Embracing enzyme promiscuity with activity-based compressed biosensing

• Type of Material: Article
• Authors: Brandon A Holt, Emory University; Hong Seo Lim, Emory University; Anirudh Sivakumar, Emory University; Hathaichanok Phuengkham, Emory University; Melanie Su, Emory University; McKenzie Tuttle, Emory University; Yilin Xu, Emory University; Haley Liakakos, Emory University; Peng Qiu, Emory University; Gabe Kwong, Emory University
• Language: English
• Date: 2023-01-23
• Publisher: Elsevier
• Publication Version: Final Published Version
• Copyright Statement: © 2022 The Authors
• Final Published Version (URL): http://dx.doi.org/10.1016/j.crmeth.2022.100372
• Title of Journal or Parent Work: Cell Reports Methods
• Volume: 3
• Issue: 1
• Start Page: 100372
• End Page: 100372
• Keywords: protease; compressed sensing; synthetic biomarker; substrate selection; activity-based sensor; protease-activatable drugs
• Research Categories: Engineering, Biomedical

Abstract

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 (C), which quantifies substrate orthogonality and protease coverage. This metric is predictive of classification accuracy across 140 in silico (Pearson r = 0.71) and 55 in vitro libraries (r = 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.