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Validation of ligands in macromolecular structures determined by X-ray crystallography

Crystallographic studies of ligands bound to biological macromolecules (proteins and nucleic acids) play a crucial role in structure-guided drug discovery and design, and also provide atomic level insights into the physical chemistry of complex formation between macromolecules and ligands. The quali...

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Detalles Bibliográficos
Autores principales: Smart, Oliver S., Horský, Vladimír, Gore, Swanand, Svobodová Vařeková, Radka, Bendová, Veronika, Kleywegt, Gerard J., Velankar, Sameer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5947763/
https://www.ncbi.nlm.nih.gov/pubmed/29533230
http://dx.doi.org/10.1107/S2059798318002541
Descripción
Sumario:Crystallographic studies of ligands bound to biological macromolecules (proteins and nucleic acids) play a crucial role in structure-guided drug discovery and design, and also provide atomic level insights into the physical chemistry of complex formation between macromolecules and ligands. The quality with which small-molecule ligands have been modelled in Protein Data Bank (PDB) entries has been, and continues to be, a matter of concern for many investigators. Correctly interpreting whether electron density found in a binding site is compatible with the soaked or co-crystallized ligand or represents water or buffer molecules is often far from trivial. The Worldwide PDB validation report (VR) provides a mechanism to highlight any major issues concerning the quality of the data and the model at the time of deposition and annotation, so the depositors can fix issues, resulting in improved data quality. The ligand-validation methods used in the generation of the current VRs are described in detail, including an examination of the metrics to assess both geometry and electron-density fit. It is found that the LLDF score currently used to identify ligand electron-density fit outliers can give misleading results and that better ligand-validation metrics are required.