Validation information in the Protein Data Bank: What is it and why should you care?

SMART, Oliver S., Vladimír HORSKÝ, Swanand GORE, Radka SVOBODOVÁ VAŘEKOVÁ, Veronika BENDOVÁ, Gerard J. KLEYWEGT and Sameer VELANKAR. Validation information in the Protein Data Bank: What is it and why should you care? In ENBIK 2018. 2018. ISBN 978-80-7592-017-1.

Basic information

• Original name: Validation information in the Protein Data Bank: What is it and why should you care?
• Authors: SMART, Oliver S. (826 United Kingdom of Great Britain and Northern Ireland), Vladimír HORSKÝ (203 Czech Republic, guarantor, belonging to the institution), Swanand GORE (826 United Kingdom of Great Britain and Northern Ireland), Radka SVOBODOVÁ VAŘEKOVÁ (203 Czech Republic, belonging to the institution), Veronika BENDOVÁ (203 Czech Republic, belonging to the institution), Gerard J. KLEYWEGT (528 Netherlands) and Sameer VELANKAR (826 United Kingdom of Great Britain and Northern Ireland).
• Edition: ENBIK 2018, 2018.

Other information

• Original language: English
• Type of outcome: Presentations at conferences
• Field of Study: 10608 Biochemistry and molecular biology
• Country of publisher: Czech Republic
• Confidentiality degree: is not subject to a state or trade secret
• RIV identification code: RIV/00216224:14310/18:00103001
• Organization unit: Faculty of Science
• ISBN: 978-80-7592-017-1
• Keywords in English: PDB; Protein Data Bank; three-dimensional macromolecular structure; validation; validation metrics; density fit; visualization; ligands; ValTrendsDB

Abstract

Widespread availability of biomacromolecular structural data has accelerated the progress of research in various life sciences. As an example of this paradigm shift, computer-assisted studies of ligands bound to active sites of proteins and nucleic acids became possible, which in turn aided structure-guided drug discovery and design. Published structures are stored in many databases that have emerged over time, the largest one being the Protein Data Bank (PDB). Concerns regarding quality of available structures have gone hand-in-hand with broad structure production and usage. Curators of the PDB database have reacted by developing the PDB validation pipeline. Here, we present the available validation metrics and show how their values can be combined into a single score that can be used to rank macromolecular structures and their domains in search results. A major challenge that accompanies crystallographic experiments is how to correctly interpret electron density at binding sites. Incorrect solution of this ambiguity is one of the reasons why quality of ligands in complexes in the PDB is a concerning matter. Therefore, it comes as no surprise that several ligand validation methods are part of the PDB validation pipeline. Here, we describe these methods. Furthermore, we discuss that the currently used LLDF metric can give misleading results.

Links

• MUNI/A/1204/2017, interní kód MU: Name: Matematické statistické modelování 2 (Acronym: MaStaMo2)

Investor: Masaryk University, Category A