A new test set for validating predictions of protein–ligand interaction

By J. Willem M. Nissink, Christopher W. Murray, M. Hartshorn, Marcel L. Verdonk, Jason C. Cole, Robin Taylor. Proteins: Structure, Function, and Bioinformatics, 2002

415 citations recorded by the source corpus.

We present a large test set of protein–ligand complexes for the purpose of validating algorithms that rely on the prediction of protein–ligand interactions. The set consists of 305 complexes with protonation states assigned by manual inspection. The following checks have been carried out to identify unsuitable entries in this set: (1) assessing the involvement of crystallographically related protein units in ligand binding; (2) identification of bad clashes between protein side chains and ligand; and (3) assessment of structural errors, and/or inconsistency of ligand placement with crystal structure electron density. In addition, the set has been pruned to assure diversity in terms of protein–ligand structures, and subsets are supplied for different protein‐structure resolution ranges. A classification of the set by protein type is available. As an illustration, validation results are shown for GOLD and SuperStar. GOLD is a program that performs flexible protein–ligand docking, and SuperStar is used for the prediction of favorable interaction sites in proteins. The new CCDC/Astex test set is freely available to the scientific community (http://www.ccdc.cam.ac.uk). Proteins 2002;49:457–471. © 2002 Wiley‐Liss, Inc.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/68b607655a8db47821ca694e7914e61633a5f0ac

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Citations in the corpus

Protein-ligand docking in the new millennium--a retrospective of 10 years in the field. (2013, methodology): “The performance of MolDock has been evaluated against 77 protein-ligand complexes from the GOLD dataset [198], resulting, in general, in higher average accuracies than Glide, Surflex, FlexX and GOLD.”
AffinDB: a freely accessible database of affinities for protein–ligand complexes from the PDB (2005, methodology): “Subsequently, the database was augmented by seeking affinity data for PDB complexes of different datasets, such as a docking test set of validated structures (27) or datasets for certain target classes (e.”
General and targeted statistical potentials for protein–ligand interactions (2005, methodology): “Different values for this cut off were tested, but in general the success rate in reproducing binding modes for the complexes in the Cambridge Crystallographic Data Centre (CCDC)/Astex validation set 31 proved to be insensitive to this.”
LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation (2006, background): “[19] presented a large test set of 305 ligand-bound protein complexes.”
Molecular complexes at a glance: automated generation of two-dimensional complex diagrams (2006, methodology): “The layout generation was tested on the CCDC Astex dataset (Nissink et al., 2002).”
PubChem3D: Conformer generation (2011, background): “This data is not without its considerable issues [6-10].”
Improved protein–ligand docking using GOLD (2003, methodology): “…84% on a test set of 70 complexes 8 ; FlexX gave a success rate of 47% on a test set of 200 complexes 9 ; and recently, GOLD was shown to give a 68% success rate on the CCDC/Astex validation set of 305 complexes 7 ; all complexes in all three test sets were taken from the Protein Data Bank (PDB).”
iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis (2011, methodology): “, CCDC/ Astex set [20] ) and ERA, ER, and TK with 1000 compounds as test sets.”