We have developed DockScreen, a database of in silico biomolecular interactions designed to enable rational molecular toxicological insight within a computational toxicology framework. This database is composed of chemical/target (receptor and enzyme) binding scores calculated by molecular docking of more than 1000 chemicals into 150 protein targets and contains nearly 135 thousand unique ligand/target binding scores. Obtaining this dataset was achieved using eHiTS (Simbiosys Inc.), a fragment-based molecular docking approach with an exhaustive search algorithm, on a heterogeneous distributed high-performance computing framework. The chemical landscape covered in DockScreen comprises selected environmental and therapeutic chemicals. The target landscape covered in DockScreen was selected based on the availability of high-quality crystal structures that covered the assay space of phase I ToxCast in vitro assays. This in silico data provides continuous information that establishes a means for quantitatively comparing, on a structural biophysical basis, a chemical’s profile of biomolecular interactions. The combined minimum-score chemical/target matrix is provided. 1. Introduction A major challenge with chemicals in consumer products, including but not limited to both pharmaceutical and environmental chemicals, is the ability to fully discover, characterize, and anticipate adverse effects that may result as a consequence of exposure to these chemicals. Classical safety assessment and animal studies are not only cost-prohibitive and lengthy [1, 2]; they often do not include the data required for extrapolations that are inherent in human risk assessment [3]. Developing and evaluating predictive strategies to elucidate the mode of biological activity of environmental chemicals are major undertaking of the US Environmental Protection Agency’s Computational Toxicology program (http://www.epa.gov/comptox/). Aligning these strategies with the Agency’s ongoing chemical-specific risk assessment needs provision of additional incentive to develop new means of elucidating key determinants of toxicity in the chemical source-to-outcome continuum at a molecular level of accountability. This has provided the motivation for the development of tools such as the Aggregated Computational Toxicology Resource (http://www.epa.gov/actor) [1], the DSSTox Toxico-Chemoinformatics initiative (http://www.epa.gov/ncct/dsstox/) [4], and the ToxRefDB (http://www.epa.gov/ncct/toxrefdb/) [5] in vivo animal effects database. In an attempt to fill the inherently large data gaps required for
References
[1]
R. Judson, A. Richard, D. Dix et al., “ACToR—aggregated computational toxicology resource,” Toxicology and Applied Pharmacology, vol. 233, no. 1, pp. 7–13, 2008.
[2]
R. Judson, A. Richard, D. J. Dix et al., “The toxicity data landscape for environmental chemicals,” Environmental Health Perspectives, vol. 117, no. 5, pp. 685–695, 2009.
[3]
J. R. Rabinowitz, M.-R. Goldsmith, S. B. Little, and M. A. Pasquinelli, “Computational molecular modeling for evaluating the toxicity of environmental chemicals: prioritizing bioassay requirements,” Environmental Health Perspectives, vol. 116, no. 5, pp. 573–577, 2008.
[4]
A. M. Richard and C. R. Williams, “Distributed structure-searchable toxicity (DSSTox) public database network: a proposal,” Mutation Research, vol. 499, pp. 27–52, 2002.
[5]
M. T. Martin, R. S. Judson, D. M. Reif, R. J. Kavlock, and D. J. Dix, “Profiling chemicals based on chronic toxicity results from the U.S. EPA ToxRef database,” Environmental Health Perspectives, vol. 117, no. 3, pp. 392–399, 2009.
[6]
National Research Council, Toxicity Testing in the 21st Century: A Vision and a Strategy, The National Academies Press, Washington, DC, USA, 2007.
[7]
D. J. Dix, K. A. Houck, M. T. Martin, A. M. Richard, R. W. Setzer, and R. J. Kavlock, “The toxcast program for prioritizing toxicity testing of environmental chemicals,” Toxicological Sciences, vol. 95, no. 1, pp. 5–12, 2007.
[8]
J. Bajorath, “Integration of virtual and high-throughput screening,” Nature Reviews Drug Discovery, vol. 1, no. 11, pp. 882–894, 2002.
[9]
D. C. Fara, T. I. Oprea, E. R. Prossnitz, C. G. Bologa, B. S. Edwards, and L. A. Sklar, “Integration of virtual and physical screening,” Drug Discovery Today: Technologies, vol. 3, no. 4, pp. 377–385, 2006.
