PDBe: Protein Data Bank in Europe

Gutmanas, Aleksandras; Alhroub, Younes; Battle, Gary M.; Berrisford, John M.; Bochet, Estelle; Conroy, Matthew J.; Dana, Jose M.; Montecelo, Manuel A Fernandez; Ginkel, Glen van; Gore, Swanand; Haslam, Pauline; Hatherley, Rowan; Hendrickx, Pieter; Hirshberg, Miriam; Lagerstedt, Ingvar; Mir, Saqib; Mukhopadhyay, Abhik; Oldfield, Thomas J.; Patwardhan, Ardan; Rinaldi, Luana; Sahni, Gaurav; Sanz-García, Eduardo; Sen, Sanchayita; Slowley, R.; Velankar, Sameer; Wainwright, Michael; Kleywegt, Gerard J.

doi:10.1093/nar/gkt1180

Cited by 160 publications

(91 citation statements)

References 41 publications

(56 reference statements)

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“…Models of biomolecular structures determined experimentally, by X-ray diffraction or Nuclear Magnetic Resonance (NMR) spectroscopy, are deposited in a world-wide public repository, the Protein Data Bank (PDB, http://www.pdb.org) [1–4]. Electron Microscopy (EM) models are deposited in a separate database, EMDB (http://www.emdatabank.org/) [5, 6].…”

Section: Protein–ligand Modelsmentioning

confidence: 99%

Models of protein–ligand crystal structures: trust, but verify

Deller

Rupp

2015

J Comput Aided Mol Des

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X-ray crystallography provides the most accurate models of protein–ligand structures. These models serve as the foundation of many computational methods including structure prediction, molecular modelling, and structure-based drug design. The success of these computational methods ultimately depends on the quality of the underlying protein–ligand models. X-ray crystallography offers the unparalleled advantage of a clear mathematical formalism relating the experimental data to the protein–ligand model. In the case of X-ray crystallography, the primary experimental evidence is the electron density of the molecules forming the crystal. The first step in the generation of an accurate and precise crystallographic model is the interpretation of the electron density of the crystal, typically carried out by construction of an atomic model. The atomic model must then be validated for fit to the experimental electron density and also for agreement with prior expectations of stereochemistry. Stringent validation of protein–ligand models has become possible as a result of the mandatory deposition of primary diffraction data, and many computational tools are now available to aid in the validation process. Validation of protein–ligand complexes has revealed some instances of overenthusiastic interpretation of ligand density. Fundamental concepts and metrics of protein–ligand quality validation are discussed and we highlight software tools to assist in this process. It is essential that end users select high quality protein–ligand models for their computational and biological studies, and we provide an overview of how this can be achieved.

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Section: Protein–ligand Modelsmentioning

confidence: 99%

Models of protein–ligand crystal structures: trust, but verify

Deller

Rupp

2015

J Comput Aided Mol Des

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“…To explore the resolution limit for successful rigid body fitting, we selected five highresolution cryo-EM ribosome maps from the EMDatabank (Lawson et al, 2011), ranging in resolution from 5.5 to 6.9Å, for which structural models were deposited in the Protein Databank (Gutmanas et al, 2014) (Table 1). The ribosome is an excellent case study as it contains many chains of various sizes and types.…”

Section: Exploring the Limits Of Rigid Body Fittingmentioning

confidence: 99%

Defining the limits and reliability of rigid-body fitting in cryo-EM maps using multi-scale image pyramids

Zundert

Bonvin

2016

Journal of Structural Biology

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. Defining the limits and reliability of rigid-body fitting in cryo-EM maps using multi-scale image pyramids. J. Struct. Biol., 195, 252-258 (2016).https://doi.org/10. 1016/j.jsb.2016.06.011 Defining the limits and reliability of rigid-body fitting in cryo-EM maps using multi-scale image pyramids Keywords1Cross correlation; PowerFit; Ribosome; Fisher z-transformation; Core-weighted; Modeling. AbstractCryo-electron microscopy provides fascinating structural insight into large macromolecular machines at increasing detail. Despite significant advances in the field, the resolution of the resulting three-dimensional images is still typically insufficient for de novo model building.To bridge the resolution gap and give an atomic interpretation to the data, high-resolution models are typically placed into the density as rigid bodies. Unfortunately, this is often done manually using graphics software, a subjective method that can lead to over-interpretation of the data. A more objective approach is to perform an exhaustive cross-correlation-based search to fit subunits into the density. Here we show, using five experimental ribosome maps ranging in resolution from 5.5 to 6.9Å, that cross-correlation-based fitting is capable of successfully fitting subunits correctly in the density for over 90% of the cases. Importantly, we provide indicators for the reliability and ambiguity of a fit, using the Fisher ztransformation and its associated confidence intervals, giving a formal approach to identify over-interpreted regions in the density. In addition, we quantify the resolution requirement for a successful fit as a function of the subunit size. For larger subunits the resolution of the data can be down-filtered to 20Å while still retaining an unambiguous fit. We leverage this information through the use of multi-scale image pyramids to accelerate the search up to 30-fold on CPUs and 40-fold on GPUs at a negligible loss in success rate. We implemented this approach in our rigid-body fitting software PowerFit, which can be freely downloaded from https://github.com/haddocking/powerfit.

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“…In 2003, the Worldwide Protein Data Bank (wwPDB) was formed, uniting these centers to ensure that the PDB would remain a global, publicly available, and uniform archive [16,17]. The wwPDB partners (RCSB PDB [15]; Protein Data Bank in Europe, PDBe [18]; Protein Data Bank Japan, PDBj [19]) developed clear guidelines and policies for data deposition and annotation. In 2006, the BioMagResBank (BMRB) archive of NMR experimental data joined the wwPDB [20,21].…”

Section: Evolution Of the Protein Data Bankmentioning

confidence: 99%