Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.4782
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dc.contributor.authorDepta, Philipp Nicolas-
dc.contributor.authorDosta, Maksym-
dc.contributor.authorWenzel, Wolfgang-
dc.contributor.authorKozlowska, Mariana-
dc.contributor.authorHeinrich, Stefan-
dc.date.accessioned2022-12-13T14:46:53Z-
dc.date.available2022-12-13T14:46:53Z-
dc.date.issued2022-11-24-
dc.identifierdoi: 10.3390/ijms232314699-
dc.identifier.citationInternational Journal of Molecular Sciences 23 (23): 14699 (2022)de_DE
dc.identifier.issn1422-0067de_DE
dc.identifier.urihttp://hdl.handle.net/11420/14348-
dc.description.abstractMacromolecular self-assembly is at the basis of many phenomena in material and life sciences that find diverse applications in technology. One example is the formation of virus-like particles (VLPs) that act as stable empty capsids used for drug delivery or vaccine fabrication. Similarly to the capsid of a virus, VLPs are protein assemblies, but their structural formation, stability, and properties are not fully understood, especially as a function of the protein modifications. In this work, we present a data-driven modeling approach for capturing macromolecular self-assembly on scales beyond traditional molecular dynamics (MD), while preserving the chemical specificity. Each macromolecule is abstracted as an anisotropic object and high-dimensional models are formulated to describe interactions between molecules and with the solvent. For this, data-driven protein–protein interaction potentials are derived using a Kriging-based strategy, built on high-throughput MD simulations. Semi-automatic supervised learning is employed in a high performance computing environment and the resulting specialized force-fields enable a significant speed-up to the micrometer and millisecond scale, while maintaining high intermolecular detail. The reported generic framework is applied for the first time to capture the formation of hepatitis B VLPs from the smallest building unit, i.e., the dimer of the core protein HBcAg. Assembly pathways and kinetics are analyzed and compared to the available experimental observations. We demonstrate that VLP self-assembly phenomena and dependencies are now possible to be simulated. The method developed can be used for the parameterization of other macromolecules, enabling a molecular understanding of processes impossible to be attained with other theoretical models.-
dc.description.abstractMacromolecular self-assembly is at the basis of many phenomena in material and life sciences that find diverse applications in technology. One example is the formation of virus-like particles (VLPs) that act as stable empty capsids used for drug delivery or vaccine fabrication. Similarly to the capsid of a virus, VLPs are protein assemblies, but their structural formation, stability, and properties are not fully understood, especially as a function of the protein modifications. In this work, we present a data-driven modeling approach for capturing macromolecular self-assembly on scales beyond traditional molecular dynamics (MD), while preserving the chemical specificity. Each macromolecule is abstracted as an anisotropic object and high-dimensional models are formulated to describe interactions between molecules and with the solvent. For this, data-driven protein–protein interaction potentials are derived using a Kriging-based strategy, built on high-throughput MD simulations. Semi-automatic supervised learning is employed in a high performance computing environment and the resulting specialized force-fields enable a significant speed-up to the micrometer and millisecond scale, while maintaining high intermolecular detail. The reported generic framework is applied for the first time to capture the formation of hepatitis B VLPs from the smallest building unit, i.e., the dimer of the core protein HBcAg. Assembly pathways and kinetics are analyzed and compared to the available experimental observations. We demonstrate that VLP self-assembly phenomena and dependencies are now possible to be simulated. The method developed can be used for the parameterization of other macromolecules, enabling a molecular understanding of processes impossible to be attained with other theoretical models.en
dc.language.isoende_DE
dc.publisherMultidisciplinary Digital Publishing Institutede_DE
dc.relation.ispartofInternational journal of molecular sciencesde_DE
dc.rightsCC BY 4.0de_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de_DE
dc.subjectmultiscale modelingde_DE
dc.subjectmolecular discrete element methodde_DE
dc.subjectsupervised learningde_DE
