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 a machine learning classifier to discriminate fast-degrading proteins from the rest of the proteome, achieving an area under the receiver operating characteristic curve (AUC) of 0.72.

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利用机器学习揭示大肠杆菌中的蛋白质降解

革兰氏阴性细菌中细胞内蛋白质的降解调节各种细胞过程，并通过消除受损的蛋白质充当质量控制机制。为了了解是什么导致细胞的蛋白水解机制降解某些蛋白质而保留其他蛋白质，我们采用了定量脉冲SILAC（细胞培养物中氨基酸的稳定同位素标记）方法，然后进行质谱分析，以确定半衰期。指数增长的大肠杆菌蛋白质组，在标准条件下。我们开发了一种基于可能性的统计测试，以发现活跃降解的蛋白质并鉴定出数十种快速降解的新型蛋白质。最后，我们使用结构，物理化学和蛋白质-蛋白质相互作用网络描述符来训练机器学习分类器，以区分蛋白质组其余部分中快速降解的蛋白质，从而在接收器工作特征曲线（AUC）下获得0.72的面积。