Associate Professor of Pharmaceutical Sciences
BS, Pharmacy, University of Athens, Greece
MSc, Medicinal Chemistry, University of Illinois, Medical Center, Chicago
PhD, 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 are also navigating through a spectrum of families and classifying them in computational terms by descriptor mapping. Both projects attempt to shed light on the ab initio computational prediction of the requirements for a binding site. Our results thus far suggest that codifying sites from diverse protein families using numerical representations 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 toward 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 that it enables us to identify structures with more ideal pharmacokinetic properties, an increased chemical diversity, and a higher chance of optimal binding to a target. 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 chemokine receptors. Molecular dynamics simulations combined with model-building and docking methodologies are employed in order to probe the role of factors dictating selectivity.
Kontoyianni, M. (2012), A Decade Later, J. Autacoids 1(3), e112.
Liu, Z., Crider, A.M., Ansbro, D., Hayes, C., and Kontoyianni, M. (2012), A Structure-Based Approach to Understanding Somatostatin Receptor-4 Agonism (sst4), J. Chem. Inf. Model. 52, 171-186.
Kontoyianni, M. and Liu, Z. (2012), Structure-Based Design in the GPCR Target Space, Curr. Med. Chem. 19, 544-556
Kontoyianni, M. and Rosnick, C. (2012), Functional Prediction of Binding Pockets, J. Chem. Inf. Model. 52, 824-833.
Kontoyianni, M., Madhav, P., Suchanek, E. and Seibel, W. (2008), Theoretical and Practical Considerations in Docking and Scoring: A Beaten Field? Curr. Med. Chem. 15, 107-116.
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.
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.
Kontoyianni, M., Sokol, G.S., and McClellan, L.M. (2005), Evaluation of Library Ranking Efficacy in Virtual Screening, J. Comp. Chem. 26, 11-22.
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).
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.
Orme, M.M., Baindur, N., Robbins, K.G., Harris, S.M., Kontoyianni, M., Hurley, L.H., Kerwin, S.M., Mundy, G., and Petrie, C. Compositions and methods for treating bone deficit conditions. US6649631, 2003.