Hub, Jochen S.

[["dc.bibliographiccitation.artnumber","e0196751"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Ariz-Extreme, Igor"],["dc.contributor.author","Hub, Jochen S."],["dc.date.accessioned","2019-07-09T11:45:49Z"],["dc.date.available","2019-07-09T11:45:49Z"],["dc.date.issued","2018"],["dc.description.abstract","Approximately 90% of the structures in the Protein Data Bank (PDB) were obtained by X-ray crystallography or electron microscopy. Whereas the overall quality of structure is considered high, thanks to a wide range of tools for structure validation, uncertainties may arise from density maps of small molecules, such as organic ligands, ions or water, which are non-covalently bound to the biomolecules. Even with some experience and chemical intuition, the assignment of such disconnected electron densities is often far from obvious. In this study, we suggest the use of molecular dynamics (MD) simulations and free energy calculations, which are well-established computational methods, to aid in the assignment of ambiguous disconnected electron densities. Specifically, estimates of (i) relative binding affinities, for instance between an ion and water, (ii) absolute binding free energies, i.e., free energies for transferring a solute from bulk solvent to a binding site, and (iii) stability assessments during equilibrium simulations may reveal the most plausible assignments. We illustrate this strategy using the crystal structure of the fluoride specific channel (Fluc), which contains five disconnected electron densities previously interpreted as four fluoride and one sodium ion. The simulations support the assignment of the sodium ion. In contrast, calculations of relative and absolute binding free energies as well as stability assessments during free MD simulations suggest that four of the densities represent water molecules instead of fluoride. The assignment of water is compatible with the loss of these densities in the non-conductive F82I/F85I mutant of Fluc. We critically discuss the role of the ion force fields for the calculations presented here. Overall, these findings indicate that MD simulations and free energy calculations are helpful tools for modeling water and ions into crystallographic density maps."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2018"],["dc.identifier.doi","10.1371/journal.pone.0196751"],["dc.identifier.pmid","29771936"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15319"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59312"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1932-6203"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.subject.ddc","530"],["dc.subject.mesh","Binding Sites"],["dc.subject.mesh","Crystallography, X-Ray"],["dc.subject.mesh","Databases, Protein"],["dc.subject.mesh","Electrons"],["dc.subject.mesh","Entropy"],["dc.subject.mesh","Fluorides"],["dc.subject.mesh","Ions"],["dc.subject.mesh","Ligands"],["dc.subject.mesh","Molecular Dynamics Simulation"],["dc.subject.mesh","Protein Binding"],["dc.subject.mesh","Proteins"],["dc.subject.mesh","Thermodynamics"],["dc.subject.mesh","Water"],["dc.title","Assigning crystallographic electron densities with free energy calculations-The case of the fluoride channel Fluc."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1005800"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Shevchuk, Roman"],["dc.contributor.author","Hub, Jochen S."],["dc.date.accessioned","2019-07-09T11:44:38Z"],["dc.date.available","2019-07-09T11:44:38Z"],["dc.date.issued","2017-10"],["dc.description.abstract","Small-angle X-ray scattering is an increasingly popular technique used to detect protein structures and ensembles in solution. However, the refinement of structures and ensembles against SAXS data is often ambiguous due to the low information content of SAXS data, unknown systematic errors, and unknown scattering contributions from the solvent. We offer a solution to such problems by combining Bayesian inference with all-atom molecular dynamics simulations and explicit-solvent SAXS calculations. The Bayesian formulation correctly weights the SAXS data versus prior physical knowledge, it quantifies the precision or ambiguity of fitted structures and ensembles, and it accounts for unknown systematic errors due to poor buffer matching. The method further provides a probabilistic criterion for identifying the number of states required to explain the SAXS data. The method is validated by refining ensembles of a periplasmic binding protein against calculated SAXS curves. Subsequently, we derive the solution ensembles of the eukaryotic chaperone heat shock protein 90 (Hsp90) against experimental SAXS data. We find that the SAXS data of the apo state of Hsp90 is compatible with a single wide-open conformation, whereas the SAXS data of Hsp90 bound to ATP or to an ATP-analogue strongly suggest heterogenous ensembles of a closed and a wide-open state."],["dc.identifier.doi","10.1371/journal.pcbi.1005800"],["dc.identifier.pmid","29045407"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14848"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59056"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1553-7358"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","572"],["dc.subject.mesh","Bayes Theorem"],["dc.subject.mesh","Computational Biology"],["dc.subject.mesh","Molecular Dynamics Simulation"],["dc.subject.mesh","Protein Conformation"],["dc.subject.mesh","Proteins"],["dc.subject.mesh","Scattering, Small Angle"],["dc.subject.mesh","X-Ray Diffraction"],["dc.title","Bayesian refinement of protein structures and ensembles against SAXS data using molecular dynamics."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]