lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests

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ID: 298187
2013
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Abstract
Abstract 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.
Reference Key
openalex_W2140673705 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Valerio Mariani, Marco Biasini, Alessandro Barbato, Torsten Schwede
Journal BMC Bioinformatics
Year 2013
DOI
10.1093/bioinformatics/btt473
URL
https://academic.oup.com/bioinformatics/article-pdf/29/21/2722/18533347/btt473.pdf
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