Protein Modeling and Docking, Drug and Target Discovery, Adverse Effects, New Therapies
Professor
Skaggs School of Pharmacy and Pharmaceutical Sciences
Dr. Abagyan’s research focuses on the development of novel technologies for structure based drug discovery and optimization, structural systems biology for target finding and protein modeling. The lab screens specific biomedical targets to discover new drug leads, and validate them experimentally. The applications include cancer, neuro-degeneration, parasitic viral and endocrine diseases. To extend the reach of docking we model alternative functional states and allosteric pockets of the kinases, GPCRs and Nuclear Receptors. We derived comprehensive sets of ligand pockets (the Pocketome) competing for ligands and metabolites in different organisms. These data are used for target identification and multi-target pharmacology profiling. We dock drugs, leads and environmental chemicals to the ‘anti-target’ models to predict endocrine disruption and other adverse effects. We also identify new promising uses of existing drugs on the basis of the multi-target pharmacology.
Education:
S.c. laude M.S. in Molecular and Chemical Biophysics (1980) Moscow Inst. Physics & Technology; Ph.D. in Protein Structure Prediction (1984) Moscow State University.
Awards and Honors:
Two CapCure awards for excellence in prostate cancer research (2000, 2002); Princess Diana award and medal, Sydney (2003); UCSD Faculty and Staff Excellence Award (2007). AACP’s 2016 Teacher of the Year Award; SSPPS Faculty of the Year Award, 2018
Leadership Experience:
Director of Computational Biology & IT at Skirball Inst. of Biomolecular Medicine, New York (1994-1999); Director at Novartis Institute, GNF (1999-2002); SBDD chair, MipTec, Basel, Switzerland (2002-2009); Founder of MolSoft (1994); Member of Board of Directors of Syrrx (2001-2002); SAB Member of Plexus Vaccines (2001- 2003); Editorial Boards (current): Endocrine Disruptors, Am.J.Pharm.Tox, Cancer Genomics and proteomics, IUPAC Glossary Committee, J.Comp- Aided Mol.Des.; Steering, Review or Advisory panel member for UCSD Bioinformatics Program, the Swiss NSF NCCR- Transcure Center, and Hong Kong PolyU State Key Laboratory and Steering, Review or Advisory panel member.
- Pharmaceutical Chemistry II, Physical Pharmacology (SPPS 222)
- Principles of Pharmaceutical Sciences and Drug Development (SPPS 263A)
- Internal Coordinate Mechanics (ICM) for structure prediction, dynamics and accurate ligand docking.
- Stochastic global optimization method with collective (fragment) moves, square-root sampling.
- Ligand Guided homology modeling. Lead discovery for kinases, nuclear receptors and GPCRs.
- The Pocketome for target profiling and poly-pharmacology prediction
- Bottegoni et al. (2009). Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking. J Med Chem 52:397-406.
- Chen YC, Totrov M, Abagyan R (2014) Docking to multiple pockets or ligand fields for screening, activity prediction and scaffold hopping. Future Med Chem, 6, 1741-55
- Ali et al., (2015) Adverse Effects of Cholinesterase Inhibitors in Dementia According to the Pharmacovigilance Databases of the United-States and Canada. PLoS One 10(12):e0144337
- Bennet et al., (2016) An electrostatic mechanism for Ca++ mediated regulation of gap junction channels. Nature Communications12 (7):8770
- Chan et al., (2017) The anthelmintic praziquantel is a human serotoninergic G-protein-coupled receptor ligand. Nature Communications 8(1): 1910
- Cohen et al. (2017) Population scale data reveals the antidepressant effects of ketamine and other therapeutics approved for non-psychiatric indications. Sci Rep. 3;7(1):1450
- Modeling by homology, lead discovery and optimization for drug targets including kinases, proteases, NRs, transporters and GPCRs
- Virtual ligand screening and structure-focused libraries; target identification via Pocketome
- Predicting polypharmacology and adverse effects of drugs and environmental chemicals