What Really Happens When I Take a Drug? Philip E. Bourne University of California San Diego [email protected] http://www.sdsc.edu/pb Vancouver April 12, 2012 Big Questions in the Lab {In the spirit of Hamming} 1. Can we improve how science is disseminated and comprehended? 2. What is the ancestry and organization of the protein structure universe and what can we learn from it? 3. Are there alternative ways to represent proteins from which we can learn something new? 4. What really happens when we take a drug? 5. Can we contribute to the treatment of neglected Erren et al 2007 PLoS Comp. Biol., 3(10): e213 {tropical} diseases? Motivators Our Motivation • 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 2006 Nature Chemical Biology 2 689-700 Motivators Our Broad Approach • Involves the fields of: – Structural bioinformatics – Cheminformatics – Biophysics – Systems biology – Pharmaceutical chemistry • L. Xie, L. Xie, S.L. Kinnings and P.E. Bourne 2012 Novel Computational Approaches to Polypharmacology as a Means to Define Responses to Individual Drugs, Annual Review of Pharmacology and Toxicology 52: 361-379 • L. Xie, S.L. Kinnings, L. Xie and P.E. Bourne 2012 Predicting the Polypharmacology of Drugs: Identifying New Uses Through Bioinformatics and Cheminformatics Approaches in Drug Repurposing M. Barrett and D. Frail (Eds.) Wiley and Sons. (available upon request) Disciplines Touched & 2012 Reviews A Quick Aside – RCSB PDB Pharmacology/Drug View 2012 • Establish linkages to drug resources (FDA, Drug Name Asp Aspirin PubChem, DrugBank, ChEBI, BindingDB etc.) % Similarity to Has Bound Drug Drug Molecule 100 • Create query capabilities for drug information • Provide superposed views of ligand binding sites • Analyze and display protein-ligand interactions Mockups of drug view features Led by Peter Rose RCSB PDB’s Drug Work RCSB PDB Team A Quick Aside PDB Scope/Deliverables • Part I: small molecule drugs, nutraceuticals, and their targets ( DrugBank) - 2012 • Part II: peptide derived compounds (PRD)- tbd • Part III: toxins and toxin targets (T3DB), human metabolites (HMDB) • Part IV: biotherapeutics, i.e., monoclonal antibodies • Part V: veterinary drugs (FDA Green Book) RCSB PDB’s Drug Work Our Approach • We characterize a known protein-ligand binding site from a 3D structure (primary site) and search for similar sites (secondary sites) on a proteome wide scale independent of global structure similarity • We try a static and dynamic network- based approach to understand the implications of drug binding to multiple sites Methodology Applications Thus Far • Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Early detection of side-effects (J&J) • Late detection of side-effects (torcetrapib) • Lead optimization (e.g., SERMs, Optima, Limerick) • Drugomes (TB, P. falciparum, T. cruzi) Applications Approach - Need to Start with a 3D Drug- Receptor Complex – Either Experimental or Modeled 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 1TBF, 1UDT, arterial 1XOS.. hypertension Digoxin Lanoxin Congestive heart 1IGJ failure Computational Methodology Some Numbers to Show Limitations TB-drugome Pf- Drugome Target gene 3996 5491 Target protein in PDB 284 136 Solved structure in PDB 749 333 Reliable homology models 1446 1236 Structure coverage 43.29% 25.02% Drugs 274 321 Drug binding sites 962 1569 A Reverse Engineering Approach to Drug Discovery Across Gene Families Characterize ligand binding Identify off-targets by ligand site of primary target binding site similarity (Geometric Potential) (Sequence order independent profile-profile alignment) Extract known drugs or inhibitors of the primary and/or off-targets Search for similar small molecules … Dock molecules to both primary and off-targets Statistics analysis of docking score correlations Xie and Bourne 2009 Computational Methodology Bioinformatics 25(12) 305-312 Characterization of the Ligand Binding Site - The Geometric Potential . Conceptually similar to hydrophobicity or electrostatic potential that is dependant on both global and local environments • 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 Pi cos( i) 1.0 GP P neighborsDi 1.0 2.0 Computational Methodology Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9 Discrimination Power of