ArticleBioinformatics (Oxford, England)2013
lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 675 papers, 1 of them a synthesis that pooled it.
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675 citing papers in PubMed, 1 synthesis or guideline pooled it.
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615 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
motivationThe 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.
resultsThe 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.
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