Validation of Structures in the Protein Data Bank

Gore, Swanand; Sanz-García, Eduardo; Hendrickx, Pieter; Gutmanas, Aleksandras; Westbrook, John D.; Yang, Huanwang; Feng, Zhang; Baskaran, Kumaran; Berrisford, John M.; Hudson, Brian P.; Ikegawa, Yasuyo; Kobayashi, Naohiro; Lawson, Catherine L.; Mading, Steve; Mak, Lora; Mukhopadhyay, Abhik; Oldfield, Thomas J.; Patwardhan, Ardan; Peisach, Ezra; Sahni, Gaurav; Sekharan, Monica; Sen, Sanchayita; Shao, Chenghua; Smart, Oliver S.; Ulrich, Eldon L.; Yamashita, Reiko; Quesada, Martha; Young, Jasmine; Nakamura, Haruki; Markley, John L.; Berman, Helen M.; Burley, S.K.; Velankar, Sameer; Kleywegt, Gerard J.

doi:10.1016/j.str.2017.10.009

Cited by 225 publications

(209 citation statements)

References 47 publications

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“…In consultation with community experts [42] [43] [44], wwPDB and EMDB have produced a software pipeline for the validation of structures determined by crystallography, NMR and cryo-EM [45]. The validation process assesses each model in the light of the experimental data and prior knowledge to find out:…”

Section: Validation Of Structural Data and Models Is (Still) Necessarymentioning

confidence: 99%

“…Fortunately, users of the PDB do not have to be expert structural biologists themselves to assess the quality and reliability of models they retrieve from the archive, or even to select the most suitable from a number of alternative models for the same molecule or complex. In consultation with community experts [42][43][44], wwPDB and EMDB have produced a software pipeline for the validation of structures determined by crystallography, NMR and cryo-EM [45]. The validation process assesses each model in the light of the experimental data and prior knowledge to find out:…”

Section: Trust But Verify How Can I Do That?mentioning

confidence: 99%

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Structural biology data archiving – where we are and what lies ahead

2018

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For almost 50 years, structural biology has endeavoured to conserve and share its experimental data and their interpretations (usually, atomistic models) through global public archives such as the Protein Data Bank, Electron Microscopy Data Bank and Biological Magnetic Resonance Data Bank (BMRB). These archives are treasure troves of freely accessible data that document our quest for molecular or atomic understanding of biological function and processes in health and disease. They have prepared the field to tackle new archiving challenges as more and more (combinations of) techniques are being utilized to elucidate structure at ever increasing length scales. Furthermore, the field has made substantial efforts to develop validation methods that help users to assess the reliability of structures and to identify the most appropriate data for their needs. In this Review, we present an overview of public data archives in structural biology and discuss the importance of validation for users and producers of structural data. Finally, we sketch our efforts to integrate structural data with bioimaging data and with other sources of biological data. This will make relevant structural information available and more easily discoverable for a wide range of scientists.

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Section: Validation Of Structural Data and Models Is (Still) Necessarymentioning

confidence: 99%

Section: Trust But Verify How Can I Do That?mentioning

confidence: 99%

Structural biology data archiving – where we are and what lies ahead

2018

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“…The wwPDB has initiated several efforts to improve the quality of entries being deposited, including launching a deposition, biocuration, and validation tool: OneDep [6]. Many data quality measures are now available for PDB structures, such as a resolution, b-factors, MolProbity clashscores [7], to name a few. However, low-quality regions can still exist even in structures with very good metrics of global structural quality, as shown in the overlay of structures with electron density maps in Fig 1. These low-quality regions arise from structural model and electron density mismatches can be due to a variety of reasons including problems with regional protein mobility [8], data processing [9], or model fitting [10].…”

Section: Introductionmentioning

confidence: 99%

A chemical interpretation of protein electron density maps in the worldwide protein data bank

