Deconvolving multiplexed protease signatures with substrate reduction and activity clustering

• Type of Material: Article
• Authors: Qinwei Zhuang, Georgia Institute of Technology; Brandon Alexander Holt, Emory University; Gabe Kwong, Emory University; Peng Qiu, Emory University
• Language: English
• Date: 2019-09-01
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2019 Zhuang et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pcbi.1006909
• Title of Journal or Parent Work: PLoS Computational Biology
• ISSN: 1553-734X
• Volume: 15
• Issue: 9
• Start Page: e1006909
• End Page: e1006909
• Grant/Funding Information: P.Q. is an ISAC Marylou Ingram Scholar and a Carol Ann and David D. Flanagan Faculty Fellow.; This work was partially supported by funding from the National Science Foundation (CCF1552784).; This work was performed in part at the Georgia Tech Institute for Electronics and Nanotechnology, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the National Science Foundation (Grant ECCS-1542174).; This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1650044 (B.A.H.).; B.A.H is supported by the NSF GRFP; National Institutes of Health GT BioMAT Training Grant under Award Number 5T32EB006343; and the Georgia Tech President's Fellowship.; G.A.K. holds a Career Award at the Scientific Interface from the Burroughs Welcome Fund.; This work was funded by an NIH Director’s New Innovator Award (Award No. DP2HD091793).
• Research Categories: Engineering, Biomedical

Abstract

Proteases are multifunctional, promiscuous enzymes that degrade proteins as well as peptides and drive important processes in health and disease. Current technology has enabled the construction of libraries of peptide substrates that detect protease activity, which provides valuable biological information. An ideal library would be orthogonal, such that each protease only hydrolyzes one unique substrate, however this is impractical due to off-target promiscuity (i.e., one protease targets multiple different substrates). Therefore, when a library of probes is exposed to a cocktail of proteases, each protease activates multiple probes, producing a convoluted signature. Computational methods for parsing these signatures to estimate individual protease activities primarily use an extensive collection of all possible protease-substrate combinations, which require impractical amounts of training data when expanding to search for more candidate substrates. Here we provide a computational method for estimating protease activities efficiently by reducing the number of substrates and clustering proteases with similar cleavage activities into families. We envision that this method will be used to extract meaningful diagnostic information from biological samples.