Improvement of α-amino Ester Hydrolase Stability via Computational Protein Design.

Amino ester hydrolases (AEHs) are capable of rapid synthesis of cephalexin but suffer from rapid deactivation even at low temperatures. Previous efforts to engineer AEH have generated several improved variants but have been limited in scope in part due to limitations in activity assay throughput for ß-lactam synthesis reactions. Rational design of 'whole variants' was explored to rapidly improve AEH thermostability by mutating between 3-15% of residues. Most variants were found to be inactive due to a mutated calcium binding site, the function of which has not previously been described. Four active variants, all with improved melting temperatures, were characterized in terms of synthesis and hydrolysis activity, melting temperature, and deactivation at 25°C. Two variants were found to have improved total turnover numbers relative to the initial AEH variant; however, a clear tradeoff exists between improved stability and overall activity of each variant.

EnzymeML: seamless data flow and modeling of enzymatic data.

The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .

Reactor Design and Optimization of α-Amino Ester Hydrolase- Catalyzed Synthesis of Cephalexin.

Pharmaceutical production quality has recently been a focus for improvement through incorporation of end-to-end continuous processing. Enzymatic ß-lactam antibiotic synthesis has been one focus for continuous manufacturing, and α-amino ester hydrolases (AEHs) are currently being explored for use in the synthesis of cephalexin due to their high reactivity and selectivity. In this study, several reactors were simulated to determine how reactor type and configuration impacts reactant conversion, fractional yield toward cephalexin, and volumetric productivity for AEH-catalyzed cephalexin synthesis. The primary reactor configurations studied are single reactors including a continuous stirred-tank reactor (CSTR) and plug flow reactor (PFR) as well as two CSTRS and a CSTR + PFR in series. Substrate concentrations fed to the reactors as well as enzyme concentration in the reactor were varied. The presence of substrate inhibition was found to have a negative impact on all reactor configurations studied. No reactor configuration simultaneously allowed high substrate conversion, high fractional yield, and high productivity; however, a single PFR was found to enable the highest substrate conversion with higher fractional yields than all other reactor configurations, by minimizing substrate inhibition. Finally, to further demonstrate the impact of substrate inhibition, an AEH engineered to improve substrate inhibition was simulated and Pareto optimal fronts for a CSTR catalyzed with the current AEH were compared to Pareto fronts for the improved AEH. Overall, reduced substrate inhibition would allow for high substrate conversion, fractional yield, and productivity with only a single CSTR.

Ultrasonically-guided flow focusing generates precise emulsion droplets for high-throughput single cell analyses.

Emulsion-based techniques have dramatically advanced our understanding of single-cell biology and complex single-cell features over the past two decades. Most approaches for precise single cell isolation rely on microfluidics, which has proven highly effective but requires substantial investment in equipment and expertise that can be difficult to access for researchers that specialize in other areas of bioengineering and molecular biotechnology. Inspired by the robust droplet generation technologies in modern flow cytometry instrumentation, here we established a new platform for high-throughput isolation of single cells within droplets of tunable sizes by combining flow focusing with ultrasonic vibration for rapid and effective droplet formation. Application of ultrasonic pressure waves to the flowing jet provided enhanced control of emulsion droplet size, permitting capture of 25,000 to 50,000 single cells per minute. As an example application, we applied this new droplet generation platform to sequence the antibody variable region heavy and light chain pairings (VH:VL) from large repertoires of single B cells. We demonstrated the recovery of > 40,000 paired CDRH3:CDRL3 antibody clusters from a single individual, validating that these droplet systems can enable the genetic analysis of very large single-cell populations. These accessible new technologies will allow rapid, large-scale, and precise single-cell analyses for a broad range of bioengineering and molecular biotechnology applications.