Dr. Ruben Abagyan: Structure Based Drug Discovery and Optimization, Structural Chemical Genomics

Research Summary: Structure Based Drug Discovery and Structural Chemical Genomics The rapid growth of the structural understanding of the human proteome, modeling techniques and computers, creates a unique opportunity to extend the list of molecular targets, and rationally discover new chemical leads, or repurpose the existing drugs. Dr. Abagyan’s research focuses on the development of novel technologies for structure based drug discovery and optimization, structural chemical genomics and protein modeling. The methods are then applied to specific biomedical targets to discover de novo drug leads for new protein targets or pockets, or optimize the lead compounds using the structure-based approach. We are particularly interested in modeling alternative functional states or structurally uncharacterized members of the kinase, GPCR and Nuclear Receptor families of proteins.

Academic Achievements

Education: M.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).

Leadership Experience: Director of Computational Biology & IT at Skirball Inst. of Biomolecular Medicine, New York (1994-1999); Director at Novartis Institute for Functional Genomics (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 of Biology Direct, Molecular & Cellular Proteomics, Cancer Genomics & Proteomics (current); Chairman of the UCSD Bioinformatics research rotation committee (2009-).

Teaching

• Pharmaceutical Chemistry II, Physical Pharmacology (SPPS 222).

Key Contributions to Pharmaceutical Sciences

• Internal Coordinate Mechanics for structure prediction, dynamics and accurate ligand docking.
• A collective internal coordinate stochastic global optimization method, square-root sampling.
• In silico de-orphanization and drug re-purposing using a model of the binding pocket only.
• Ligand Guided Method for alternative states or homology models of binding sites. Applications to androgen receptors, kinases and GPCR modeling.
• Original author of the ICM software.

Selected Recent Publications (from 160 papers)

Abagyan et al. (1994). Biased probability Monte Carlo conformational searches. J Mol Biol 235:983-1002.

Abagyan et al. (1994). ICM: A New Method for Protein Modeling and Design: Applications to Docking. J Comp Chem 15:488-506.

Totrov et al. (2001). Rapid boundary element solvation electrostatics calculations in folding simulations: successful folding of a 23-residue peptide. Biopolymers 60:24-33.

Cavasotto et al. (2003). Structure-based identification of binding sites, native ligands for G-protein coupled receptors. Proteins 51:423-433.

Bisson et al. (2007). Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs. PNAS 104:11927-11932.

Kufareva et al. (2008). Type-II kinase inhibitor docking, screening, and profiling using modified structures of active kinase states. J Med Chem 51:7921-7932.

Abagyan et al. (2009). The flexible pocketome Engine for structural chemogenomics. Methods Mol Biol 575:249-279.

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.

Cheltsov et al. (2010). Vaccinia virus virulence factor N1L is a novel promising target for antiviral therapeutic intervention. J Med Chem. 53:3899-906

Potential Collaborative Programs with Pharmaceutical Industry

• Accurate models by homology for drug targets and conformational states, kinases and GPCRs
• Developing ADME Tox models
• Ligand screening against 5 to 10 million compounds
• Generation of structure focused combinatorial libraries
• Multip-parameter optimization of drug candidates