[10]
G. V. Paolini, R. H. Shapland, W. P. van Hoorn, J. S. Mason, and A. L. Hopkins, “Global mapping of pharmacological space,” Nature Biotechnology, vol. 24, no. 7, pp. 805–815, 2006.
[11]
á. Peragovics, Z. Simon, L. Tombor, et al., “Virtual affinity fingerprints for target fishing: a new application of drug profile matching,” Journal of Chemical Information and Modeling, vol. 53, no. 1, pp. 103–113, 2013.
[12]
D. Hristozov, T. I. Oprea, and J. Gasteiger, “Ligand-based virtual screening by novelty detection with self-organizing maps,” Journal of Chemical Information and Modeling, vol. 47, no. 6, pp. 2044–2062, 2007.
[13]
E. J. Matthews, N. L. Kruhlak, R. D. Benz, and J. F. Contrera, “Assessment of the health effects of chemicals in humans: I. QSAR estimation of the maximum recommended therapeutic dose (MRTD) and no effect level (NOEL) of organic chemicals based on clinical trial data,” Current Drug Discovery Technologies, vol. 1, no. 1, pp. 61–76, 2004.
[14]
FLIPPER, “Openeye Scientific Software,” Santa Fe, NM, USA, http://www.eyesopen.com/.
[15]
A. J. S. Knox, M. J. Meegan, G. Carta, and D. G. J. Lloyd, “Considerations in compound database preparation “hidden” impact on virtual screening results,” Journal of Chemical Information and Modeling, vol. 45, no. 6, pp. 1908–1919, 2005.
[16]
G. G. Briggs, R. K. Freeman, and S. J. Yaffe, Drugs in Pregnancy and Lactation: a Reference Guide to Fetal and Neonatal Risk, Lippincott Williams & Wilkins, 2012.
[17]
Chemical Computing Group, Molecular Operating Environment (MOE) v2006.08, Chemical Computing Group, Quebec, Canada, 2006.
[18]
T. A. Halgren, “Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94,” Journal of Computational Chemistry, vol. 17, no. 5-6, pp. 490–519, 1996.
[19]
P. Emsley and K. Cowtan, “Coot: model-building tools for molecular graphics,” Acta Crystallographica Section D: Biological Crystallography, vol. 60, pp. 2126–2132, 2004.
[20]
eHITS v. 6.1, Simbiosys Inc. Toronto Canada.
[21]
Z. Zsoldos, D. Reid, A. Simon, B. S. Sadjad, and A. P. Johnson, “eHiTS: an innovative approach to the docking and scoring function problems,” Current Protein and Peptide Science, vol. 7, no. 5, pp. 421–435, 2006.
[22]
Z. Zsoldos, D. Reid, A. Simon, S. B. Sadjad, and A. P. Johnson, “eHiTS: a new fast, exhaustive flexible ligand docking system,” Journal of Molecular Graphics and Modelling, vol. 26, no. 1, pp. 198–212, 2007.
[23]
D. Plewczynski, M. ？a？niewski, R. Augustyniak, and K. Ginalski, “Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database,” Journal of Computational Chemistry, vol. 32, no. 4, pp. 742–755, 2011.
[24]
Small-Molecule Drug Discovery Suite 2014-1: QikProp , version 3.9, Schr？dinger, LLC, New York, NY, USA, 2014.
[25]
W. L. Jorgensen, D. S. Maxwell, and J. Tirado-Rives, “Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids,” Journal of the American Chemical Society, vol. 118, no. 45, pp. 11225–11236, 1996.
[26]
M. R. Goldsmith, S. D. Peterson, D. T. Chang et al., “Informing mechanistic toxicology with computational molecular models,” in Computational Toxicology, pp. 139–165, Humana Press, 2012.
[27]
M. R. Goldsmith, D. T. Chang, J. R. Rabinowitz, S. B. Little, and R. R. Tice, To Hit, or Not to Hit?: In Silico Models of In Vitro Nuclear Receptor Transactivation, National Toxicology Board of Scientific Counselors, 2010.