the Geometric Potential 4 binding site non-binding site 3.5 3 • Geometric 2.5 potential can 2 distinguish 1.5 binding and non-binding 1 sites 0.5 0 100 0 0 11 22 33 44 55 66 77 88 99 Geometric Potential Geometric Potential Scale For Residue Clusters Computational Methodology Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9 Local Sequence-order Independent Alignment with Maximum-Weight Sub-Graph Algorithm Structure A Structure B L E R V K D L L E R V K D L • 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 Computational Methodology Xie and Bourne 2008 PNAS, 105(14) 5441 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 i i i i d fa Sb fb Sa i i fa, fb are the 20 amino acid target frequencies of profile a and b, respectively Sa, Sb are the PSSM of profile a and b, respectively Computational Methodology Xie and Bourne 2008 PNAS, 105(14) 5441 Scoring The Point is this Approach Can Now be Applied on a Proteome-wide Scale • Scores for binding site matching by SOIPPA follow an extreme value distribution (EVD). Benchmark studies show that the EVD model performs at least two-orders faster and is more accurate than the non-parametric statistical method in the previous SOIPPA version a) Blosum45 and b) b) McLachlan substitution matrices. Xie, Xie and Bourne 2009 Bioinformatics 25(12) 305-312 Applications Thus Far • Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Early detection of side-effects (J&J) • Late detection of side-effects (torcetrapib) • Lead optimization (e.g., SERMs, Optima, Limerick) • Drugomes (TB, P. falciparum, T. cruzi) Applications Nelfinavir • Nelfinavir may have the most potent antitumor activity of the HIV protease inhibitors Joell J. Gills et al, Clin Cancer Res, 2007; 13(17) Warren A. Chow et al, The Lancet Oncology, 2009, 10(1) • Nelfinavir can inhibit receptor tyrosine kinase(s) • Nelfinavir can reduce Akt activation • Our goal: • to identify off-targets of Nelfinavir in the human proteome • to construct an off-target binding network • to explain the mechanism of anti-cancer activity Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 7(4) e1002037 Possible Nelfinavir Repositioning drug target off-target? structural proteome binding site comparison 1OHR protein ligand docking MD simulation & MM/GBSA Binding free energy calculation network construction & mapping Clinical Outcomes Possible Nelfinavir Repositioning Binding Site Comparison • 5,985 structures or models that cover approximately 30% of the human proteome are searched against the HIV protease dimer (PDB id: 1OHR) • Structures with SMAP p-value less than 1.0e-3 were retained for further investigation • A total 126 structures have significant p-values < 1.0e-3 Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 2011 7(4) e1002037 Enrichment of Protein Kinases in Top Hits • The top 7 ranked off-targets belong to the same EC family - aspartyl proteases - with HIV protease • Other off-targets are dominated by protein kinases (51 off-targets) and other ATP or nucleotide binding proteins (17 off-targets) • 14 out of 18 proteins with SMAP p-values < 1.0e-4 are protein kinases Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 2011 7(4) e1002037 Distribution of Top Hits on the Human Kinome p-value < 1.0e-4 p-value < 1.0e-3 Manning et al., Science, 2002, V298, 1912 Possible Nelfinavir Repositioning Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides are comparable 1. Hydrogen bond with main chain amide of Met793 (without it 3700 fold loss of inhibition) 2. Hydrophobic interactions of aniline/phenyl with gatekeeper Thr790 and other residues EGFR-DJK EGFR-Nelfinavir Co-crys ligand H-bond: Met793 with benzamide H-bond: Met793 with quinazoline N1 hydroxy O38 DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE Off-target Interaction Network Identified off-target Pathway Activation Intermediate protein Cellular effect Inhibition PLoS Comp. Biol., 2011 7(4) e1002037 Possible Nelfinavir Repositioning Other Experimental Evidence to Show Nelfinavir inhibition on EGFR, IGF1R, CDK2 and Abl is Supportive The inhibitions of Nelfinavir on IGF1R, EGFR, Akt activity were detected by immunoblotting. The inhibition of Nelfinavir on Akt activity is less than a known PI3K inhibitor Joell J. Gills et al. Clinic Cancer Research