Title
High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators.
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.
Year
DOI
Venue
2017
10.1021/acs.jcim.6b00613
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Field
DocType
Volume
Data mining,Multiple criteria,Conformational isomerism,Chemistry,Bioinformatics,Distance geometry,Protein Data Bank,Protein Data Bank (RCSB PDB),Cheminformatics,Benchmarking
Journal
57
Issue
ISSN
Citations 
3
1549-9596
3
PageRank 
References 
Authors
0.66
27
7