technology
Fengming Li: Southern University of Science and Technology
An He: Southern University of Science and Technology
Kunjia Lai: Southern University of Science and Technology
Shuang Zhang: Southern University of Science and Technology
Jia-Hong Lu: University of Macau
Ruijun Tian: Southern University of Science and Technology
Chris Soon Heng Tan: Southern University of Science and Technology

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract Cellular activities are carried out vastly by protein complexes but large repertoire of protein complexes remains functionally uncharacterized which necessitate new strategies to delineate their roles in various cellular processes and diseases. Thermal proximity co-aggregation (TPCA) is readily deployable to characterize protein complex dynamics in situ and at scale. We develop a version termed Slim-TPCA that uses fewer temperatures increasing throughputs by over 3X, with new scoring metrics and statistical evaluation that result in minimal compromise in coverage and detect more relevant complexes. Less samples are needed, batch effects are minimized while statistical evaluation cost is reduced by two orders of magnitude. We applied Slim-TPCA to profile K562 cells under different duration of glucose deprivation. More protein complexes are found dissociated, in accordance with the expected downregulation of most cellular activities, that include 55S ribosome and respiratory complexes in mitochondria revealing the utility of TPCA to study protein complexes in organelles. Protein complexes in protein transport and degradation are found increasingly assembled unveiling their involvement in metabolic reprogramming during glucose deprivation. In summary, Slim-TPCA is an efficient strategy for characterization of protein complexes at scale across cellular conditions, and is available as Python package at https://pypi.org/project/Slim-TPCA/ .

Date: 2023
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DOI: 10.1038/s41467-023-43526-2

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