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J Chem Inf Model. 2017 Mar 27;57(3):529-539. doi: 10.1021/acs.jcim.6b00613. Epub 2017 Feb 16.

High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators.

Author information

1
University of Hamburg , ZBH - Center for Bioinformatics, Bundesstra├če 43, Hamburg 20146, Germany.

Abstract

We developed a cheminformatics pipeline for the fully automated selection and extraction of high-quality protein-bound ligand conformations from X-ray structural data. The pipeline evaluates the validity and accuracy of the 3D structures of small molecules according to multiple criteria, including their fit to the electron density and their physicochemical and structural properties. Using this approach, we compiled two high-quality datasets from the Protein Data Bank (PDB): a comprehensive dataset and a diversified subset of 4626 and 2912 structures, respectively. The datasets were applied to benchmarking seven freely available conformer ensemble generators: Balloon (two different algorithms), the RDKit standard conformer ensemble generator, the Experimental-Torsion basic Knowledge Distance Geometry (ETKDG) algorithm, Confab, Frog2 and Multiconf-DOCK. Substantial differences in the performance of the individual algorithms were observed, with RDKit and ETKDG generally achieving a favorable balance of accuracy, ensemble size and runtime. The Platinum datasets are available for download from http://www.zbh.uni-hamburg.de/platinum_dataset .

PMID:
28206754
DOI:
10.1021/acs.jcim.6b00613
[Indexed for MEDLINE]

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