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Harnessing machine learning to unravel protein degradation in Escherichia coli

ABSTRACT: Degradation of intracellular proteins in Gram-negative bacteria regulates various cellular processes and serves as a quality control mechanism by eliminating damaged proteins. To understand what causes the proteolytic machinery of the cell to degrade some proteins while sparing others, we employed a quantitative pulsed-SILAC (Stable Isotope Labeling with Amino acids in Cell culture) method followed by mass spectrometry analysis to determine the half-lives for the proteome of exponentially growing Escherichia coli, under standard conditions. We developed a likelihood-based statistical test to findactively degraded proteins, and identified dozens of novel proteins that are fast-degrading. Finally, we used structural, physicochemical and protein-protein interaction network descriptorsto train a machine-learning classifier to discriminate fast-degrading proteins from the rest of the proteome. Our combined computational-experimental approach provides means for proteomic-based discovery of fast degrading proteins in bacteria and the elucidation of the factors determining protein half-livesand have implications for protein engineering. Moreover, as rapidly degraded proteins may play an important role in pathogenesis, our findings could identify new potential antibacterial drug targets

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Escherichia Coli

SUBMITTER: Meital Kupervaser

LAB HEAD: Tal Pupko

PROVIDER: PXD022112 | Pride | 2021-01-13

REPOSITORIES: Pride

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			Action	DRS
	HF2_8742_TPU_T0_1_310119.raw	Raw
	HF2_8742_TPU_T2_1_310119.raw	Raw
	HF2_8742_TPU_T4_1_310119.raw	Raw
	HF2_8742_TPU_T6_1_310119.raw	Raw
	HF2_8742_TPU_T7_1_310119.raw	Raw

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