scholarly journals Rapid response to emerging biomedical challenges and threats

IUCrJ ◽  
2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Marek Grabowski ◽  
Joanna M. Macnar ◽  
Marcin Cymborowski ◽  
David R. Cooper ◽  
Ivan G. Shabalin ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Biomedical Research ◽  
Large Scale ◽  
Structural Data ◽  
Data Bank ◽  
Rapid Response ◽  
Multiple Resources

As part of the global mobilization to combat the present pandemic, almost 100 000 COVID-19-related papers have been published and nearly a thousand models of macromolecules encoded by SARS-CoV-2 have been deposited in the Protein Data Bank within less than a year. The avalanche of new structural data has given rise to multiple resources dedicated to assessing the correctness and quality of structural data and models. Here, an approach to evaluate the massive amounts of such data using the resource https://covid19.bioreproducibility.org is described, which offers a template that could be used in large-scale initiatives undertaken in response to future biomedical crises. Broader use of the described methodology could considerably curtail information noise and significantly improve the reproducibility of biomedical research.

scholarly journals Implementing an X-ray validation pipeline for the Protein Data Bank

2012 ◽  
Vol 68 (4) ◽  
pp. 478-483 ◽  
Author(s):  
Swanand Gore ◽  
Sameer Velankar ◽  
Gerard J. Kleywegt
Keyword(s):  
Protein Data Bank ◽  
Structural Data ◽  
Data Bank ◽  
Software Pipeline ◽  
X Ray ◽  
Validation Methods ◽  
Task Forces ◽  
Related Information ◽  
Ongoing Work

There is an increasing realisation that the quality of the biomacromolecular structures deposited in the Protein Data Bank (PDB) archive needs to be assessed critically using established and powerful validation methods. The Worldwide Protein Data Bank (wwPDB) organization has convened several Validation Task Forces (VTFs) to advise on the methods and standards that should be used to validate all of the entries already in the PDB as well as all structures that will be deposited in the future. The recommendations of the X-ray VTF are currently being implemented in a software pipeline. Here, ongoing work on this pipeline is briefly described as well as ways in which validation-related information could be presented to users of structural data.

Analyzing Motion Properties of Proteins Affected by Localized Structures From a Robot Kinematics Perspective

2015 ◽  
Author(s):  
Keisuke Arikawa
Keyword(s):  
Protein Data Bank ◽  
Complex Shape ◽  
Structural Data ◽  
Data Bank ◽  
Robot Kinematics ◽  
Motion Prediction ◽  
Serial Manipulators ◽  
Localized Structures ◽  
Motion Modes ◽  
Structural Compliance

On the basis of robot kinematics, we have thus far developed a method for predicting the motion of proteins from their 3D structural data given in the Protein Data Bank (PDB data). In this method, proteins are modeled as serial manipulators constrained by springs and the structural compliance properties of the models are evaluated. We focus on localized instead of whole structures of proteins. Employing the same model used in our method of motion prediction, the motion properties of the localized structures and the relation between the motion properties of localized and whole structures are analyzed. First, we present a method for graphically expressing the deformation of objects with a complex shape, such as proteins, by approximating the shape as a rectangular prism with a mesh on its surface. We then formulate a method for comparing the motion properties of localized structures cleaved from the whole structure and those remaining in it by expressing the motion of the latter using the decomposed motion modes of the former according to the structural compliance. Finally, we show a method for evaluating the effect of a localized structure on the motion properties of proteins by applying forces to localized structures. In the formulations, we demonstrate applications as illustrative examples using the PDB data of a real protein.

scholarly journals Accurate Representation of Protein-Ligand Structural Diversity in the Protein Data Bank (PDB)

2020 ◽  
Vol 21 (6) ◽  
pp. 2243
Author(s):  
Nicolas K. Shinada ◽  
Peter Schmidtke ◽  
Alexandre G. de Brevern
Keyword(s):  
Protein Data Bank ◽  
Protein Sequence ◽  
Large Scale ◽  
Protein Structures ◽  
Structural Diversity ◽  
Data Bank ◽  
Protein Distribution ◽  
Research Areas ◽  
Identity Threshold ◽  
Protein Sequence Identity

The number of available protein structures in the Protein Data Bank (PDB) has considerably increased in recent years. Thanks to the growth of structures and complexes, numerous large-scale studies have been done in various research areas, e.g., protein–protein, protein–DNA, or in drug discovery. While protein redundancy was only simply managed using simple protein sequence identity threshold, the similarity of protein-ligand complexes should also be considered from a structural perspective. Hence, the protein-ligand duplicates in the PDB are widely known, but were never quantitatively assessed, as they are quite complex to analyze and compare. Here, we present a specific clustering of protein-ligand structures to avoid bias found in different studies. The methodology is based on binding site superposition, and a combination of weighted Root Mean Square Deviation (RMSD) assessment and hierarchical clustering. Repeated structures of proteins of interest are highlighted and only representative conformations were conserved for a non-biased view of protein distribution. Three types of cases are described based on the number of distinct conformations identified for each complex. Defining these categories decreases by 3.84-fold the number of complexes, and offers more refined results compared to a protein sequence-based method. Widely distinct conformations were analyzed using normalized B-factors. Furthermore, a non-redundant dataset was generated for future molecular interactions analysis or virtual screening studies.

scholarly journals Protein Data Bank Japan (PDBj): maintaining a structural data archive and resource description framework format

