Protein Modeling and Docking, Drug and Target Discovery, Adverse Effects, New Therapies
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, neurodegeneration, 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 Student-voted Faculty of the Year Award (2018). Recognition as top 1% most performing and cited faculty, https://hcr.clarivate.com/ (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 Committee member of UCSD Bioinformatics and Systems Biology Graduate Program, the Swiss NSF NCCR- Transcure Center, and Hong Kong PolyU State Key Laboratory and Steering, Review or Advisory panel member. European Research Council Synergy Review Panel (2018,2019,2022)
- Pharmaceutical Chemistry II, Physical Pharmacology (SPPS 222)
- Principles of Pharmaceutical Sciences and Drug Development (SPPS 263A)
- Internal Coordinate Mechanics (ICM) for structure sampling, dynamics, molecular docking.
- Stochastic global optimization method with collective (fragment) moves, square-root sampling.
- Ligand Guided homology modeling. Structure-based lead discovery for many molecular targets.
- The Pocketome for target profiling and pharmacology prediction of leads and drugs
- Totrov M, Abagyan R (1997) Flexible protein–ligand docking by global energy optimization in internal coordinates. Proteins: Structure, Function, and Bioinformatics 29 (S1), 215-220
- An JH, Totrov M, Abagyan R (2005) Pocketome via comprenehsive identification and classification of ligand binding envelopes. Mol.Cell.Proteomics 4(6),752-761
- 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, 1741-5
- 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
- Chahal KK et al. (2019). Nilotinib, an approved leukemia drug, inhibits smoothened signaling in Hedgehog-dependent medulloblastoma. PLoS One, 14, e0214901
- Meewan et al. (2019) Discovery of New Inhibitors of Hepatitis C Virus NS3/4A Protease and Its D168A Mutant. ACS Omega. 4, 16999-17008
- Makunts T, Wollmer MA, Abagyan R, (2020) Postmarketing safety surveillance data reveals antidepressant effects of botulinum toxin across indications and sites. Sci Rep. 10(1):12851
- Sauvey C et al. (2021) Antineoplastic kinase inhibitors: A new class of potent anti-amoebic compounds. PLoS Negl Trop Dis, e0008425
- In silico screening of billions of compounds for binders to a molecular target, structure-based lead discovery and optimization, finding covalent or non-covalent inhibitors with a particular multi-target profile. Current therapeutic areas: oncology, neurodegenerative, psychiatric and neurological diseases, autoimmune disease, viral, bacterial, or eukaryotic parasitic infectious pathogens
- Finding molecular targets of an active compound by docking to Pocketome-encyclopedia
- Machine-learning models for pharmacological outcomes and molecular targets, predicting multi-target pharmacology and adverse effects of drugs and environmental chemicals