Yao

Moseley

2019

Preprint

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High-quality three-dimensional structural data is of great value for the functional interpretation of biomacromolecules, especially proteins; however, structural quality varies greatly across the entries in the worldwide Protein Data Bank (wwPDB). Since 2008, the wwPDB has required the inclusion of structure factors with the deposition of x-ray crystallographic structures to support the independent evaluation of structures with respect to the underlying experimental data used to derive those structures. However, interpreting the discrepancies between the structural model and its underlying electron density data is difficult, since derived electron density maps use arbitrary electron density units which are inconsistent between maps from different wwPDB entries. Therefore, we have developed a method that converts electron density values into units of electrons. With this conversion, we have developed new methods that can evaluate specific regions of an x-ray crystallographic structure with respect to a physicochemical interpretation of its corresponding electron 2 density map. We have systematically compared all deposited x-ray crystallographic protein models in the wwPDB with their underlying electron density maps, if available, and characterized the electron density in terms of expected numbers of electrons based on the structural model. The methods generated coherent evaluation metrics throughout all PDB entries with associated electron density data, which are consistent with visualization software that would normally be used for manual quality assessment. To our knowledge, this is the first attempt to derive units of electrons directly from electron density maps without the aid of the underlying structure factors. These new metrics are biochemically-informative and can be extremely useful for filtering out low-quality structural regions from inclusion into systematic analyses that span large numbers of PDB entries. Furthermore, these new metrics will improve the ability of non-crystallographers to evaluate regions of interest within PDB entries, since only the PDB structure and the associated electron density maps are needed. These new methods are available as a well-documented Python package on GitHub and the Python Package Index under a modified Clear BSD open source license. Author summaryElectron density maps are very useful for validating the x-ray structure models in the Protein Data Bank (PDB). However, it is often daunting for non-crystallographers to use electron density maps, as it requires a lot of prior knowledge. This study provides methods that can infer chemical information solely from the electron density maps available from the PDB to interpret the electron density and electron density discrepancy values in terms of units of electrons. It also provides methods to evaluate regions of interest in terms of the number of missing or excessing electrons, so that a broader audience, such as biologists or bioinformaticians, can also make better use of the electron density information available in the ...

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“…These VTFs consist of experts from the community who provide recommendations on assessing the quality of both the coordinate model and the supporting experimental evidence for each experimental method. The three VTFs for crystallography (Read et al, 2011), NMR (Montelione et al, 2013) and 3DEM (Henderson et al, 2012), which was established in collaboration with the EMDB project partners (Lawson et al, 2011), have provided recommendations which have now been implemented in the wwPDB validation pipeline (Gore et al, 2012(Gore et al, , 2017. The introduction of this pipeline, and the wwPDB validation report (VR) files that it produces (in both humanreadable and machine-readable formats), has significantly expanded the scope of quality assessment provided by the wwPDB partners.…”

Section: Introductionmentioning

confidence: 99%

“…A full description of the different sections of the VR has recently been published (Gore et al, 2017). Here, we describe the most important summary sections that facilitate quality assessment by both expert and nonspecialist users.…”

Section: Introductionmentioning

confidence: 99%

Worldwide Protein Data Bank validation information: usage and trends

Smart

Horský

Gore

et al. 2018

Acta Cryst Sect D Struct Biol

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Realising the importance of assessing the quality of the biomolecular structures deposited in the Protein Data Bank (PDB), the Worldwide Protein Data Bank (wwPDB) partners established Validation Task Forces to obtain advice on the methods and standards to be used to validate structures determined by X-ray crystallography, nuclear magnetic resonance spectroscopy and three-dimensional electron cryo-microscopy. The resulting wwPDB validation pipeline is an integral part of the wwPDB OneDep deposition, biocuration and validation system. The wwPDB Validation Service webserver (https://validate.wwpdb.org) can be used to perform checks prior to deposition. Here, it is shown how validation metrics can be combined to produce an overall score that allows the ranking of macromolecular structures and domains in search results. The ValTrends DB database provides users with a convenient way to access and analyse validation information and other properties of X-ray crystal structures in the PDB, including investigating trends in and correlations between different structure properties and validation metrics.

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Validation of Structures in the Protein Data Bank

Cited by 225 publications

References 47 publications

Structural biology data archiving – where we are and what lies ahead

Structural biology data archiving – where we are and what lies ahead

A chemical interpretation of protein electron density maps in the worldwide protein data bank

Worldwide Protein Data Bank validation information: usage and trends

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