Autonomous Platform for Biocatalyst Performance Screening and Optimization
- Leon Hennecke, M.Sc., Institut für Technische Biokatalyse -
The focus of this project is on the acute need for active and stable biocatalysts under real process conditions. Today, it is no longer a challenge to generate large libraries of enzyme variants and analyzing them for activity, selectivity and stability in high-throughput screening methods1. However, the selected enzyme variants enzyme variants often do not meet the desired requirements under industrial process conditions. This is where this research will bridge the gap. The product is a microstructured system for the rapid, autonomous determination of the performance data of biocatalysts under process conditions by means of inline analytical methods. In this project, recent concepts from chemical research2,3 are used for the autonomous determination and optimization of process parameters for industrial biocatalysis. As a sensitive inline analytical method, mid-infrared (MIR) spectroscopy could serve as a sensitive inline analytical method4, which allows a biocatalyst characterization with real substrates under process conditions. Due to the intended continuous mode of operation all biocatalyst-specific technical process parameters (kinetics and stability) can be determined in the autonomously operated screening platform. This will significantly reduce the development times of new biobased products for the bioeconomy.
The state of the art is that the screening of large variant libraries is generally not carried out with substrates, but rather with model substances. Furthermore, the screening of biotransformations in organic media is usually hardly possible. All activity and selectivity determination methods are usually based on initial reaction rate measurements, which do not consider inhibition phenomena and/or thermodynamics. It is particularly important to emphasize that the determination of the necessary biocatalyst parameters, such as the kinetics, thermodynamics, and process stability in the real process medium with all reactants and accompanying substances is currently not possible or very costly5. As a consequence, no real process-relevant parameters can be determined.
The aim of the project is to design a robust microreactor system in order to achieve a sufficient heat transfer, sterilizability and reusability. The substrate solutions are homogenized with a static mixing unit and pumped into a continuously operated bioreactor (see Fig. 1).
As a continuously operated bioreactor unit, different systems (packed bed reactor, enzyme membrane reactor) will be compared. By a compact design of the reactor a defined reaction temperature and/or a temperature gradient within the reactor is achieved. An inline analytical unit will be integrated at the outlet of the bioreactor (project objective 1). The reaction spectra will be automatically transmitted to a self-learning software and chemometrically evaluated (project objective 2). Through the coupling of autonomous variation of flow velocities and temperature with a sensitive molecular analysis, the characteristic kinetic parameters can be determined (see Fig. 1). These include the maximum reaction velocity (vmax), the initial reaction velocity (v0), the Michaelis-Menten (Km) and inhibition constants (Ki), the equilibrium constant, and the stability of the respective biocatalysts expressed in half-life (τ1/2). The second goal of the project is the development of a software structure, which allows a low-calibration evaluation of the spectral data for kinetic model determination. Hereby an algorithm will be selected resp. programmed and be trained with the spectral data for pattern recognition (machine learning). From these, kinetic and chemometric models are generated, which will be used to evaluate a broad spectrum of enzymes and reactions. This structure grows with the progress of the project and improves the algorithm through continuous addition of new data.
The authors acknowledge the Federal Ministry of Education and Research of Germany (BMBF) for the financial support (FKZ 031B1014).
1Dörr M, Fibinger MPC, Last D, Schmidt S, Santos-Aberturas J, Böttcher D, et al. Fully automatized high-throughput enzyme library screening using a robotic platform. Biotechnol Bioeng. 2016;113(7):1421–32.
2 Short M, Schenk C, Thierry D, Rodriguez JS, Biegler LT, Garcia-Muñoz S. KIPET – An Open-Source Kinetic Parameter Estimation Toolkit [Internet]. Vol. 47, Computer Aided Chemical Engineering. Elsevier Masson SAS; 2019. 299–304 p. Available from: https://doi.org/10.1016/B978-0-12-818597-1.50047-3
3 Waldron C, Pankajakshan A, Quaglio M, Cao E, Galvanin F, Gavriilidis A. An autonomous microreactor platform for the rapid identification of kinetic models. React Chem Eng. 2019;4(9):1623–36.
4 Hiessl R, Hennecke L, Plass C, Kleber J, Wahlefeld S, Otter R, et al. FTIR based kinetic characterisation of an acid-catalysed esterification of 3-methylphthalic anhydride and 2-ethylhexanol. Anal Methods. 2020;12(24):3137–44.
5 Žnidaršič-Plazl P. The Promises and the Challenges of Biotransformations in Microflow. Biotechnol J. 2019;14(8).