Data-driven multiscale modeling of self-assembly and hierarchical structural formation in biological macro-molecular systems

P. N. Depta (), M. Dosta () and S. Heinrich ()
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P. N. Depta: Hamburg University of Technology, Institute of Solids Process Engineering and Particle Technology
M. 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 '21, 2023, pp 513-529 from Springer

Abstract: Abstract Self-assembly and hierarchical structural formation are essential in many systems of both nature and technology. Examples are the dynamic self-assembly of multi-protein clusters and the interdependency with catalytic activity, as well as the self-assembly of virus capsids critical for virus function. In an attempt to better understand and model these systems, we develop a multiscale modeling methodology to capture macro-molecular self-assembly on the micro-meter and milli-second scale. Focus of this report is the derivation and implementation of data-driven interaction potentials based on molecular dynamics simulations using Universal Kriging on the example of the hepatitis B core antigen (HBcAg).

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-17937-2_32

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DOI: 10.1007/978-3-031-17937-2_32

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