2011 ◽  
Vol 40 (D1) ◽  
pp. D453-D460 ◽  
Author(s):  
A. R. Kinjo ◽  
H. Suzuki ◽  
R. Yamashita ◽  
Y. Ikegawa ◽  
T. Kudou ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Resource Description Framework ◽  
Structural Data ◽  
Data Bank ◽  
Data Archive ◽  
Description Framework ◽  
Resource Description

scholarly journals Worldwide Protein Data Bank validation information: usage and trends

2018 ◽  
Vol 74 (3) ◽  
pp. 237-244 ◽  
Author(s):  
Oliver S. Smart ◽  
Vladimír Horský ◽  
Swanand Gore ◽  
Radka Svobodová Vařeková ◽  
Veronika Bendová ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Three Dimensional ◽  
Data Bank ◽  
Resonance Spectroscopy ◽  
X Ray ◽  
Validation Metrics ◽  
Task Forces ◽  
X Ray Crystallography ◽  
Structure Properties

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 ValTrendsDBdatabase 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.

scholarly journals Experimentally Determined Long Intrinsically Disordered Protein Regions Are Now Abundant in the Protein Data Bank

2020 ◽  
Vol 21 (12) ◽  
pp. 4496 ◽  
Author(s):  
Alexander Miguel Monzon ◽  
Marco Necci ◽  
Federica Quaglia ◽  
Ian Walsh ◽  
Giuseppe Zanotti ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Large Scale ◽  
Large Data ◽  
Intrinsically Disordered Protein ◽  
Data Bank ◽  
Data Sets ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Disordered Protein ◽  
Common Definition

Intrinsically disordered protein regions are commonly defined from missing electron density in X-ray structures. Experimental evidence for long disorder regions (LDRs) of at least 30 residues was so far limited to manually curated proteins. Here, we describe a comprehensive and large-scale analysis of experimental LDRs for 3133 unique proteins, demonstrating an increasing coverage of intrinsic disorder in the Protein Data Bank (PDB) in the last decade. The results suggest that long missing residue regions are a good quality source to annotate intrinsically disordered regions and perform functional analysis in large data sets. The consensus approach used to define LDRs allows to evaluate context dependent disorder and provide a common definition at the protein level.

GeoMine: interactive pattern mining of protein–ligand interfaces in the Protein Data Bank

2020 ◽  
Author(s):  
Konrad Diedrich ◽  
Joel Graef ◽  
Katrin Schöning-Stierand ◽  
Matthias Rarey
Keyword(s):  
Protein Data Bank ◽  
Web Application ◽  
Pattern Mining ◽  
Structural Data ◽  
Data Bank ◽  
Supplementary Information ◽  
User Friendliness ◽  
Iterative Search ◽  
Potential Applications ◽  
Query Generation

Abstract Summary The searching of user-defined 3D queries in molecular interfaces is a computationally challenging problem that is not satisfactorily solved so far. Most of the few existing tools focused on that purpose are desktop based and not openly available. Besides that, they show a lack of query versatility, search efficiency and user-friendliness. We address this issue with GeoMine, a publicly available web application that provides textual, numerical and geometrical search functionality for protein–ligand binding sites derived from structural data contained in the Protein Data Bank (PDB). The query generation is supported by a 3D representation of a start structure that provides interactively selectable elements like atoms, bonds and interactions. GeoMine gives full control over geometric variability in the query while performing a deterministic, precise search. Reasonably selective queries are processed on the entire set of protein–ligand complexes in the PDB within a few minutes. GeoMine offers an interactive and iterative search process of successive result analyses and query adaptations. From the numerous potential applications, we picked two from the field of side-effect analyze showcasing the usefulness of GeoMine. Availability and implementation GeoMine is part of the ProteinsPlus web application suite and freely available at https://proteins.plus. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PDBeCIF: an open-source mmCIF/CIF parsing and processing package

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Glen van Ginkel ◽  
Lukáš Pravda ◽  
José M. Dana ◽  
Mihaly Varadi ◽  
Peter Keller ◽  
...  
Keyword(s):  
Open Source ◽  
Protein Data Bank ◽  
Hybrid Methods ◽  
Structural Data ◽  
Data Bank ◽  
File Format ◽  
Macromolecular Complexes ◽  
Link Type ◽  
Information File ◽  
Multiscale Structures

Abstract Background Biomacromolecular structural data outgrew the legacy Protein Data Bank (PDB) format which the scientific community relied on for decades, yet the use of its successor PDBx/Macromolecular Crystallographic Information File format (PDBx/mmCIF) is still not widespread. Perhaps one of the reasons is the availability of easy to use tools that only support the legacy format, but also the inherent difficulties of processing mmCIF files correctly, given the number of edge cases that make efficient parsing problematic. Nevertheless, to fully exploit macromolecular structure data and their associated annotations such as multiscale structures from integrative/hybrid methods or large macromolecular complexes determined using traditional methods, it is necessary to fully adopt the new format as soon as possible. Results To this end, we developed PDBeCIF, an open-source Python project for manipulating mmCIF and CIF files. It is part of the official list of mmCIF parsers recorded by the wwPDB and is heavily employed in the processes of the Protein Data Bank in Europe. The package is freely available both from the PyPI repository (http://pypi.org/project/pdbecif) and from GitHub (https://github.com/pdbeurope/pdbecif) along with rich documentation and many ready-to-use examples. Conclusions PDBeCIF is an efficient and lightweight Python 2.6+/3+ package with no external dependencies. It can be readily integrated with 3rd party libraries as well as adopted for broad scientific analyses.

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