Ucl120810

• 1. Polypharmacology Studied Using Structural Bioinformatics and Systems Biology Philip E. Bourne University of California San Diego [email_address] http://www.sdsc.edu/pb UCL December 08, 2010

2. Big Questions in the Lab

• Can we improve how science is disseminated and comprehended?

• What is the ancestry of the protein structure universe and what can we learn from it?

• Are there alternative ways to represent proteins from which we can learn something new?

• What really happens when we take a drug?

• Can we contribute to the treatment of neglected {tropical} diseases?

3. What Really Happens When We Take a Drug?

• If we knew the answer we could:

• • Contribute to the design of improved drugs with minimal side effects

• • Contribute to how existing drugs and NCEs might be repositioned

Motivation 4. Why We Think This is Important

• Ehrlichs philosophy of magic bullets targeting individual chemoreceptors has not been realized in most cases witness the recent success of big pharma

• Stated another way The notion of one drug, one target, to treat one disease is a little nave in a complex system

Motivation 5. Polypharmacology - One Drug Binds to Multiple Targets

• Tykerb Breast cancer

• Gleevac Leukemia, GI cancers

• Nexavar Kidney and liver cancer

• Staurosporine natural product alkaloid uses many e.g., antifungal antihypertensive

Collins and Workman 2006Nature Chemical Biology2 689-700 Motivation 6. We Have Developed a Theoretical Approach to Address Polypharmacology

• Involves the fields of:

• • Structural bioinformatics

• • Cheminformatics

• • Systems-level biology

• • Pharmaceutical chemistry

Our Approach 7. Our Approach

• We can characterize a known protein-ligand binding site from a 3D structure (primary site) and search for that site on a proteome wide scale independent of global structure similarity

Our Approach 8. Which Means

• We could perhaps find alternative binding sites ( off-targets ) for existing pharmaceuticals and NCEs?

• If we can make this high throughput we could rationally explore a large network of protein-ligands interactions

Our Approach 9. What Have These Off-targets and Networks Told Us So Far? Some Examples

• • Nothing

• • A possible explanation for a side-effect of a drug already on the market(SERMs -PLoS Comp. Biol. ,2007 3(11) e217)

• • The reason a drug failed(Torcetrapib -PLoS Comp Biol 2009 5(5) e1000387)

• • How to optimize a NCE ( NCE against T. BruceiPLoS Comp Biol. 2010 6(1): e1000648)

• • A multi-target/drug strategy to attack a pathogen(TB-drugomePLoS Comp Biol2010 6(11): e1000976)

• • A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)

Our Stories 10. Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples Computational Methodology Generic Name Other Name Treatment PDBid Lipitor Atorvastatin High cholesterol 1HWK, 1HW8 Testosterone Testosterone Osteoporosis 1AFS, 1I9J .. Taxol Paclitaxel Cancer 1JFF, 2HXF, 2HXH Viagra Sildenafil citrate ED, pulmonary arterial hypertension 1TBF, 1UDT, 1XOS.. Digoxin Lanoxin Congestive heart failure 1IGJ 11. Number of released entries Year: 12. A Quick Aside RCSB PDB Pharmacology/Drug View 2010-2011

• Establish linkages to drug resources (FDA, PubChem, DrugBank, ChEBI, BindingDB etc.)

• Create query capabilities for drug information

• Provide superposed views of ligand binding sites

• Analyze and display protein-ligand interactions

Mockups of drug view features RCSB PDB Ligand View RCSB PDB Team Drug Name Asp Aspirin Has Bound Drug % Similarity to Drug Molecule 100 13. A Reverse Engineering Approach toDrug Discovery Across Gene Families Characterize ligand bindingsite of primary target(Geometric Potential) Identify off-targets by ligandbinding site similarity (Sequence order independentprofile-profile alignment) Extract known drugsor inhibitors of theprimary and/or off-targets Search for similar small molecules Dock molecules to bothprimary and off-targets Statistics analysisof docking scorecorrelations Computational Methodology Xie and Bourne 2009Bioinformatics 25(12) 305-312 14.

• Initially assign C atom with a value that is the distance to the environmental boundary

• Update the value with those of surrounding C atoms dependent on distances and orientation atoms within a 10A radius define i

• Conceptually similar to hydrophobicity

• or electrostatic potential that is

• dependant on both global and local

• environments

Characterization of the Ligand Binding Site- The Geometric Potential Xie and Bourne 2007BMC Bioinformatics,8(Suppl 4):S9 Computational Methodology 15. Discrimination Power of the Geometric Potential

• Geometric potential can distinguish binding and non-binding sites

100 0 Geometric Potential Scale Computational Methodology Xie and Bourne 2007BMC Bioinformatics,8(Suppl 4):S9 16. Local Sequence-order Independent Alignment with Maximum-Weight Sub-Graph Algorithm L E R V K D L L E R V K D L Structure A Structure B

• Build an associated graph from the graph representations of two structures being compared. Each of the nodes is assigned with a weight from the similarity matrix

• The maximum-weight clique corresponds to the optimum alignment of the two structures

Xie and Bourne 2008PNAS , 105(14) 5441 Computational Methodology 17. Similarity Matrix of Alignment

• Chemical Similarity

• Amino acid grouping: (LVIMC), (AGSTP), (FYW), and (EDNQKRH)

• Amino acid chemical similarity matrix

• Evolutionary Correlation

• Amino acid substitution matrix such as BLOSUM45

• Similarity score between two sequence profiles

f a ,f bare the 20 amino acid target frequencies of profileaandb , respectively S a ,S bare the PSSM of profileaandb , respectively Computational Methodology Xie and Bourne 2008PNAS , 105(14) 5441 18. What Have These Off-targets and Networks Told Us So Far? Some Examples

• • Nothing

• • A possible explanation for a side-effect of a drug already on the market(SERMs -PLoS Comp. Biol. ,2007 3(11) e217)

• • The reason a drug failed(Torcetrapib -PLoS Comp Biol 2009 5(5) e1000387)

• • How to optimize a NCE ( NCE against T. BruceiPLoS Comp Biol. 2010 6(1): e1000648)

• • A multi-target/drug strategy to attack a pathogen(TB-drugomePLoS Comp Biol2010 6(11): e1000976)

• • A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)

Our Stories 19. Selective Estrogen Receptor Modulators (SERM)

• One of the largest classes of drugs

• Breast cancer, osteoporosis, birth control etc.

• Amine and benzine moiety

Side Effects- The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217 20. Adverse Effects of SERMs cardiac abnormalitiesthromboembolicdisorders ocular toxicitiesloss of calciumhomeostatis????? Side Effects- The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217 21. Ligand Binding Site Similarity Search On a Proteome Scale

• Searching human proteins covering ~38% of the drugable genome against SERM binding site

• MatchingSacroplasmic Reticulum(SR) Ca2+ ion channel ATPase (SERCA) TG1 inhibitor site

• ER ranked top with p-value