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lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests
Motivation: The assessment of protein structure prediction techniques requires objective criteria to measure the similarity between a computational model and the experimentally determined reference structure. Conventional similarity measures based on a global superposition of carbon α atoms are stro...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3799472/ https://www.ncbi.nlm.nih.gov/pubmed/23986568 http://dx.doi.org/10.1093/bioinformatics/btt473 |
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author | Mariani, Valerio Biasini, Marco Barbato, Alessandro Schwede, Torsten |
author_facet | Mariani, Valerio Biasini, Marco Barbato, Alessandro Schwede, Torsten |
author_sort | Mariani, Valerio |
collection | PubMed |
description | Motivation: The assessment of protein structure prediction techniques requires objective criteria to measure the similarity between a computational model and the experimentally determined reference structure. Conventional similarity measures based on a global superposition of carbon α atoms are strongly influenced by domain motions and do not assess the accuracy of local atomic details in the model. Results: The Local Distance Difference Test (lDDT) is a superposition-free score that evaluates local distance differences of all atoms in a model, including validation of stereochemical plausibility. The reference can be a single structure, or an ensemble of equivalent structures. We demonstrate that lDDT is well suited to assess local model quality, even in the presence of domain movements, while maintaining good correlation with global measures. These properties make lDDT a robust tool for the automated assessment of structure prediction servers without manual intervention. Availability and implementation: Source code, binaries for Linux and MacOSX, and an interactive web server are available at http://swissmodel.expasy.org/lddt Contact: torsten.schwede@unibas.ch Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-3799472 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-37994722013-10-18 lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests Mariani, Valerio Biasini, Marco Barbato, Alessandro Schwede, Torsten Bioinformatics Original Papers Motivation: The assessment of protein structure prediction techniques requires objective criteria to measure the similarity between a computational model and the experimentally determined reference structure. Conventional similarity measures based on a global superposition of carbon α atoms are strongly influenced by domain motions and do not assess the accuracy of local atomic details in the model. Results: The Local Distance Difference Test (lDDT) is a superposition-free score that evaluates local distance differences of all atoms in a model, including validation of stereochemical plausibility. The reference can be a single structure, or an ensemble of equivalent structures. We demonstrate that lDDT is well suited to assess local model quality, even in the presence of domain movements, while maintaining good correlation with global measures. These properties make lDDT a robust tool for the automated assessment of structure prediction servers without manual intervention. Availability and implementation: Source code, binaries for Linux and MacOSX, and an interactive web server are available at http://swissmodel.expasy.org/lddt Contact: torsten.schwede@unibas.ch Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2013-11-01 2013-08-27 /pmc/articles/PMC3799472/ /pubmed/23986568 http://dx.doi.org/10.1093/bioinformatics/btt473 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Mariani, Valerio Biasini, Marco Barbato, Alessandro Schwede, Torsten lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests |
title | lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests |
title_full | lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests |
title_fullStr | lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests |
title_full_unstemmed | lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests |
title_short | lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests |
title_sort | lddt: a local superposition-free score for comparing protein structures and models using distance difference tests |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3799472/ https://www.ncbi.nlm.nih.gov/pubmed/23986568 http://dx.doi.org/10.1093/bioinformatics/btt473 |
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