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.

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

Language

English

Type of outcome

Prezentace na konferencích

Field of Study

10608 Biochemistry and molecular biology

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

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

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

Abstract

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.

Links

MUNI/A/1204/2017, interní kód MU

Name: Matematické statistické modelování 2 (Acronym: MaStaMo2)

Investor: Masaryk University, Category A

Citovat

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.

@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  -

Detailed Information on Publication Record