The Uppsala Electron-Density Server.

By Gerard J. Kleywegt, M. Harris, Jinxun Zou, T. C. Taylor, A. Wählby, T. Jones. 2004

344 citations recorded by the source corpus.

The Uppsala Electron Density Server (EDS; http://eds.bmc.uu.se/) is a web-based facility that provides access to electron-density maps and statistics concerning the fit of crystal structures and their maps. Maps are available for approximately 87% of the crystallographic Protein Data Bank (PDB) entries for which structure factors have been deposited and for which straightforward map calculations succeed in reproducing the published R value to within five percentage points. Here, an account is provided of the methods that are used to generate the information contained in the server. Some of the problems that are encountered in the map-generation process as well as some spin-offs of the project are also discussed.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Features and development of Coot (2010, neutral): “Similarly, in the case of structures for which experimental data have been deposited, the model and phased reﬂections may both be obtained from the Electron Density Server (Kleywegt et al. , 2004).”
MolProbity: More and better reference data for improved all‐atom structure validation (2018, methodology): “7 MolProbity has some features specifically tailored for X-ray crystallography, and is also suitable, and used, for cryoEM, neutron, NMR, and computational models.”
PDBe: Protein Data Bank in Europe (2010, methodology): “In addition to providing the annotated structures to the depositor, we also include detailed chemical dictionary descriptions for new ligands (22), Electron Density Server ( 24 ) validation reports for X-ray entries, quaternary structure descriptions from PISA (25) and sequence and taxonomy cross-re”
A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions (2011, positive): “We first determined the full list of protein-containing PDB entries for which we could obtain electron densities from the Uppsala Electron Density Server (EDS) (Kleywegt et al., 2004).”
Prediction of amino acid side chain conformation using a deep neural network (2017, methodology): “residues with mis-assigned conformers as independently confirmed by RSRZ outlier analysis(18, 25) which accounted for ~8% of the outliers (shown in green), and misassigned conformers not identified by RSRZ outliers which accounted for ~48% (shown in cyan).”
MolProbity’s Ultimate Rotamer-Library Distributions for Model Validation (2016, methodology): “To remedy this, the current dataset was additionally filtered by a local real-space correlation coefficient (RSCC) metric [34] and by local map value.”
A public database of macromolecular diffraction experiments. (2016, methodology): “…numerous advances in macromolecular structure validation, such as the now obligatory Rfree factor (Brünger, 1992) and more recently the RSRZ test (Kleywegt et al., 2004) in the new wwPDB validation tools, MolProbity (Davis et al., 2007; Chen et al., 2010) and other statistical quality…”
Implementing an X-ray validation pipeline for the Protein Data Bank (2012, methodology): “…between the model and data, the VTF recommends the use of R and Rfree (Brünger, 1992) as global parameters and per-residue assessment of the real-space R value (RSR; Jones et al., 1991) by calculating RSR-Z scores as is performed by the Uppsala Electron-Density Server (EDS; Kleywegt et al., 2004).”
A New Generation of Crystallographic Validation Tools for the Protein Data Bank (2011, methodology): “Thus, statistics such as RSR Z-scores cannot be calculated for most ligands (Kleywegt et al., 2004).”
PubChem3D: conformer ensemble accuracy (2013, methodology): “In the present study, the same criteria as used in their study (RSCC > 0.9, RSR 0.2, and 1 OWAB 50) were applied, after downloading these data from the electron density server (EDS) [49,50].”
PDB_REDO: constructive validation, more than just looking for errors (2012, background): “Reproducing R factors is known to be a challenging problem (Kleywegt et al., 2004; Afonine et al., 2010) and many problems can be reduced to a lack of knowledge of the original refinement parameters, e.g. about the treatment of bulk solvent.”
Using iterative fragment assembly and progressive sequence truncation to facilitate phasing and crystal structure determination of distantly related proteins. (2016, neutral): “2(b), there are a number of regions that do not correlate well with the experimental 2m|Fo| D|Fc| A-weighted map calculated by the Electron Density Server (EDS; Kleywegt et al., 2004).”
Recent developments in phasing and structure refinement for macromolecular crystallography (2009, methodology): “In the intervening years, further measures of model-todata agreement have been developed, for example in WhatCheck [54], SFCheck [55], and the electron-density server [ 56 ].”