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 these processes, we develop a multiscale model framework in combination with experimental validation to describe the dynamic formation and stability of multi-enzyme complex clusters and agglomerates, while accounting for various process parameters. For this, multiple methods are combined, including molecular dynamics (MD) , multiple coarse-grained (CG) levels , the discrete element method (DEM) [5-7], and computational fluid dynamics (CFD), as it can be seen in Fig. 2. Consequently, scales from atomistic, where single enzyme clusters are modeled over microseconds, are linked to the micrometer scale for modeling agglomerate systems over milliseconds. The models are parameterized “bottom-up” and validated “top-down” by comparison with experimental data, which is obtained from biolayer interferometry, dynamic light scattering (DLS), 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.
Specifically, the discrete element method (DEM) is used in the framework of the in-house code MUSEN [5, 6] to model the enzyme agglomeration and large-scale interaction. MUSEN possesses an extendable multiscale and multi-physics framework, as well as CPU/GPU support, and is continuously enhanced. Novel models are developed to bridge the gap between MD and DEM in order to enable a multiscale investigation of biological and other processes on the nanometer to micrometer scale.
First results for single PDC components show that the dynamic formation and breakup of catalytic agglomerates can be predicted using a newly developed DEM diffusion model in combination with interaction forces derived from MD without any experimental data fitting – yielding accurate scale-bridging diffusion kinetics and agglomerate sizes matching corresponding dynamic light scattering data from experimental investigations. A visual representation of the agglomeration process starting from a random distribution of individual PDC components to an agglomerized state can be found in Fig. 3. The newly developed models and methodology can be easily adapted to investigate other phenomena such as interface absorption and are therefore widely applicable.
 Castellana M., Wilson M.Z., Xu Y., Joshi P., ... & 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.
 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.
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