EnzymAgglo - Multiscale model-based investigation of enzyme clusters and agglomerates for cascade bioreactions
Nicolas Depta, M.Sc. ETH
In biotechnology and process engineering, the formation of stable enzyme clusters and agglomerates for bioreactions is of highest interest . Enzymatic biocatalysis enables efficient reaction pathways, which are essential for many processes in nature and technology. Applications in technology and occurrences in nature are various and include for example the processing of food in the human body, production of cheese and wine, as well as pharmaceutical products.
To date, the formation of catalytically active enzyme clusters and agglomerates, although experimentally observed , is hardly understood. Furthermore, the impact of parameters, such as pH, temperature, and mechanical stresses on these processes is scarcely understood as well. No model is available to predict the formation and stability of enzyme clusters and agglomerates, as well as their influence on overall catalytic performance. Understanding these processes, however, is crucial to allow for targeted modification, optimization, and possibly de novo creation of efficient bioreaction cascades.
To gain insight into the structural formation processes of multi-enzyme clusters and agglomerates, we develop a multiscale model framework termed by us the molecular discrete element method “MDEM” in combination with experimental validation. For this, multiple methods are combined, including molecular dynamics (MD) , multiple coarse-grained (CG) levels [4,5], the discrete element method (DEM) [6-8], and computational fluid dynamics (CFD), as it can be seen in Figure 2. In order to couple MD with DEM, enzymes (generally complex nanoscopic structures) are abstracted as primary particles with an orientation and defined anisotropic properties. These are subjected to treatment by appropriate models: A force-based diffusion model was developed  to enforce the correct thermodynamic properties (constant temperature, i.e. canonical ensemble) and anisotropic diffusion of objects in liquid systems. Furthermore, complex interaction models for short- and long-range interaction between individual enzymes are derived from MD (atomistic and CG).
Consequently, the framework is parameterized “bottom-up” and validated “top-down” by comparison with experimental data, which is obtained from biolayer interferometry, dynamic light scattering, and activity assays. As a model enzyme, the Pyruvate Dehydrogenase Complex (PDC) is used, which possesses a highly regulated multi-enzymatic machinery and many processes of interest.
Figure 2. Multiscale modeling approach for enzyme interaction and agglomeration.
First results for the PDC component E2 (with linker arm; see Figure 3) show that the continuous formation and breakup of enzymatic agglomerates can be predicted using a newly developed DEM diffusion model  in combination with interaction forces derived from MD without any experimental data fitting. This approach yields accurate scale-bridging diffusion kinetics and agglomerate sizes matching corresponding dynamic light scattering data from experimental investigations. The developed framework and its models can be easily adapted to investigate other phenomena such as interface absorption and are therefore widely applicable.
Figure 3. PDC agglomeration of E2 (with linker arm) system with equal mass contributions of monomers, trimers, 60-mers (each 0.33 mg/ml) (preliminary results). The structural formation starts from a randomly initialized system (left) and equilibrates (number of interactions investigated) in approximately 0.1 ms (right). Agglomerate structures with a rH ~ 50-70 nm form, which agrees with experimental results from DLS (75.2 nm ± 10.4%). A continuous formation and breakage of agglomerates is observed. System size 1 µm3; periodic boundary conditions; color indicates number of monomers in agglomerate (blue 1; red 400).
 Castellana M., ... & Wingreen N. S. (2014). Nature Biotechnology, 32(10), 1011-1018.
 Guo J., Hezaveh S., Tatur J., Zeng A. P., Jandt U. (2017). Biochemical Journal, 474(5), 865-875.
 Hezaveh S., Zeng A.P., Jandt U. (2016). J. Phys. Chem. B, 120(19), 4399-4409
 Hezaveh S., Zeng A.P., Jandt U. (2017). ACS Omega, 2(3), 1134-1145.
 Hezaveh S., Zeng A.P., Jandt U. (2018). J. Chem. Inf. Model, 58 (2), 362-369.
 Dosta M., Antonyuk S., Heinrich S. (2013). Industrial Engineering Chemistry Research, 52(33), 11275-11281.
 Spettl A., Dosta M., Antonyuk S., Heinrich S., Schmidt V. (2015). Adv. Powder Techn., 26(3), 1021-1030.
 Schnegas S., Antonyuk S., Heinrich S. (2015). Powder Techn., 237, 529-536.
 Depta P.N., Jandt U., Dosta M., Zeng A.-P., Heinrich S. (2019). J. Chem. Inf. Model, 59 (1), 386-398.
P.N. Depta, M. Dosta, S. Heinrich - Institute of Solids Process Engineering and Particle Technology
S. Ilhan, U. Jandt, A.P. Zeng - Institute of Bioprocess and Biosystems Engineering. Link
Project funding and Start Date
Within the DFG priority program SPP 1934 - DiSPBiotech -
- +49 40 42878 2765