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

By Valerio Mariani, Marco Biasini, Alessandro Barbato, Torsten Schwede. 2013

1,054 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Robust deep learning based protein sequence design using ProteinMPNN (2022, methodology): “3Å noise generated 2-3 times more sequences with AlphaFold predictions within lDDT-Ca (14) of 95.”
AFsample: improving multimer prediction with AlphaFold using massive sampling (2023, methodology): “AF2 estimates the per-residue accuracy using the predicted LDDT (pLDDT; Mariani et al. 2013).”
Assessment of three-dimensional RNA structure prediction in CASP15 (2023, positive): “INF scores were calculated from interaction predictions dependent on ClaRNA 34 ; (4) OpenStructure, 35,36 a framework used to find lDDT, a metric that measures structural similarity (unlike for proteins, our implementation of lDDT for RNA did not penalize for stereochemical violations); and (5)…”
Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022, positive): “…between the test set and our training data was substantially positively correlated with the performance of RhoFold+, as measured by the TM score and the local distance difference test (LDDT), a superposition-free score that evaluates local distance differences for all atoms in a model 32,33 .”
Sampling alternative conformational states of transporters and receptors with AlphaFold2 (2022, critical): “In contrast with a recent preprint ( Saldaño et al., 2021 ), the predicted flexibility values failed to correlate with their pLDDT values, which reflect the confidence of the AF2 prediction of each residue’s local environment ( Mariani et al., 2013 ).”
Improved protein structure refinement guided by deep learning based accuracy estimation (2020, background): “…three types of predictions given a protein structure model: local measures of structure accuracy measured by per residue C ᵦ local distance difference test (l-DDT) scores (13) , a native C ᵦ contact map thresholded at 15 Å (referred to as mask ), and per residue-pair distributions of signed C ᵦ -”
Introducing “best single template” models as reference baseline for the Continuous Automated Model Evaluation (CAMEO) (2019, methodology): “The “BaselinePotential” server implements a classical distance-based statistical potential.(9) Statistics have been extracted for pairwise distances between all chemically distinguishable heavy atoms in the 20 naturally occurring amino acids and histograms have been computed, neglecting all interact”
Hallucinating symmetric protein assemblies (2022, methodology): “The loss function guiding the search is computed by inputting N copies of the sequence into the AlphaFold2 (AF2) network (26), and combining structure prediction confidence metrics (pLDDT; per-residue structural accuracy (27), and pTM; an estimate of the TM-score (28)) with a measure of cyclic symme”
High‐accuracy protein structure prediction in CASP14 (2021, background): “…precision 3 ; (2) lDDT, a local difference distance test that evaluates how well the all-atom distance map of the target is reproduced by the model 17 ; (3) CADaa, which compares residues' contact surface areas 18 ; (4) Sphere-Grinder (SG), a measure of local environment conservation 19 ; and (5)…”
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models (2021, methodology): “This confidence measure is called pLDDT and corresponds to the model’s predicted per-residue scores on the lDDT-C metric (14).”