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Weiel, M.* ; Götz, M.* ; Klein, A.* ; Coquelin, D.* ; Floca, R.O.* ; Schug, A.*

Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions.

Nat. Mach. Intell., DOI: 10.1038/s42256-021-00366-3 (2021)
DOI
Creative Commons Lizenzvertrag
Open Access Green as soon as Postprint is submitted to ZB.
Molecular simulations are a powerful tool to complement and interpret ambiguous experimental data on biomolecules to obtain structural models. Such data-assisted simulations often rely on parameters, the choice of which is highly non-trivial and crucial to performance. The key challenge is weighting experimental information with respect to the underlying physical model. We introduce FLAPS, a self-adapting variant of dynamic particle swarm optimization, to overcome this parameter selection problem. FLAPS is suited for the optimization of composite objective functions that depend on both the optimization parameters and additional, a priori unknown weighting parameters, which substantially influence the search-space topology. These weighting parameters are learned at runtime, yielding a dynamically evolving and iteratively refined search-space topology. As a practical example, we show how FLAPS can be used to find functional parameters for small-angle X-ray scattering-guided protein simulations.
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Publication type Article: Journal article
Document type Scientific Article
Keywords X-ray-scattering; Adenylate Kinase; Multiobjective Optimization; Protein-structure; Stability; Complex; Binding; States
ISSN (print) / ISBN 2522-5839
e-ISSN 2522-5839
Journal Nature machine intelligence
Publisher Springer
Publishing Place [London]
Reviewing status Peer reviewed
Institute(s) Helmholtz AI - KIT (HAI - KIT)
Grants DFG Research Training Group 2450
Helmholtz AI platform grant
Helmholtz Association's Initiative and Networking Funds
Federal Ministry of Education and Research
Ministry of Science, Research and Arts Baden-Wurttemberg