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

k 2018

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 et. al.

Základní údaje

Originální název

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

Autoři

SMART, Oliver S. (826 Velká Británie a Severní Irsko), Vladimír HORSKÝ (203 Česká republika, garant, domácí), Swanand GORE (826 Velká Británie a Severní Irsko), Radka SVOBODOVÁ VAŘEKOVÁ (203 Česká republika, domácí), Veronika BENDOVÁ (203 Česká republika, domácí), Gerard J. KLEYWEGT (528 Nizozemské království) a Sameer VELANKAR (826 Velká Británie a Severní Irsko)

Vydání

ENBIK 2018, 2018

Další údaje

Jazyk

angličtina

Typ výsledku

Prezentace na konferencích

Obor

10608 Biochemistry and molecular biology

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Kód RIV

RIV/00216224:14310/18:00103001

Organizační jednotka

Přírodovědecká fakulta

ISBN

978-80-7592-017-1

Klíčová slova anglicky

PDB; Protein Data Bank; three-dimensional macromolecular structure; validation; validation metrics; density fit; visualization; ligands; ValTrendsDB

Změněno: 12. 6. 2018 18:56, Mgr. Vladimír Horský, Ph.D.

Anotace

V originále

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.

Návaznosti

MUNI/A/1204/2017, interní kód MU

Název: Matematické statistické modelování 2 (Akronym: MaStaMo2)

Investor: Masarykova univerzita, Matematické statistické modelování 2, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

SMART, Oliver S., Vladimír HORSKÝ, Swanand GORE, Radka SVOBODOVÁ VAŘEKOVÁ, Veronika BENDOVÁ, Gerard J. KLEYWEGT a 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.

@proceedings{1420656,
   author = {Smart, Oliver S. and Horský, Vladimír and Gore, Swanand and Svobodová Vařeková, Radka and Bendová, Veronika and Kleywegt, Gerard J. and Velankar, Sameer},
   booktitle = {ENBIK 2018},
   keywords = {PDB; Protein Data Bank; three-dimensional macromolecular structure; validation; validation metrics; density fit; visualization; ligands; ValTrendsDB},
   language = {eng},
   isbn = {978-80-7592-017-1},
   title = {Validation information in the Protein Data Bank: What is it and why should you care?},
   year = {2018}
}

TY  - CONF
ID  - 1420656
AU  - Smart, Oliver S. - Horský, Vladimír - Gore, Swanand - Svobodová Vařeková, Radka - Bendová, Veronika - Kleywegt, Gerard J. - Velankar, Sameer
PY  - 2018
TI  - Validation information in the Protein Data Bank: What is it and why should you care?
SN  - 9788075920171
KW  - PDB
KW  - Protein Data Bank
KW  - three-dimensional macromolecular structure
KW  - validation
KW  - validation metrics
KW  - density fit
KW  - visualization
KW  - ligands
KW  - ValTrendsDB
N2  - 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.
ER  -

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