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[News]
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Can we trust docking results?
Sept 2010 IBM Systems and Technology Group releases a white paper with eHiTS and Cell
Oct 2008
EPA's ToxCastTM project will use SimBioSys' eHiTS as docking engine
Nov, 2007
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[Events]
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| 243rd ACS
Mar 25-29, 2012
San Diego, CA
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Boston, MA, USA Feb 25-27, 2009
Use of training techniques in eHiTS improves score-RMSD and score-IC50 correlations in in-silico high throughput screening.
Danni L. Harris, Zsolt Zsoldos
SimBioSys Inc., 135 Queen's Plate Dr, Unit 520, Toronto, ON M9W
6V1, Canada
Abstract:
Screening of compounds has become a prevalent step in a variety of
biologically related applications including drug discovery, predictive
metabolism and toxicity prediction. The docking paradigm consists of two
interrelated steps, namely pose prediction, and scoring. While most docking
approaches are capable of predicting poses consistent with known structural solutions,
they do not generally score these as the top ranking poses. Furthermore,
correlations between docking scores and low RMSD values or bioactivity given in
terms of lnKd or IC50 values are often limited (J. Med. Chem, 49:5912-5931).
Such correlations are crucial if docking is to play a reliable role in either
prioritizing prospective ligands for synthesis or in ranking protein-target
interactions in metabolic and toxicity studies. We describe in detail the mixed
physical and informatics approach of the scoring function of eHiTS (Electronic
High Throughput Screening), and demonstrate by comparing to quantum mechanical
results how it is well equipped to capture subtle interactions such as Pi-cation,
non-conventional hydrogen bonding, and Pi-stacking. Good score-based low RMSD
discrimination of biochemically and pharmacologically relevant poses as well as
score-IC50 correlations are shown with illustrations from several systems:
nicotinic acetylcholine receptors and their surrogate binding proteins (AChBP),
kinases, and cytochrome P450s. We demonstrate how these features may be further
enhanced using eHiTS' unique training utility, and discuss this as a method to
improve discrimination of ligand-target recognition.
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