Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech College of Engineering and Emory School of Medicine, Atlanta, GA 30332, USA.
Parker H. Petit Institute of Bioengineering and Bioscience, Atlanta, GA 30332, USA.
Cell Rep Methods. 2022 Dec 30;3(1):100372. doi: 10.1016/j.crmeth.2022.100372. eCollection 2023 Jan 23.
The development of protease-activatable drugs and diagnostics requires identifying substrates specific to individual proteases. However, this process becomes increasingly difficult as the number of target proteases increases because most substrates are promiscuously cleaved by multiple proteases. We introduce a method-substrate libraries for compressed sensing of enzymes (SLICE)-for selecting libraries of promiscuous substrates that classify protease mixtures (1) without deconvolution of compressed signals and (2) without highly specific substrates. SLICE ranks substrate libraries using a compression score (), which quantifies substrate orthogonality and protease coverage. This metric is predictive of classification accuracy across 140 (Pearson = 0.71) and 55 libraries ( = 0.55). Using SLICE, we select a two-substrate library to classify 28 samples containing 11 enzymes in plasma (area under the receiver operating characteristic curve [AUROC] = 0.93). We envision that SLICE will enable the selection of libraries that capture information from hundreds of enzymes using fewer substrates for applications like activity-based sensors for imaging and diagnostics.
蛋白酶激活药物和诊断试剂的开发需要鉴定针对特定蛋白酶的底物。然而，随着靶蛋白酶数量的增加，这一过程变得越来越困难，因为大多数底物都会被多种蛋白酶非特异地切割。我们引入了一种方法——用于酶的压缩感应的底物文库（SLICE）——用于选择具有分类蛋白酶混合物的广谱底物文库（1）无需对压缩信号进行反卷积，（2）无需高度特异性的底物。SLICE 使用压缩评分（）对底物文库进行排序，该评分量化了底物的正交性和蛋白酶覆盖率。该指标可预测 140 个文库（Pearson 相关系数 = 0.71）和 55 个文库（Spearman 秩相关系数 = 0.55）的分类准确性。使用 SLICE，我们选择了一个包含两种底物的文库，用于对含有 11 种酶的 28 个血浆样本进行分类（接受者操作特征曲线下面积 [AUROC] = 0.93）。我们设想 SLICE 将能够选择使用更少的底物从数百种酶中获取信息的文库，用于成像和诊断等基于活性的传感器等应用。
