000862389 001__ 862389
000862389 005__ 20220930130210.0
000862389 0247_ $$2doi$$a10.1371/journal.pcbi.1006900
000862389 0247_ $$2ISSN$$a1553-734X
000862389 0247_ $$2ISSN$$a1553-7358
000862389 0247_ $$2Handle$$a2128/22075
000862389 0247_ $$2pmid$$apmid:30901335
000862389 0247_ $$2WOS$$aWOS:000463877900064
000862389 0247_ $$2altmetric$$aaltmetric:58134203
000862389 037__ $$aFZJ-2019-02717
000862389 082__ $$a610
000862389 1001_ $$0P:(DE-HGF)0$$aWeiel, Marie$$b0
000862389 245__ $$aRapid interpretation of small-angle X-ray scattering data
000862389 260__ $$aSan Francisco, Calif.$$bPublic Library of Science$$c2019
000862389 3367_ $$2DRIVER$$aarticle
000862389 3367_ $$2DataCite$$aOutput Types/Journal article
000862389 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1555480376_5262
000862389 3367_ $$2BibTeX$$aARTICLE
000862389 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000862389 3367_ $$00$$2EndNote$$aJournal Article
000862389 520__ $$aThe fundamental aim of structural analyses in biophysics is to reveal a mutual relation between a molecule’s dynamic structure and its physiological function. Small-angle X-ray scattering (SAXS) is an experimental technique for structural characterization of macromolecules in solution and enables time-resolved analysis of conformational changes under physiological conditions. As such experiments measure spatially averaged low-resolution scattering intensities only, the sparse information obtained is not sufficient to uniquely reconstruct a three-dimensional atomistic model. Here, we integrate the information from SAXS into molecular dynamics simulations using computationally efficient native structure-based models. Dynamically fitting an initial structure towards a scattering intensity, such simulations produce atomistic models in agreement with the target data. In this way, SAXS data can be rapidly interpreted while retaining physico-chemical knowledge and sampling power of the underlying force field. We demonstrate our method’s performance using the example of three protein systems. Simulations are faster than full molecular dynamics approaches by more than two orders of magnitude and consistently achieve comparable accuracy. Computational demands are reduced sufficiently to run the simulations on commodity desktop computers instead of high-performance computing systems. These results underline that scattering-guided structure-based simulations provide a suitable framework for rapid early-stage refinement of structures towards SAXS data with particular focus on minimal computational resources and time.
000862389 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000862389 588__ $$aDataset connected to CrossRef
000862389 7001_ $$0P:(DE-HGF)0$$aReinartz, Ines$$b1
000862389 7001_ $$0P:(DE-Juel1)173652$$aSchug, Alexander$$b2$$eCorresponding author$$ufzj
000862389 773__ $$0PERI:(DE-600)2193340-6$$a10.1371/journal.pcbi.1006900$$gVol. 15, no. 3, p. e1006900 -$$n3$$pe1006900 -$$tPLoS Computational Biology$$v15$$x1553-7358$$y2019
000862389 8564_ $$uhttps://juser.fz-juelich.de/record/862389/files/journal.pcbi.1006900.pdf$$yOpenAccess
000862389 8564_ $$uhttps://juser.fz-juelich.de/record/862389/files/journal.pcbi.1006900.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000862389 8767_ $$8PAB240035$$92019-04-02$$d2019-04-16$$eAPC$$jDeposit$$lDeposit: PLoS$$pPCOMPBIOL-D-18-01414$$z2350 USD
000862389 909CO $$ooai:juser.fz-juelich.de:862389$$popenCost$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire$$pdnbdelivery
000862389 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173652$$aForschungszentrum Jülich$$b2$$kFZJ
000862389 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000862389 9141_ $$y2019
000862389 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000862389 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000862389 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000862389 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000862389 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bPLOS COMPUT BIOL : 2017
000862389 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal
000862389 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ
000862389 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000862389 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000862389 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000862389 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000862389 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000862389 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000862389 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000862389 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central
000862389 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000862389 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000862389 9201_ $$0I:(DE-Juel1)NIC-20090406$$kNIC$$lJohn von Neumann - Institut für Computing$$x1
000862389 980__ $$ajournal
000862389 980__ $$aVDB
000862389 980__ $$aUNRESTRICTED
000862389 980__ $$aI:(DE-Juel1)JSC-20090406
000862389 980__ $$aI:(DE-Juel1)NIC-20090406
000862389 980__ $$aAPC
000862389 9801_ $$aAPC
000862389 9801_ $$aFullTexts