Bestimmung von Strukturinformation aus experimentellen Messdaten für Biomoleküle

Gunsteren, Wilfred F. van; Allison, Jane R.; Daura, Xavier; Dolenc, Jožica; Hansen, Niels; Mark, Alan E.; Oostenbrink, Chris; Rusu, Victor H.; Smith, Lorna J.

doi:10.1002/ange.201601828

Cited by 8 publications

(4 citation statements)

References 137 publications

(234 reference statements)

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“…Therefore, virtual atomic positions for prochiral CH 2 (C β in Ser), for CH (C α and C β in Thr) and pseudo atomic positions for CH 3 (C1 in GalNAc and C γ in Thr) were used to calculate interproton distances. For the NOE analysis, since experimental NOE distances represent an average over space and time, and for small molecules the NOE intensity is proportional to r −6 , averaging is performed as

{⟨r^{- 6}⟩}^{- 1 / 6}

. More elaborate approaches to compute the NMR spectra explicitly from simulation trajectories were shown to deviate by at most 8–9 % from this inverse sixth power assumption …”

Section: Methodsmentioning

confidence: 99%

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Structural Aspects of the O‐glycosylation Linkage in Glycopeptides via MD Simulations and Comparison with NMR Experiments

et al. 2019

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A powerful conformational searching and enhanced sampling simulation method, and unbiased molecular dynamics simulations have been used along with NMR spectroscopic observables to provide a detailed structural view of O‐glycosylation. For four model systems, the force‐field parameters can accurately predict experimental NMR observables (J couplings and NOE's). This enables us to derive conclusions based on the generated ensembles, in which O‐glycosylation affects the peptide backbone conformation by forcing it towards to an extended conformation. An exception is described for β ‐GalNAc‐Thr where the α content is increased and stabilized via hydrogen bonding between the sugar and the peptide backbone, which was not observed in the rest of the studied systems. These observations might offer an explanation for the evolutionary preference of α ‐linked GalNAc glycosylation instead of a β link.

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{⟨r^{- 6}⟩}^{- 1 / 6}

. More elaborate approaches to compute the NMR spectra explicitly from simulation trajectories were shown to deviate by at most 8–9 % from this inverse sixth power assumption …”

Section: Methodsmentioning

confidence: 99%

“…3 J‐coupling can be related to a torsional angle through the Karplus relation. At particular angles, variations in the Karplus relation parameters (a, b, c) lead to differences of up to 3 Hz even though the experimental uncertainties are generally lower than 0.1 Hz …”

Section: Methodsmentioning

confidence: 99%

Structural Aspects of the O‐glycosylation Linkage in Glycopeptides via MD Simulations and Comparison with NMR Experiments

et al. 2019

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“…Thus, interproton distances involving the aliphatic hydrogen atoms were calculated using virtual atomic positions (for CH, and prochiral CH 2 ) and pseudoatomic positions (for CH 3 ) for these hydrogen atoms . The pseudoatom NOE bound distance corrections of Wüthrich et al were used, see, e.g., ref .…”

Section: Methodsmentioning

confidence: 99%

“…These issues make deriving a structural model for a protein based on NMR data a rather nontrivial task, beset with a variety of pitfalls and ambiguities . One way to reduce the latter is by combining NMR data of different types, e.g., NOEs, S 2 order parameters, and 3 J -couplings in structure determination or refinement of a particular protein.…”

Section: Introductionmentioning

confidence: 99%

Using Complementary NMR Data Sets To Detect Inconsistencies and Model Flaws in the Structure Determination of Human Interleukin-4

2017

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The derivation of protein structure from values of observable quantities measured in NMR experiments is a rather nontrivial task due to (i) the limited number of data compared to degrees of freedom of a protein, (ii) the uncertainty inherent to the function connecting an observable quantity to molecular structure, (iii) the finite quality of biomolecular models and force fields used in structure refinement, and (iv) the conformational freedom of a protein in aqueous solution, which requires extensive conformational sampling and appropriate conformational averaging when calculating or restraining to sets of NMR data. The protein interleukin-4 (IL-4) has been taken as a test case using NOE distances, S order parameters, and J-couplings as test data and the former two types of data as restraints. It is shown that, by combining sets of different, complementary NMR data as restraints in MD simulations, inconsistencies in the data or flaws in the model and procedures used to derive protein structure from NMR data can be detected. This leads to an improved structural interpretation of such data particularly in more mobile loop regions.

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Validierung von molekularen Simulationen: eine Übersicht verschiedener Aspekte

et al. 2017

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Computersimulationen molekularer Systeme ermöglichen die Beschreibung von Struktur‐Energie‐Funktions‐Beziehungen molekularer Prozesse auf subatomarer, atomarer, supraatomarer oder supramolekularer Ebene. Um die Ergebnisse von solchen Simulationen sachgerecht zu interpretieren, muss zunächst die Qualität der berechneten Eigenschaften evaluiert werden. Diese ist abhängig von der Art und Weise, wie die Simulationen durchgeführt wurden und wie diese validiert wurden durch Vergleich mit Werten Qexp experimentell messbarer Grössen Q. Die folgenden Punkte müssen berücksichtigt werden: 1) die Genauigkeit von Qexp, 2) die Genauigkeit der Funktion Q(rN), die genutzt wird, um Q als Funktion der Konfiguration rN von N Partikeln zu berechnen, 3) die Empfindlichkeit der Funktion Q(rN) hinsichtlich der Konfiguration rN, 4) die relativen Zeitskalen der Simulation und des Experiments, 5) der Grad der Äquivalenz von berechneten und experimentellen Eigenschaften und 6) der Grad, bis zu dem das simulierte System den experimentellen Gegebenheiten entspricht. Experimentelle Daten sind in ihrem Geltungsbereich eingeschränkt und entsprechen im Allgemeinen zeitlichen sowie räumlichen Mittelwerten. Eine kritische Analyse verschiedener Faktoren, die den scheinbaren Grad der (Nicht‐)Übereinstimmung zwischen Simulationen und Experiment beeinflussen, werden in diesem Aufsatz präsentiert und anhand von Beispielen aus der Literatur illustriert. Maßnahmen, um die Validierung molekularer Simulationen zu verbessern, werden ebenfalls besprochen.

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Bestimmung von Strukturinformation aus experimentellen Messdaten für Biomoleküle

Abstract: Die Identifikationsnummer (ORCID) eines Autors ist unter http://dx.

Cited by 8 publications

References 137 publications

Structural Aspects of the O‐glycosylation Linkage in Glycopeptides via MD Simulations and Comparison with NMR Experiments

Structural Aspects of the O‐glycosylation Linkage in Glycopeptides via MD Simulations and Comparison with NMR Experiments

Using Complementary NMR Data Sets To Detect Inconsistencies and Model Flaws in the Structure Determination of Human Interleukin-4

Validierung von molekularen Simulationen: eine Übersicht verschiedener Aspekte

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