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Structural Systems Pharmacology

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Presented to the Biomedical Engineering journal club on December 12, 2018, University of Virginia.

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Structural Systems Pharmacology

  1. 1. Zheng Zhao Cam Mura Phil Bourne http://www.slideshare.net/pebourne http://bit.ly/zhaoBourne [zz+pb pubs] http//bournelab.org [in prep] December, 2018
  2. 2. 2 Nat Rev Drug Discov 2010, 9 (3), 203-1 Drug discovery is a lengthy, cost-intensive process with high attrition rate. drug
  3. 3. A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690  Single gene knockouts only affect phenotype in 10-20% of cases  35% of biologically active compounds bind to two or more targets that do not have similar sequences or global shapes Paolini et al. Nat. Biotechnol. 2006 24:805–815 Kaiser et al. Nature 462 (2009) 175-81 Motivators  Predict side effects  Repurpose drugs
  4. 4. Currently 147,073 structures
  5. 5. 6 Year Number of entries and total number of polymer chains released per year Year Year Year Average Molecular Weight released per year
  6. 6. physiological process Understanding of dynamics and kinetics of protein- ligand interactions physiological processphysiological processphysiological process Knowledge representation and discovery & model integration Prediction of molecular interaction network on a large/genome scale Reconstruction, analysis and simulation of biological networks Traditional Approach Systems-based Approach Motivators
  7. 7. Why dock? Enzymology, Drug design Interested in designing a new protease inhibitor?
  8. 8. 08feb10 Why dock? Enzymology, Drug design Interested in designing a new protease inhibitor? Common approach: Begin by determining the 3D crystal structures of protein•drug complexes, thereby guiding further rounds of “rational” structure- based drug design (SBDD)
  9. 9. Consider the cellular interior… the cytosol… Proteins live in a densely crowded environment of molecular interactions (not in isolation, not at infinite dilution…) McGuffee&Elcock,PLOSCompBiol(2010)
  10. 10. Consider the cellular interior… the cytosol… These molecular contacts occur across many length-scales… the cytoplasm an antibiotic bound to the ribosome an anti-cancer drug bound to ABL kinase Mura & McAnany (2014), Molecular Simulation
  11. 11. P•L complexes: Intermolecular interactions P should bind L, but (maybe?) not too tightly—too low a G ==> extremely tight binding [low koff] The usual types of (noncovalent) interactions defining a P•L complex: 1) apolar (vdW / Lennard-Jones / dipole•••induced dipole, etc.) 2) polar (H-bonds, dipole•••dipole) 3) electrostatic (Coulombic) Recurring theme (& basis of docking!) Biomolecular interactions exhibit extremely high levels of geomet- ric/steric & chemical (e.g., -δD–Hδ+ ····Aδ- ) complementarity
  12. 12. Why dock in silico, in general?  Prediction of bio-molecular interactions? (A test of our basic molecular/physicochemical models and theories...)  Computer aided analysis saves resources (time, $$)  Measuring the relative strengths of interactions in some milieu of potentially interacting proteins…  Automated prediction of molecular interactions to aid in rational drug design (SBDD, CADD pipelines) ?  Drug design: Virtual Screening (VS)  Drug molecule database growth  (A whole new area is opening up now, with Deep Learning)
  13. 13. Protein•Ligand Docking, in context Molecular recognition is a (the!) central phenomenon in biology • Enzymes  Substrates • Receptors  Signal inducing ligands • Antibodies  Antigens Classifying docking problems in biology • Protein•Ligand docking – Rigid docking – Flexible docking • Protein•protein docking • Protein•{DNA, RNA} docking • {DNA, RNA}•ligand docking Ligand•Protein Docking • Proteins  Drugs (SBDD/CADD) • Proteins  Natural small-molecule substrates (metabolomics, lipidomics) OPTIONAL slide?
  14. 14. The Molecular Docking Problem Given two molecules with 3D conformations at atomic resolution: • Do the molecules bind to each other? …If yes, • What does the inter-molecular complex look like (3D) ? • What is the binding affinity (Kd)? Structures of protein-ligand complexes • X-ray (PDB: ~80,000 entries from X-ray crystallography, cryo-EM, and possibly other diffraction-based methods, as well as NMR) • NMR structure bundles Importance of protein 3D structures • Resolution < 2.5Å • Homology models (as receptor model) can be problematic Very OPTIONAL slide?
  15. 15. Docking Concepts: A target site (druggable?) Very OPTIONAL slide?
  16. 16. 17sep10 Generation of Cavity Model X-ray structure of HIV protease Molecular surface model at active site Active site filled with spheres. Sphere centers become potential locations for ligand atoms. Very OPTIONAL slide?
  17. 17. Protein•Ligand docking: blind or focused OPTIONAL slide?Mura & McAnany (2014), Molecular Simulation
  18. 18. Flexibility—an added complication(/opportunity) At T > 0, molecules are not static entities… They move! Mura & McAnany (2014), Molecular Simulation Esystem = U + K Simple idea  Simple functional forms… To achieve a balance between accuracy and computational simplicity
  19. 19. Can docking account for dynamics?—Yes… In many approaches, both clever and brute-force… • Lifting the rigidly frozen receptor constraint (ligand almost always treated as flexible, for years now); allowing receptor DoFs to be sampled (at least at level of sampling over side-chain rotamers) • A "relaxed complex" family of approaches, pioneered by McCammon & colleagues • Ensemble-based methods (e.g., biased MD to generate the docking ensemble) • Simulate the receptor∙∙∙drug binding process, in physical terms! (Recent, less brute-force approaches to this cleverly combine BD and MD.)
  20. 20. This is amazing.
  21. 21. This is amazing.
  22. 22. This is amazing.
  23. 23. physiological process Understanding of dynamics and kinetics of protein- ligand interactions physiological processphysiological processphysiological process Knowledge representation and discovery & model integration Prediction of molecular interaction network on a large/genome scale Reconstruction, analysis and simulation of biological networks Traditional Approach Systems-based Approach Motivators
  24. 24. Integrating chemical genomics and structural systems biology MD simulation Mj Q Refined interaction model Mj Q SMAP Protein-ligand docking Mj Q Mi 3D model of novel Target 3D model of annotated target Initial interaction model Query chemical Network modeling Experimental support Generalized Network Enrichment of Structure- Activity Relationships Xie & Bourne 2008 PNAS 105(14):5441-6 Xie et al 2012 Ann Rev Pharm & Tox 52:361-79 Xie et al 2016 Ann Rev Pharm & Tox in press
  25. 25.  Similar binding sites may bind similar ligands  A 3D object recognition problem • Globally different, but locally similar • Dynamic • Scalable SMAP – Determining Binding Site Similarity Across Protein Space
  26. 26.  Why? Large search space  Challenge: inherent flexibility and errors in predicted structures  Representation of the protein structure - Ca atoms only - Delaunay tessellation - Graph representation  Geometric Potential (GP) 0.2 0.1)cos( 0.1      i Di Pi PGP neighbors a100 0 Geometric Potential Scale 0 0.5 1 1.5 2 2.5 3 3.5 4 0 11 22 33 44 55 66 77 88 99 Geometric Potential binding site non-binding site Algorithm Xie & Bourne 2007 BMC Bioinformatics 4:S9
  27. 27. SMAP - Sequence-order Independent Profile-Profile Alignment (SOIPPA) L E R V K D L L E R V K D L Structure A Structure B S = 8 S = 4 Algorithm L E R V K D L S = 8 Xie & Bourne 2008 PNAS 105(14):5441-6
  28. 28. 0 0.01 0.02 0.03 0.04 0.05 0.06 0 0.1 0.2 0.3 0.4 True Positive RatioFalsePositiveRatio PSI-Blast CE SOIPPA 0 0.01 0.02 0.03 0.04 0.05 0.06 0 0.1 0.2 0.3 0.4 True Positive Ratio FalsePositiveRatio PSI-Blast CE SOIPPA Proteins with the same global shape Proteins with different global shape Xie & Bourne, PNAS, 105(2008):5441
  29. 29. • 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
  30. 30. PKA Phosphoinositide-3 Kinase (D) and Actin- Fragmin Kinase (E) ChaK (“Channel Kinase”) PKA Scheeff & Bourne 2005 PLOS Comp Biol 1(5):e49
  31. 31. physiological process Understanding of dynamics and kinetics of protein- ligand interactions physiological processphysiological processphysiological process Knowledge representation and discovery & model integration Prediction of molecular interaction network on a large/genome scale Reconstruction, analysis and simulation of biological networks Traditional Approach Systems-based Approach Motivators
  32. 32.  Drug repurposing to target Ebola virus SSP Pipeline Z Zhao, L Xie, P. E. Bourne et. al BMC Bioinform. Proteom e Compound lib Target( s) Interaction network(s) Candidate Compound(s) Data Collection Literature Chemical space 3D structure ; Docking; MD simulation 5 Similarity ; Profiling Druggabilit y Drug- likeness
  33. 33. physiological process Understanding of dynamics and kinetics of protein- ligand interactions physiological processphysiological processphysiological process Knowledge representation and discovery & model integration Prediction of molecular interaction network on a large/genome scale Reconstruction, analysis and simulation of biological networks Traditional Approach Systems-based Approach Motivators
  34. 34. http://www.rcsb.org/pdb/pathway/pw.do Brunk et al 2016 BMC Sys Biol, 10:26
  35. 35. Proteome Drug binding site alignments SMAP Predicted drug targets Drug and endogenous substrate binding site analysis Competitively inhibitable targets Inhibition simulations in context-specific model COBRA Toolbox Predicted causal targets and genetic risk factors Metabolic network Scientific literature Tissue and biofluid localization data Gene expression data Physiological objectives System exchange constraints Flux states optimizing objective Physiological context-specific model Influx Efflux Drug response phenotypes Drugtargets Physiological objectives Causal drug targets All targets 336 genes 1587 reactions Chang et al PLOS Comp. Biol. 2010 6(9): e1000938
  36. 36.  Lei Xie (CUNY CS): methodological developments  J Guler & J Papin labs (UVa): new anti-malarial drug design project, using a systems-bio approach  Bourne lab (Eli Draizen, Daniel Mietchen, Stella Veretnik): discussions and scientific feedback  Others? (Zheng??) Include an Acknowledgements slide……?…
  37. 37. 42  Heart disease (cardiac systems biology; Saucerman)  Idea 1 ?...  Idea 2 ?...  Communicable diseases (systems biology of infectious disease and microbial networks; Papin)  Anti-malarial drug-design collaboration w/ Guler and Papin  Cancers/neoplasms (signal transduction networks; Janes)  Maybe…?  Others? Add this Slide/idea as way to open doors to potential future collaborations.... ? Could be useful to end on a slide that’s something like this.

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