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 find actively degraded proteins and identified dozens of fast-degrading novel proteins. Finally, we used structural, physicochemical, and protein-protein interaction network descriptors to train

SUBMITTER: Nagar N 

PROVIDER: S-EPMC7857536 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature