Improvedin situCharacterization of Proteome-wide Protein Complex Dynamics with Thermal Proximity Co-Aggregation

Abstract

Vast majority of cellular activities are carried out by protein complexes that assembled dynamically in response to cellular needs and environmental cues. Disarrayed cellular activities could arise from dysregulated protein complexes leading to diseases and developmental defects. Large scale efforts had uncovered a large repertoire of functionally uncharacterized protein complexes which necessitate new strategies to delineate their roles in various cellular activities and diseases. Thermal proximity co-aggregation profiling could be readily deployed to simultaneously characterize the dynamics for hundreds to thousands of protein complexes in situ across different cellular conditions. Toward this goal, we had optimized the original method both experimentally and computationally. In this new iteration termed Slim-TPCA, fewer temperatures are used increases throughputs by over 3X, while coupled with new scoring metrics and statistical evaluation resulted in minimal compromise in coverage and the detection of more relevant protein complexes. Overall, less samples are needed, false positives from batch effects are minimized and statistical evaluation time is reduced by two orders of magnitude. We applied Slim-TPCA to profile state of protein complexes in K562 cells under different duration of glucose deprivation. More protein complexes are found dissociated based on TPCA signature in accordance with expected downregulation of most cellular activities. These complexes include 55S ribosome and various respiratory complexes in mitochondria revealing the utility of TPCA to study protein complexes in organelles. On other hand, protein complexes involved in protein transport and degradation are found increasingly associated revealing their involvement in metabolic reprogramming during glucose deprivation. In summary. Slim-TPCA is an efficient strategy for proteome-wide characterization of protein complexes. The various algorithmic improvement of Slim-TPCA is available as Python package at https://pypi.org/project/Slim-TPCA/