Biology subjects

Kwong, G. A.

Publications and source records attributed to Kwong, G. A..

3 recordsLinked to original sources

bioRxiv2019

Proteases as Biological Bits for Programmable Medicine

Engineered biocircuits that interface with living systems as plug-and-play constructs may enable new applications for programmable therapies and diagnostics. We create biological bits (bbits) using proteases - a family of pleiotropic, promiscuous enzymes - to construct the biological equivalent of Boolean logic gates, comparators and analog-to-digital converters. We use these modules to write a cell-free bioprogram that can combine with bacteria-infected blood, quantify infection burden, and then calculate and unlock a selective drug dose. Inspired by probabilistic computing, we leverage multi- and common-target protease promiscuity as the biological analog of superposition to program three probabilistic bbits that solve all implementations of the two-bit oracle problem, Learning Parity with Noise. Treating a network of dysregulated proteases in a living animal as an oracle, we use this algorithm to resolve the probability distribution of coagulation proteases in vivo, allowing diagnosis of pulmonary embolism with high sensitivity and specificity (AUROC = 0.92) in a mouse model of thrombosis. Our results demonstrate that protease activity can be programmed in cell-free systems to carry out classical and probabilistic algorithms for programmable medicine.

bioengineering↗

bioRxiv2019

Deconvolving multiplexed protease signatures with substrate reduction and activity clustering

Proteases are pleiotropic, promiscuous enzymes that degrade proteins and peptides, which drive important processes in health and disease. The ability to quantify the activity of protease signatures by sampling with Massively Multiplexed Activity (MMA) libraries will provide unparalleled biological information. Under such a framework, a designed library of peptide substrates is exposed to a cocktail of proteases, the cleavage velocity of each substrate is measured, and individual protease activity levels are inferred from the data. Previous studies have developed individual protease sensors, but multiplexed substrate cleavage data becomes difficult to interpret as the number of cross-cutting proteases increases. Computational methods for parsing this data to estimate individual protease activities primarily use an extensive compendium of all possible protease-substrate combinations, which require impractical amounts of training data when scaling up to MMA libraries. 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. This method is scalable and will enable the future use of MMA libraries with applications spanning therapeutic and diagnostic biotechnology.

bioengineering↗

bioRxiv2019

Bacterial defiance as a form of prodrug failure

Classifying the mechanisms of antibiotic failure has led to the development of new treatment strategies for killing bacteria. Among the currently described mechanisms, which include resistance, persistence and tolerance, we propose bacterial defiance as a form of antibiotic failure specific to prodrugs. As a prototypic model of a bacteria-activated prodrug, we construct cationic antimicrobial peptides (AMP), which are charge neutralized until activated by a bacterial protease. This construct successfully eliminated the vast majority of bacteria populations, while localizing activity to bacterial membranes and maintaining low active drug concentration. However, we observed defiant bacteria populations, which survive in the presence of identical drug concentration and exposure time. Using a multi-rate kinetic feedback model, we show that bacteria switch between susceptibility and defiance under clinically relevant environmental (e.g., hyperthermia) and genetic (e.g., downregulated protease expression) conditions. From this model, we derive a dimensionless quantity (Bacterial Advantage Heuristic, BAH) - representing the balance between bacterial proliferation and prodrug activation - that perfectly classifies bacteria as defiant or susceptible across a broad range of conditions. To apply this concept to other classes of prodrugs, we expand this model to include both linear and nonlinear terms and use general pharmacokinetic parameters (e.g., half-life, EC50, etc.). Taken together, this model reveals an analogous dimensionless quantity (General Advantage Key, GAK), which can applied to prodrugs with different activation mechanisms. We envision that these studies will enable the development of more effective prodrugs to combat antibiotic resistance.

bioengineering↗