Protease circuits for processing biological information

• Type of Material: Article
• Authors: Brandon Alexander Holt, Georgia Tech; Gabe Kwong, Emory University
• Language: English
• Date: 2020-10-06
• Publisher: NATURE RESEARCH
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2020
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41467-020-18840-8
• Title of Journal or Parent Work: NATURE COMMUNICATIONS
• Volume: 11
• Issue: 1
• Start Page: 5021
• End Page: 5021
• Grant/Funding Information: This work was funded by an NIH Director’s New Innovator Award (Award No. DP2HD091793) as well as an R01 from the NCI (GR10003709). 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. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1650044 (B.A.H.). G.A.K. holds a Career Award at the Scientific Interface from the Burroughs Welcome Fund.
• Keywords: Multidisciplinary Sciences; DYNAMICS; GENE-EXPRESSION; PRINCIPLES; Science & Technology; COMPUTATION; GATES; MODULATE; SPECIFICITY; NETWORK; OUTER-MEMBRANE PROTEASE; Science & Technology - Other Topics; SYNTHETIC ANALOG
• Research Categories: Computer Science; Biology, Biostatistics; Health Sciences, General

Abstract

Engineered biocircuits designed with biological components have the capacity to expand and augment living functions. Here we demonstrate that proteases can be integrated into digital or analog biocircuits to process biological information. We first construct peptide-caged liposomes that treat protease activity as two-valued (i.e., signal is 0 or 1) operations to construct the biological equivalent of Boolean logic gates, comparators and analog-to-digital converters. We use these modules to assemble a cell-free biocircuit that can combine with bacteria-containing blood, quantify bacteria burden, and then calculate and unlock a selective drug dose. By contrast, we treat protease activity as multi-valued (i.e., signal is between 0 and 1) by controlling the degree to which a pool of enzymes is shared between two target substrates. We perform operations on these analog values by manipulating substrate concentrations and combine these operations to solve the mathematical problem Learning Parity with Noise (LPN). These results show that protease activity can be used to process biological information by binary Boolean logic, or as multi-valued analog signals under conditions where substrate resources are shared.