001     1037179
005     20250203215420.0
024 7 _ |a 10.1021/acs.jctc.3c01333
|2 doi
024 7 _ |a 1549-9618
|2 ISSN
024 7 _ |a 1549-9626
|2 ISSN
024 7 _ |a 10.34734/FZJ-2025-00524
|2 datacite_doi
024 7 _ |a 38718349
|2 pmid
024 7 _ |a WOS:001225225200001
|2 WOS
037 _ _ |a FZJ-2025-00524
082 _ _ |a 610
100 1 _ |a Antila, Hanne S.
|0 0000-0002-2474-5053
|b 0
|e Corresponding author
245 _ _ |a Evaluating Polarizable Biomembrane Simulations against Experiments
260 _ _ |a Washington, DC
|c 2024
|b [Verlag nicht ermittelbar]
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1738571050_780
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Owing to the increase of available computational capabilities and the potential for providing a more accurate description, polarizable molecular dynamics force fields are gaining popularity in modeling biomolecular systems. It is, however, crucial to evaluate how much precision is truly gained with increasing cost and complexity of the simulation. Here, we leverage the NMRlipids open collaboration and Databank to assess the performance of available polarizable lipid models─the CHARMM-Drude and the AMOEBA-based parameters─against high-fidelity experimental data and compare them to the top-performing nonpolarizable models. While some improvement in the description of ion binding to membranes is observed in the most recent CHARMM-Drude parameters, and the conformational dynamics of AMOEBA-based parameters are excellent, the best nonpolarizable models tend to outperform their polarizable counterparts for each property we explored. The identified shortcomings range from inaccuracies in describing the conformational space of lipids to excessively slow conformational dynamics. Our results provide valuable insights for the further refinement of polarizable lipid force fields and for selecting the best simulation parameters for specific applications.
536 _ _ |a 5244 - Information Processing in Neuronal Networks (POF4-524)
|0 G:(DE-HGF)POF4-5244
|c POF4-524
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Dixit, Sneha
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Kav, Batuhan
|0 P:(DE-Juel1)178946
|b 2
|e Corresponding author
700 1 _ |a Madsen, Jesper J.
|0 0000-0003-1411-9080
|b 3
700 1 _ |a Miettinen, Markus S.
|0 0000-0002-3999-4722
|b 4
700 1 _ |a Ollila, O. H. Samuli
|0 0000-0002-8728-1006
|b 5
773 _ _ |a 10.1021/acs.jctc.3c01333
|g Vol. 20, no. 10, p. 4325 - 4337
|0 PERI:(DE-600)2166976-4
|n 10
|p 4325 - 4337
|t Journal of chemical theory and computation
|v 20
|y 2024
|x 1549-9618
856 4 _ |u https://juser.fz-juelich.de/record/1037179/files/antila-et-al-2024-evaluating-polarizable-biomembrane-simulations-against-experiments.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1037179
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)178946
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-524
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Molecular and Cellular Information Processing
|9 G:(DE-HGF)POF4-5244
|x 0
914 1 _ |y 2024
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-12
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2024-12-12
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b J CHEM THEORY COMPUT : 2022
|d 2024-12-12
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2024-12-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2024-12-12
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b J CHEM THEORY COMPUT : 2022
|d 2024-12-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2024-12-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2024-12-12
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IBI-7-20200312
|k IBI-7
|l Strukturbiochemie
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)IBI-7-20200312
980 _ _ |a UNRESTRICTED
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21