Systems Biology & Pharmacology from a Structural Perspective
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Philip Bourne gave a talk on his past research leading data science at the NIH and his current and future research. Some of his past work includes developing methods for determining binding site similarity across protein space using geometric potentials and sequence-order independent profile-profile alignment. His current research focuses on using these methods for applications like predicting drug efficacy, toxicity and repurposing old drugs for new uses. He discussed challenges like the large search space and flexibility of proteins. Going forward, he hopes cloud computing environments can help make data and tools more accessible and reusable to advance systems pharmacology research.
Systems Biology & Pharmacology from a Structural Perspective
- 1. Philip E. Bourne, PhD, FACMI National Center for Biotechnology Information philip.bourne@nih.gov http://www.slideshare.net/pebourne August 30, 2016, University of Virginia
- 2. The past 2.5 years has very much been devoted to leading data science at the NIH and while this is predominantly a talk setting the context for my research, followed by current and future research, elements of work in open data science will inevitably creep in ….
- 3. Clegg et al. 1980 Nature 5788:298-300 Apoferritin Iron storage
- 4. Clegg et al. 1980 Nature 5788:298-300 Apoferritin Iron storage Biologically active molecule
- 5. Currently 122,000 structures & ~10TB
- 7. