Assistant Professor of Pharmaceutical Sciences
B.S., Pharmacy, University of Athens, Greece
M.Sc., Medicinal Chemistry, University of Illinois, Medical Center, Chicago
Ph.D., Medicinal Chemistry/Computational Chemistry, University of North Carolina, Chapel Hill
Post-doctoral, Computational Biology, University of Washington, Seattle
Dr. Maria Kontoyianni is an Assistant Professor in Medicinal Chemistry, in the School of Pharmacy.
She holds a Ph.D. in computational chemistry from the University of North Carolina, Chapel Hill, where she worked under the supervision of Professor Phil Bowen. After a post-doctoral fellowship with Professor Terry Lybrand at the University of Washington, she joined ZymoGenetics, where she focused on ligand-based design and homology modeling. She then moved to Research & Development of Fortune 500 companies, such as Johnson & Johnson and Procter & Gamble, applying computational approaches to various therapeutic targets from hit identification to lead optimization. In her most recent post, she was the Head of Drug Discovery in a small biotechnology firm in Barcelona. She holds seven patents, is the author of several peer-reviewed publications, a consultant and an expert evaluator of the European Union large scale (multi-million) grant applications proposals. Her laboratory focuses on the classification of structural data pertaining to ligand-protein complexes, development of computational tools to better understand ligand recognition by macromolecular targets, and drug discovery approaches to specific disease areas.
More specifically, one major research area in the laboratory involves the investigation of protein binding sites and requirements for binding. We have compiled a list of structures for which both the bound target/ligand (holo) and free (apo) forms exist, in order to identify and correlate the nature of pockets with ligand characteristics needed at the macromolecular level. Because the heart of any structure-based modeling is the definition of a binding site, we have also undertaken a study that navigates through a spectrum of families and classifies them in computational terms by descriptor mapping. The work attempts to shed light on the ab initio computational prediction of the requirements for a binding site. Our results suggest that codifying sites from diverse protein families using a numerical representation is feasible. This in turn enables us to predict targets for new ligands or ligands for new targets.
Another thrust in the computational laboratory examines and systematizes sets of known drugs against respective homologous protein families in order to extrapolate common scaffolds that can then be used as starting points to building drugs piece-by-piece. These sets of scaffolds are representative of a generalized lead-like rather than drug-like chemical space applicable to a particular protein family. With these starting units, we proceed with fragment creation and linking toward different "hot" spots of the active site. The advantage of starting with smaller molecules is to identify structures that have ideal pharmaco-kinetic properties, an increased chemical diversity, and a higher chance of optimal binding to a target than larger, complex molecules. The approach concentrates on targets within a specific family of proteins and derives its knowledge base from a combination of compound and protein space, rather than a generalized chemical selection.
Finally, we are interested in understanding the origins of structural variation observed experimentally in several forms of the cytochrome P450s and disease-related targets, namely somatostatin and protein farnesyltransferase. Molecular dynamics simulations are being performed, combined with ligand-based methodologies, in order to probe the role of factors dictating selectivity.
1. M. Kontoyianni, P. Madhav, and E. Suchanek (2008), Theoretical and Practical Considerations in Docking and Scoring: A Beaten Field? Curr. Med. Chem. 15, 107-116.
2. Hopkins, C.R., O’Neil, S.V., Laufersweiler, M.C., Wang, Y., Soper, D.L., Ellis, C.D., Kontoyianni, M., Pokross, M., Petrey, M.E., Roesgen, J.T., Obringer, C.M., Richardson, E.C., DeMuth, T.P. Jr. (2006), Design and synthesis of novel N-sulfonyl-2-indole carboxamides as potent PPAR- binding agents with potential application to the treatment of osteoporosis, Bioorg. Med. Chem. Lett. 16, 5659-5663.
3. Zhong, H., Stewart, E.L., Kontoyianni, M., Bowen, J.P. (2005), Ab Initio and DFT Conformational Studies of Propanal, 2-Butanone, and Analogous Imines and Enamines, J. Chem. Theory Comput. 1(2), 230-238.
4. Kontoyianni, M., Sokol, G.S., and McClellan, L.M. (2005), Evaluation of Library Ranking Efficacy in Virtual Screening, J. Comp. Chem. 26, 11-22.
5. Kontoyianni, M., McClellan, L.M., and Sokol, G.S. (2004), Evaluation of Docking Performance: Comparative Data on Docking Algorithms, J. Med. Chem. 47, 558-565 (one of the top 15 most accessed papers in J. Med. Chem., 2004).
6. Dyatkin, A.D., Santulli,R.J., Hoekstra, W.J., Kinney, W.A., Kontoyianni, M., Kimball, E.S., Fisher, C.M., Prouty, S, Abraham, W.A., Andrade-Gordon, P., Hlasta, D.J., He,W., Hornby, P., Damiano, B.P., and Maryanoff, B.E. (2004), Aza-Bicyclic Amino Acid Sulfonamides as 41/47 Integrin Antagonists, Bioorg. Med. Chem. Lett. 14, 591-596.