- Oral presentation
- Open Access
Combining pharmacophore- and MD-based modelling for phase II metabolism prediction
Journal of Cheminformatics volume 6, Article number: O15 (2014)
As metabolism is considered a main cause for adverse drug reactions and failures of new drug candidates, our goal is to establish an in silico method to efficiently predict phase II metabolism – in particular sulfotransferase (SULT) activity. Since sulfotransferases exhibit low substrate specificities caused by their high degree of conformational freedom , activity prediction is a challenging task.
We therefore established a workflow based on molecular dynamics (MD) simulations to cover the whole spectrum of structural flexibility and incorporated it into multiple pharmacophores that represent specific modes of action. Using an ensemble of pharmacophores for virtual screening ensures accurate categorization of potential SULT ligands (e.g. substrates, inhibitors). Recent advances in MD technology  allowed for refinement of these pharmacophores by high-throughput MD simulations of ligand-target complexes. In addition, the initial binding process of a soluble ligand to the substrate-binding site of SULT was captured in unbiased 100 ns simulations using the software Desmond .
Dong D, Ako R, Wu B: Crystal structures of human sulfotransferases: insights into the mechanisms of action and substrate selectivity. Expert Opin Drug Metab Toxicol. 2012, 8: 635-646. 10.1517/17425255.2012.677027.
Dror RO, et al: Biomolecular simulation: a computational microscope for molecular biology. Annu Rev Biophys. 2012, 41: 429-452. 10.1146/annurev-biophys-042910-155245.
Bowers KJ, et al: Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters. SC 2006 Conference, Proceedings of the ACM/IEEE. 2006
About this article
Cite this article
Rakers, C., Wolber, G. Combining pharmacophore- and MD-based modelling for phase II metabolism prediction. J Cheminform 6, O15 (2014). https://doi.org/10.1186/1758-2946-6-S1-O15
- Molecular Dynamic
- Molecular Dynamic Simulation
- Adverse Drug Reaction
- Drug Candidate
- Virtual Screening