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

TLDR: The lDDT is a superposition-free score that evaluates local distance differences of all atoms in a model, including validation of stereochemical plausibility, which makes it a robust tool for the automated assessment of structure prediction servers without manual intervention.

Abstract: 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 Cα atoms are strongly influenced by domain motions and do not assess the accuracy of local atomic details in the model.; The local Distance Difference Test (lDDT) is a superposition-free score which evaluates local distance differences of all atoms in a model, including validation of stereo-chemical 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 presence of domain movements, while maintaining good correlation to 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.