Data-Driven Multiscale Modeling of Self-Assembly and Hierarchical Structural Formation in Biological Macro-Molecular Systems: Pyruvate Dehydrogenase Complex

P. N. Depta (), Maksym Dosta () and S. Heinrich ()
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P. N. Depta: Hamburg University of Technology, Institute of Solids Process Engineering and Particle Technology
Maksym Dosta: Hamburg University of Technology, Institute of Solids Process Engineering and Particle Technology
S. Heinrich: Hamburg University of Technology, Institute of Solids Process Engineering and Particle Technology

A chapter in High Performance Computing in Science and Engineering '22, 2024, pp 355-370 from Springer

Abstract: Abstract Macro-molecular self-assembly and hierarchical structural formation are crucial for a variety of systems in nature and technology. Especially biological systems often rely on a specific structural organization to enable their function. Examples are multi-enzyme complexes enabling catalytic activity through structure-based phenomena such as metabolic channeling or the self-assembly of virus capsids necessary for transport of the genetic material and overall infection process. This project attempts to improve understanding and modeling capabilities of such systems by developing a multiscale modeling methodology for self-assembly on the scales of micro-meters and milli-second including a data-driven parameterization approach. As model systems the hepatitis B core antigen (HBcAg) and pyruvate dehydrogenase complex (PDC) are used, which feature a macro-molecular self-assembly crucial in enabling their function.

Date: 2024
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DOI: 10.1007/978-3-031-46870-4_23

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