Journal Article

FZJ-2021-06087

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions

Weiel, M. ; Götz, M. ; Klein, A. ; Coquelin, D. ; Floca, R. ; Schug, A. (Corresponding author)FZJ*

2021
Springer Nature Publishing [London]

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Please use a persistent id in citations: http://hdl.handle.net/2128/29627  doi:10.1038/s42256-021-00366-3

Abstract: 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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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. Forschergruppe Schug (hkf6_20200501) (hkf6_20200501)

Appears in the scientific report 2021

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Database coverage:
; ; ; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; DEAL Nature ; Essential Science Indicators ; IF >= 20 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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