Experimental Inferential Structure Determination of Ensembles for Intrinsically Disordered Proteins.
We develop a Bayesian approach to determine the most probable structural ensemble model from candidate structures for intrinsically disordered proteins (IDPs) that takes full advantage of NMR chemical shifts and J-coupling data, their known errors and variances, and the quality of the theoretical ba...
| Publicado en: | Journal of the American Chemical Society Vol. 138; no. 13; pp. 4530 - 4539 |
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| Autores principales: | , |
| Formato: | Artículo |
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American Chemical Society
4/6/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=114714944&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 114714944 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00027863 ACS jtl: Journal of the American Chemical Society issn: 00027863 maglogo: N pubinfo: dt: 4/6/2016 vid: 138 iid: 13 pid: 997 pub: American Chemical Society artinfo: ui: 114714944 10.1021/jacs.6b00351 ppf: 4530 ppct: 9 formats: tig: atl: Experimental Inferential Structure Determination of Ensembles for Intrinsically Disordered Proteins. aug: au: Brookes, David H. Head-Gordon, Teresa affil: Department of Bioengineering, University of California, Berkeley, California 94720, United States Department of Chemistry, University of California, Berkeley, California 94720, United States Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States Chemical Sciences Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, California 94720, United States su: Protein research Nuclear magnetic resonance Mathematical optimization Boltzmann's equation Conformational analysis sug: subj: Protein research Nuclear magnetic resonance Mathematical optimization Boltzmann's equation Conformational analysis ab: We develop a Bayesian approach to determine the most probable structural ensemble model from candidate structures for intrinsically disordered proteins (IDPs) that takes full advantage of NMR chemical shifts and J-coupling data, their known errors and variances, and the quality of the theoretical back-calculation from structure to experimental observables. Our approach differs from previous formulations in the optimization of experimental and back-calculation nuisance parameters that are treated as random variables with known distributions, as opposed to structural or ensemble weight optimization or use of a reference ensemble. The resulting experimental inferential structure determination (EISD) method is size extensive with O(N) scaling, with N = number of structures, that allows for the rapid ranking of large ensemble data comprising tens of thousands of conformations. We apply the EISD approach on singular folded proteins and a corresponding set of ∼25 000 misfolded states to illustrate the problems that can arise using Boltzmann weighted priors. We then apply the EISD method to rank IDP ensembles most consistent with the NMR data and show that the primary error for ranking or creating good IDP ensembles resides in the poor back-calculation from structure to simulated experimental observable. We show that a reduction by a factor of 3 in the uncertainty of the back-calculation error can improve the discrimination among qualitatively different IDP ensembles for the amyloid-beta peptide. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2016 holdings: @attributes: islocal: N |
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