dc.subjectmacromolecular self-assemblyde_DE
dc.subjectcapsid formationde_DE
dc.subjecthepatitis B VLPde_DE
dc.subject.ddc570: Biowissenschaften, Biologiede_DE
dc.titleHierarchical coarse-grained strategy for macromolecular self-assembly : application to hepatitis B virus-like particlesde_DE
dc.typeArticlede_DE
dc.date.updated2022-12-09T20:22:58Z-
dc.identifier.doi10.15480/882.4782-
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0205415-
tuhh.oai.showtruede_DE
tuhh.abstract.englishMacromolecular self-assembly is at the basis of many phenomena in material and life sciences that find diverse applications in technology. One example is the formation of virus-like particles (VLPs) that act as stable empty capsids used for drug delivery or vaccine fabrication. Similarly to the capsid of a virus, VLPs are protein assemblies, but their structural formation, stability, and properties are not fully understood, especially as a function of the protein modifications. In this work, we present a data-driven modeling approach for capturing macromolecular self-assembly on scales beyond traditional molecular dynamics (MD), while preserving the chemical specificity. Each macromolecule is abstracted as an anisotropic object and high-dimensional models are formulated to describe interactions between molecules and with the solvent. For this, data-driven protein–protein interaction potentials are derived using a Kriging-based strategy, built on high-throughput MD simulations. Semi-automatic supervised learning is employed in a high performance computing environment and the resulting specialized force-fields enable a significant speed-up to the micrometer and millisecond scale, while maintaining high intermolecular detail. The reported generic framework is applied for the first time to capture the formation of hepatitis B VLPs from the smallest building unit, i.e., the dimer of the core protein HBcAg. Assembly pathways and kinetics are analyzed and compared to the available experimental observations. We demonstrate that VLP self-assembly phenomena and dependencies are now possible to be simulated. The method developed can be used for the parameterization of other macromolecules, enabling a molecular understanding of processes impossible to be attained with other theoretical models.de_DE
tuhh.publisher.doi10.3390/ijms232314699-
tuhh.publication.instituteFeststoffverfahrenstechnik und Partikeltechnologie V-3de_DE
tuhh.identifier.doi10.15480/882.4782-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.issue23de_DE
tuhh.container.volume23de_DE
dc.relation.projectTeilprojekt von SPP 1934: Multiskalige modellgestützte Untersuchungen funktionaler Enzym- und Proteinagglomerate für biotechnologische Anwendung - Teil 2: Von der Struktur zur Funktionde_DE
dc.relation.projectOpen-Access-Publikationskosten / 2022-2024 / Technische Universität Hamburg (TUHH)-
dc.rights.nationallicensefalsede_DE
dc.identifier.scopus2-s2.0-85143716099de_DE
datacite.relation.IsSupplementedBydoi:10.15480/336.4676-
tuhh.container.articlenumber14699de_DE
local.status.inpressfalsede_DE
local.type.versionpublishedVersionde_DE
datacite.resourceTypeArticle-
datacite.resourceTypeGeneralJournalArticle-
item.openairetypeArticle-
item.mappedtypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.creatorOrcidDepta, Philipp Nicolas-
item.creatorOrcidDosta, Maksym-
item.creatorOrcidWenzel, Wolfgang-
item.creatorOrcidKozlowska, Mariana-
item.creatorOrcidHeinrich, Stefan-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.creatorGNDDepta, Philipp Nicolas-
item.creatorGNDDosta, Maksym-
item.creatorGNDWenzel, Wolfgang-
item.creatorGNDKozlowska, Mariana-
item.creatorGNDHeinrich, Stefan-
crisitem.author.deptFeststoffverfahrenstechnik und Partikeltechnologie V-3-
crisitem.author.deptMehrskalensimulation von Feststoffsystemen V-EXK1 (H)-
crisitem.author.deptFeststoffverfahrenstechnik und Partikeltechnologie V-3-
crisitem.author.orcid0000-0003-0579-5220-
crisitem.author.orcid0000-0002-7578-8408-
crisitem.author.orcid0000-0001-9487-4689-
crisitem.author.orcid0000-0001-8471-8914-
crisitem.author.orcid0000-0002-7901-1698-
crisitem.author.parentorgStudiendekanat Verfahrenstechnik (V)-
crisitem.author.parentorgEhemalige Einrichtungen der TU Hamburg-
crisitem.author.parentorgStudiendekanat Verfahrenstechnik (V)-
crisitem.project.funderDeutsche Forschungsgemeinschaft (DFG)-
crisitem.project.funderDeutsche Forschungsgemeinschaft (DFG)-
crisitem.project.funderid501100001659-
crisitem.project.funderid501100001659-
crisitem.project.funderrorid018mejw64-
crisitem.project.funderrorid018mejw64-
crisitem.project.grantnoHE 4526/19-2-
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