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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F18%3A00103001" target="_blank" >RIV/00216224:14310/18:00103001 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10608 - Biochemistry and molecular biology

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů