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

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.

[1]  Randy J Read,et al.  Domain definition and target classification for CASP7 , 2007, Proteins.

[2]  Binchen Mao,et al.  Improved technologies now routinely provide protein NMR structures useful for molecular replacement. , 2011, Structure.

[3]  Andrew C. R. Martin,et al.  Assessment of comparative modeling in CASP2 , 1997, Proteins.

[4]  Christopher J. Williams,et al.  The other 90% of the protein: Assessment beyond the Cαs for CASP8 template‐based and high‐accuracy models , 2009, Proteins.

[5]  Zheng Yang,et al.  FlexE: Using elastic network models to compare models of protein structure. , 2012, Journal of chemical theory and computation.

[6]  R. Huber,et al.  Accurate Bond and Angle Parameters for X-ray Protein Structure Refinement , 1991 .

[7]  Antonio Rosato,et al.  RPF: a quality assessment tool for protein NMR structures , 2012, Nucleic Acids Res..

[8]  Vincent B. Chen,et al.  Correspondence e-mail: , 2000 .

[9]  M. Volkenstein,et al.  Statistical mechanics of chain molecules , 1969 .

[10]  Torsten Schwede,et al.  Assessment of template based protein structure predictions in CASP9 , 2011, Proteins.

[11]  Alexandre M J J Bonvin,et al.  Clustering biomolecular complexes by residue contacts similarity , 2012, Proteins.

[12]  T. Alwyn Jones,et al.  CASP3 comparative modeling evaluation , 1999, Proteins.

[13]  Adam Zemla,et al.  LGA: a method for finding 3D similarities in protein structures , 2003, Nucleic Acids Res..

[14]  Torsten Schwede,et al.  Assessment of CASP7 predictions for template‐based modeling targets , 2007, Proteins.

[15]  T. Hahn.  International tables for crystallography , 2002 .

[16]  N. Grishin,et al.  CASP9 target classification , 2011, Proteins.

[17]  Krzysztof Fidelis,et al.  CASP prediction center infrastructure and evaluation measures in CASP10 and CASP ROLL , 2014, Proteins.

[18]  M. James,et al.  A critical assessment of comparative molecular modeling of tertiary structures of proteins * , 1995, Proteins.

[19]  Robert Huber,et al.  Structure quality and target parameters , 2006 .

[20]  Roland L. Dunbrack,et al.  proteins STRUCTURE O FUNCTION O BIOINFORMATICS Improved prediction of protein side-chain conformations with SCWRL4 , 2022 .

[21]  I. Kanter,et al.  STATISTICAL PROPERTIES OF CONTACT MAPS , 1998, cond-mat/9810285.

[22]  Krzysztof Fidelis,et al.  Processing and evaluation of predictions in CASP4 , 2001, Proteins.

[23]  T J Hubbard.  RMS/Coverage graphs: A qualitative method for comparing three‐dimensional protein structure predictions , 1999, Proteins.

[24]  Matthew P Jacobson,et al.  Assessment of protein structure refinement in CASP9 , 2011, Proteins.

[25]  Arne Elofsson,et al.  MaxSub: an automated measure for the assessment of protein structure prediction quality , 2000, Bioinform..

[26]  F. Allen.  The Cambridge Structural Database: a quarter of a million crystal structures and rising. , 2002, Acta crystallographica. Section B, Structural science.

[27]  Alessandro Pandini,et al.  Predicting the accuracy of protein–ligand docking on homology models , 2011, J. Comput. Chem..

[28]  Randy J. Read,et al.  A New Generation of Crystallographic Validation Tools for the Protein Data Bank , 2011, Structure.

[29]  Anna Tramontano,et al.  Critical assessment of methods of protein structure prediction (CASP) — round x , 2014, Proteins.

[30]  Manfred J. Sippl.  On distance and similarity in fold space , 2008, Bioinform..

[31]  Roland L Dunbrack,et al.  Outcome of a workshop on applications of protein models in biomedical research. , 2009, Structure.

[32]  Ian Sillitoe,et al.  Extending CATH: increasing coverage of the protein structure universe and linking structure with function , 2010, Nucleic Acids Res..

[33]  Torsten Schwede,et al.  Automated comparative protein structure modeling with SWISS‐MODEL and Swiss‐PdbViewer: A historical perspective , 2009, Electrophoresis.

[34]  Randy J Read,et al.  Automated server predictions in CASP7 , 2007, Proteins.

[35]  Maurice Loyal Huggins.  Physical chemistry of high polymers , 1958 .

[36]  Jimin Pei,et al.  Analysis of CASP8 targets, predictions and assessment methods , 2009, Database J. Biol. Databases Curation.

[37]  Yang Zhang,et al.  Scoring function for automated assessment of protein structure template quality , 2004, Proteins.

[38]  Marco Biasini,et al.  OpenStructure: a flexible software framework for computational structural biology , 2010, Bioinform..

[39]  A. Tramontano,et al.  Critical assessment of methods of protein structure prediction (CASP)—round IX , 2011, Proteins.

[40]  John Moult,et al.  A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. , 2005, Current opinion in structural biology.

[41]  Kliment Olechnovič,et al.  CAD‐score: A new contact area difference‐based function for evaluation of protein structural models , 2013, Proteins.