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Ligand identification using electron-density map correlations

A procedure for the identification of ligands bound in crystal structures of macromolecules is described. Two characteristics of the density corresponding to a ligand are used in the identification procedure. One is the correlation of the ligand density with each of a set of test ligands after optim...

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Detalles Bibliográficos
Autores principales: Terwilliger, Thomas C., Adams, Paul D., Moriarty, Nigel W., Cohn, Judith D.
Formato: Texto
Lenguaje:English
Publicado: International Union of Crystallography 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2483487/
https://www.ncbi.nlm.nih.gov/pubmed/17164532
http://dx.doi.org/10.1107/S0907444906046233
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author Terwilliger, Thomas C.
Adams, Paul D.
Moriarty, Nigel W.
Cohn, Judith D.
author_facet Terwilliger, Thomas C.
Adams, Paul D.
Moriarty, Nigel W.
Cohn, Judith D.
author_sort Terwilliger, Thomas C.
collection PubMed
description A procedure for the identification of ligands bound in crystal structures of macromolecules is described. Two characteristics of the density corresponding to a ligand are used in the identification procedure. One is the correlation of the ligand density with each of a set of test ligands after optimization of the fit of that ligand to the density. The other is the correlation of a fingerprint of the density with the fingerprint of model density for each possible ligand. The fingerprints consist of an ordered list of correlations of each the test ligands with the density. The two characteristics are scored using a Z-score approach in which the correlations are normalized to the mean and standard deviation of correlations found for a variety of mismatched ligand-density pairs, so that the Z scores are related to the probability of observing a particular value of the correlation by chance. The procedure was tested with a set of 200 of the most commonly found ligands in the Protein Data Bank, collectively representing 57% of all ligands in the Protein Data Bank. Using a combination of these two characteristics of ligand density, ranked lists of ligand identifications were made for representative (F (o) − F (c))exp(iϕ(c)) difference density from entries in the Protein Data Bank. In 48% of the 200 cases, the correct ligand was at the top of the ranked list of ligands. This approach may be useful in identification of unknown ligands in new macromolecular structures as well as in the identification of which ligands in a mixture have bound to a macromolecule.
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spelling pubmed-24834872009-03-05 Ligand identification using electron-density map correlations Terwilliger, Thomas C. Adams, Paul D. Moriarty, Nigel W. Cohn, Judith D. Acta Crystallogr D Biol Crystallogr Research Papers A procedure for the identification of ligands bound in crystal structures of macromolecules is described. Two characteristics of the density corresponding to a ligand are used in the identification procedure. One is the correlation of the ligand density with each of a set of test ligands after optimization of the fit of that ligand to the density. The other is the correlation of a fingerprint of the density with the fingerprint of model density for each possible ligand. The fingerprints consist of an ordered list of correlations of each the test ligands with the density. The two characteristics are scored using a Z-score approach in which the correlations are normalized to the mean and standard deviation of correlations found for a variety of mismatched ligand-density pairs, so that the Z scores are related to the probability of observing a particular value of the correlation by chance. The procedure was tested with a set of 200 of the most commonly found ligands in the Protein Data Bank, collectively representing 57% of all ligands in the Protein Data Bank. Using a combination of these two characteristics of ligand density, ranked lists of ligand identifications were made for representative (F (o) − F (c))exp(iϕ(c)) difference density from entries in the Protein Data Bank. In 48% of the 200 cases, the correct ligand was at the top of the ranked list of ligands. This approach may be useful in identification of unknown ligands in new macromolecular structures as well as in the identification of which ligands in a mixture have bound to a macromolecule. International Union of Crystallography 2007-01-01 2006-12-13 /pmc/articles/PMC2483487/ /pubmed/17164532 http://dx.doi.org/10.1107/S0907444906046233 Text en © International Union of Crystallography 2007 http://journals.iucr.org/services/termsofuse.html This is an open-access article distributed under the terms described at http://journals.iucr.org/services/termsofuse.html.
spellingShingle Research Papers
Terwilliger, Thomas C.
Adams, Paul D.
Moriarty, Nigel W.
Cohn, Judith D.
Ligand identification using electron-density map correlations
title Ligand identification using electron-density map correlations
title_full Ligand identification using electron-density map correlations
title_fullStr Ligand identification using electron-density map correlations
title_full_unstemmed Ligand identification using electron-density map correlations
title_short Ligand identification using electron-density map correlations
title_sort ligand identification using electron-density map correlations
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2483487/
https://www.ncbi.nlm.nih.gov/pubmed/17164532
http://dx.doi.org/10.1107/S0907444906046233
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