Dataset Information

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

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 ? 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.

SUBMITTER: Mariani V

PROVIDER: S-EPMC3799472 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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

Mariani Valerio V Biasini Marco M Barbato Alessandro A Schwede Torsten T

Bioinformatics (Oxford, England) 20130827 21

<h4>Motivation</h4>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.<h4>Results</h4>The Local Distance Difference Test (lDDT) is a superposition-free score tha ...[more]

PMID: 23986568

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Project description:BACKGROUND: Protein structure comparison is a central issue in structural bioinformatics. The standard dissimilarity measure for protein structures is the root mean square deviation (RMSD) of representative atom positions such as alpha-carbons. To evaluate the RMSD the structures under comparison must be superimposed optimally so as to minimize the RMSD. How to evaluate optimal fits becomes a matter of debate, if the structures contain regions which differ largely--a situation encountered in NMR ensembles and proteins undergoing large-scale conformational transitions. RESULTS: We present a probabilistic method for robust superposition and comparison of protein structures. Our method aims to identify the largest structurally invariant core. To do so, we model non-rigid displacements in protein structures with outlier-tolerant probability distributions. These distributions exhibit heavier tails than the Gaussian distribution underlying standard RMSD minimization and thus accommodate highly divergent structural regions. The drawback is that under a heavy-tailed model analytical expressions for the optimal superposition no longer exist. To circumvent this problem we work with a scale mixture representation, which implies a weighted RMSD. We develop two iterative procedures, an Expectation Maximization algorithm and a Gibbs sampler, to estimate the local weights, the optimal superposition, and the parameters of the heavy-tailed distribution. Applications demonstrate that heavy-tailed models capture differences between structures undergoing substantial conformational changes and can be used to assess the precision of NMR structures. By comparing Bayes factors we can automatically choose the most adequate model. Therefore our method is parameter-free. CONCLUSIONS: Heavy-tailed distributions are well-suited to describe large-scale conformational differences in protein structures. A scale mixture representation facilitates the fitting of these distributions and enables outlier-tolerant superposition.

Project description:BACKGROUND: The automation of objectively selecting amino acid residue ranges for structure superpositions is important for meaningful and consistent protein structure analyses. So far there is no widely-used standard for choosing these residue ranges for experimentally determined protein structures, where the manual selection of residue ranges or the use of suboptimal criteria remain commonplace. RESULTS: We present an automated and objective method for finding amino acid residue ranges for the superposition and analysis of protein structures, in particular for structure bundles resulting from NMR structure calculations. The method is implemented in an algorithm, CYRANGE, that yields, without protein-specific parameter adjustment, appropriate residue ranges in most commonly occurring situations, including low-precision structure bundles, multi-domain proteins, symmetric multimers, and protein complexes. Residue ranges are chosen to comprise as many residues of a protein domain that increasing their number would lead to a steep rise in the RMSD value. Residue ranges are determined by first clustering residues into domains based on the distance variance matrix, and then refining for each domain the initial choice of residues by excluding residues one by one until the relative decrease of the RMSD value becomes insignificant. A penalty for the opening of gaps favours contiguous residue ranges in order to obtain a result that is as simple as possible, but not simpler. Results are given for a set of 37 proteins and compared with those of commonly used protein structure validation packages. We also provide residue ranges for 6351 NMR structures in the Protein Data Bank. CONCLUSIONS: The CYRANGE method is capable of automatically determining residue ranges for the superposition of protein structure bundles for a large variety of protein structures. The method correctly identifies ordered regions. Global structure superpositions based on the CYRANGE residue ranges allow a clear presentation of the structure, and unnecessary small gaps within the selected ranges are absent. In the majority of cases, the residue ranges from CYRANGE contain fewer gaps and cover considerably larger parts of the sequence than those from other methods without significantly increasing the RMSD values. CYRANGE thus provides an objective and automatic method for standardizing the choice of residue ranges for the superposition of protein structures.

Dataset Information

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

Publications

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

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