Assessing and maximizing data quality in macromolecular crystallography

Karplus, P.A.; Diederichs, Kay

doi:10.1016/j.sbi.2015.07.003

Cited by 210 publications

(185 citation statements)

References 48 publications

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“…The resulting MS/MS datasets were analyzed using PEAKS7 program (Bioinformatics Solutions Inc., Waterloo, ON, Canada). A database containing 16 possible sequences of WFA and a known set of , where j donates mean error for half-dataset (61).…”

Section: Methodsmentioning

confidence: 99%

Molecular Basis for Recognition of the Cancer Glycobiomarker, LacdiNAc (GalNAc[β1→4]GlcNAc), by Wisteria floribunda Agglutinin

Haji-Ghassemi

Gilbert

Spence

et al. 2016

Journal of Biological Chemistry

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Neoplastic transformation often results in cells with unusual glycosylation patterns specific to the type and stage of different cancers, providing numerous venues for therapeutic and diagnostic reagents (1, 2). Lectins are non-catalytic proteins with strict specificity for individual mono-or oligosaccharides, which can make them powerful tools for detecting changes in the carbohydrate structure in glycoproteins and glycolipids.

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Section: Methodsmentioning

confidence: 99%

Molecular Basis for Recognition of the Cancer Glycobiomarker, LacdiNAc (GalNAc[β1→4]GlcNAc), by Wisteria floribunda Agglutinin

Haji-Ghassemi

Gilbert

Spence

et al. 2016

Journal of Biological Chemistry

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“…For example, the shorter constructs, KDM5A(1-588)⌬AP, KDM5B(1-604)⌬AP, and KDM5C(1-618)⌬AP, generated 20 -30 mg of soluble protein from a 1-liter culture. 56. d ͗I/I͘ ϭ averaged ratio of the intensity (I) to the error of the intensity (I).…”

Section: A Hypothetical Model Of the N-terminal Half Of Kdm5-tomentioning

confidence: 99%

Characterization of a Linked Jumonji Domain of the KDM5/JARID1 Family of Histone H3 Lysine 4 Demethylases

Horton¹,

Engstrom²,

Zoeller³

et al. 2016

Journal of Biological Chemistry

104

165

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The KDM5/JARID1 family of Fe(II)-and ␣-ketoglutarate-dependent demethylases remove methyl groups from tri-and dimethylated lysine 4 of histone H3. Accumulating evidence from primary tumors and model systems supports a role for KDM5A (JARID1A/RBP2) and KDM5B (JARID1B/PLU1) as oncogenic drivers. The KDM5 family is unique among the Jumonji domain-containing histone demethylases in that there is an atypical insertion of a DNA-binding ARID domain and a histone-binding PHD domain into the Jumonji domain, which separates the catalytic domain into two fragments (JmjN and JmjC). Here we demonstrate that internal deletion of the ARID and PHD1 domains has a negligible effect on in vitro enzymatic kinetics of the KDM5 family of enzymes. We present a crystal structure of the linked JmjN-JmjC domain from KDM5A, which reveals that the linked domain fully reconstitutes the cofactor (metal ion and ␣-ketoglutarate) binding characteristics of other structurally characterized Jumonji domain demethylases. Docking studies with GSK-J1, a selective inhibitor of the KDM6/ KDM5 subfamilies, identify critical residues for binding of the inhibitor to the reconstituted KDM5 Jumonji domain. Further, we found that GSK-J1 inhibited the demethylase activity of KDM5C with 8.5-fold increased potency compared with that of KDM5B at 1 mM ␣-ketoglutarate. In contrast, JIB-04 (a paninhibitor of the Jumonji demethylase superfamily) had the opposite effect and was ϳ8-fold more potent against KDM5B than against KDM5C. Interestingly, the relative selectivity of JIB-04 toward KDM5B over KDM5C in vitro translates to a ϳ10 -50-fold greater growth-inhibitory activity against breast cancer cell lines. These data define the minimal requirements for enzymatic activity of the KDM5 family to be the linked JmjNJmjC domain coupled with the immediate C-terminal helical zinc-binding domain and provide structural characterization of the linked JmjN-JmjC domain for the KDM5 family, which should prove useful in the design of KDM5 demethylase inhibitors with improved potency and selectivity.

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“…As was also shown (Karplus & Diederichs, 2015), another relation exists between CC 1/2 and the signal-to-noise ratio when the signal is normally distributed. The two equations linking CC 1/2 to CC* and the signal-to-noise ratio hIi/hi, respectively, can then be combined as…”

Section: Methods and Theorymentioning

confidence: 53%

Dissecting random and systematic differences between noisy composite data sets

Diederichs

2017

Acta Crystallogr D Struct Biol

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Composite data sets measured on different objects are usually affected by random errors, but may also be influenced by systematic (genuine) differences in the objects themselves, or the experimental conditions. If the individual measurements forming each data set are quantitative and approximately normally distributed, a correlation coefficient is often used to compare data sets. However, the relations between data sets are not obvious from the matrix of pairwise correlations since the numerical value of the correlation coefficient is lowered by both random and systematic differences between the data sets. This work presents a multidimensional scaling analysis of the pairwise correlation coefficients which places data sets into a unit sphere within low-dimensional space, at a position given by their CC* values [as defined by Karplus & Diederichs (2012),Science,336, 1030–1033] in the radial direction and by their systematic differences in one or more angular directions. This dimensionality reduction can not only be used for classification purposes, but also to derive data-set relations on a continuous scale. Projecting the arrangement of data sets onto the subspace spanned by systematic differences (the surface of a unit sphere) allows, irrespective of the random-error levels, the identification of clusters of closely related data sets. The method gains power with increasing numbers of data sets. It is illustrated with an example from low signal-to-noise ratio image processing, and an application in macromolecular crystallography is shown, but the approach is completely general and thus should be widely applicable.

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Assessing and maximizing data quality in macromolecular crystallography

Cited by 210 publications

References 48 publications

Molecular Basis for Recognition of the Cancer Glycobiomarker, LacdiNAc (GalNAc[β1→4]GlcNAc), by Wisteria floribunda Agglutinin

Molecular Basis for Recognition of the Cancer Glycobiomarker, LacdiNAc (GalNAc[β1→4]GlcNAc), by Wisteria floribunda Agglutinin

Characterization of a Linked Jumonji Domain of the KDM5/JARID1 Family of Histone H3 Lysine 4 Demethylases

Dissecting random and systematic differences between noisy composite data sets

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