000903372 001__ 903372
000903372 005__ 20211216142207.0
000903372 037__ $$aFZJ-2021-05061
000903372 041__ $$aEnglish
000903372 1001_ $$0P:(DE-Juel1)184777$$ade Bruyn, Emile$$b0$$eCorresponding author
000903372 1112_ $$aINM & IBI Retreat 2021$$cJülich$$d2021-10-05 - 2021-10-06$$wGermany
000903372 245__ $$aModelling Ion-Residue Interaction in Implicit Solvation for Intrinsically Disordered Proteins
000903372 260__ $$c2021
000903372 3367_ $$033$$2EndNote$$aConference Paper
000903372 3367_ $$2BibTeX$$aINPROCEEDINGS
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000903372 502__ $$cRWTH Aachen
000903372 520__ $$aThis project aims to develop a new implicit solvent model that enables rapid and accurate exploration of the conformational space of Intrinsically Disordered Proteins (IDPs).IDPs are characterised by their structural heterogeneity and visit diverse and transient conformational ensembles. These proteins have attracted broad interest due to their ubiquitous involvement in biological function and dysfunction, including promising therapeutic targets. This makes characterisation of IDPs' structure-function relationships both essential and challenging.Molecular Dynamics (MD) simulations are commonly used to explore the conformational landscape of IDPs. These explorations mostly overlook interactions between ions and solvent with individual amino acid residues that reshape the energy landscape and drive ensemble switching in IDPs. MD simulations are also unfortunately slow to stabilise complex aggregates of solvent and solutes, even though these simulations are drastically reduced in complexity compared to biological conditions. Ion motions have been identified as a rate-determining step in the already large free energy surface sample required, and as such impede the exploration and study of IDP dynamics. Approaches to avoid this rate determining step exist: coarse-grained models combined with Monte Carlo (MC) simulation approaches have already proven valuable in characterising several IDPs. Achieving finer accuracy through atomistic MC simulations require implicit or continuum solvent, which at present insufficiently model the interactions between ions and residues, and therefore cannot accurately explore IDPs’ conformational landscape.To accurately model the effect of ions on individual amino acid residues, this project uses existing trajectory data and data from highly time-resolved state-of-the-art MD simulations. The positional data within MD trajectories are used as input for Machine Learning to obtain data-driven parameters for a new implicit solvent model.
000903372 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000903372 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x1
000903372 7001_ $$0P:(DE-Juel1)132307$$aZimmermann, Olav$$b1
000903372 7001_ $$0P:(DE-HGF)0$$aGrohe, Martin$$b2
000903372 7001_ $$0P:(DE-Juel1)145921$$aRossetti, Giulia$$b3
000903372 8564_ $$uhttp://www.csn.fz-juelich.de/inmibi2021/
000903372 909CO $$ooai:juser.fz-juelich.de:903372$$pVDB
000903372 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)184777$$aForschungszentrum Jülich$$b0$$kFZJ
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000903372 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132307$$aForschungszentrum Jülich$$b1$$kFZJ
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000903372 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aInformatik RWTH$$b2
000903372 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145921$$aForschungszentrum Jülich$$b3$$kFZJ
000903372 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)145921$$aRWTH Aachen$$b3$$kRWTH
000903372 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
000903372 9141_ $$y2021
000903372 920__ $$lyes
000903372 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000903372 9201_ $$0I:(DE-Juel1)IAS-5-20120330$$kIAS-5$$lComputational Biomedicine$$x1
000903372 9201_ $$0I:(DE-Juel1)INM-9-20140121$$kINM-9$$lComputational Biomedicine$$x2
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000903372 980__ $$aI:(DE-Juel1)INM-9-20140121
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