Description
In the second part of the structure-based drug design lectures, we will first focus on high-throughput virtual screening techniques using 3D pharmacophore interaction models. This technique allows for focusing on the most relevant binding features necessary for a small molecular ligand to bind to a protein. Together with the pre-calculation of potential ligand conformations, virtual screening can be performed at lower computational cost when compared to classical docking. In the absence of an experimentally determined crystal structure, 3D pharmacophores can even be used to extrapolate potential protein interaction points and used for virtual screening in a similar manner.1
We will then talk about purpose and limitations of molecular dynamics (MD) simulations2,3 in drug design: This technique can be used to sample different ligand conformations to partially sample protein flexibility up to a certain point. MD trajectories can be used to develop statistically enhanced interaction patterns (so-called ‘dynophores’) and help to better understand protein-ligand binding.
To illustrate the applicability of the presented algorithms, several success stories for predicting and understanding protein-ligand binding will be presented. These include the inhibition of sulfo- transferase 1E1 to avoid toxicity,4 development of novel molecules to modulate innate immunity through Toll-like receptors5,6 and explaining dual- and allosteric binding of ligands to the muscarinic acetylcholine receptor including their impact on biological function.7
References
1 Wolber, G., Seidel, T., Bendix, F. & Langer, T. Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today 13, 23-29, doi:Doi 10.1016/J.Drudis.2007.09.007 (2008).
2 Rakers, C., Bermudez, M., Keller, B. G., Mortier, J. & Wolber, G. Computational close up on protein–protein interactions: how to unravel the invisible using molecular dynamics simulations? WIREs Comput Mol Sci 5, 345- 359, doi:10.1002/wcms.1222 (2015).
3 Mortier, J., Rakers, C., Bermudez, M., Murgueitio, M. S., Riniker, S. & Wolber, G. The impact of molecular dynamics on drug design: applications for the characterization of ligand-macromolecule complexes. Drug Discov Today 20, 686-702, doi:10.1016/j.drudis.2015.01.003 (2015).
4 Rakers, C., Schumacher, F., Meinl, W., Glatt, H., Kleuser, B. & Wolber, G. In silico prediction of human sulfotransferase 1E1 activity guided by pharmacophores from molecular dynamics simulations. Journal of Biological Chemistry 291, 58-71, doi:10.1074/jbc.M115.685610 (2016).
5 Bock, S., Murgueitio, M. S., Wolber, G. & Weindl, G. Acute myeloid leukaemia-derived Langerhans-like cells enhance Th1 polarization upon TLR2 engagement. Pharmacological Research 105, 44-53, doi:10.1016/j.phrs.2016.01.016 (2016).
6 Murgueitio, M. S., Henneke, P., Glossmann, H., Santos-Sierra, S. & Wolber, G. Prospective Virtual Screening in a Sparse Data Scenario: Design of Small-Molecule TLR2 Antagonists. Chemmedchem 9, 813-822, doi:Doi 10.1002/Cmdc.201300445 (2014).
7 Schmitz, J., van der Mey, D., Bermudez, M., Klöckner, J., Schrage, R., Kostenis, E., Tränkle, C., Wolber, G., Mohr, K.
& Holzgrabe, U. Dualsteric Muscarinic Antagonists - Orthosteric Binding Pose Controls Allosteric Subtype Selectivity. J Med Chem 57, 6739-6750, doi:10.1021/jm500790x (2014).
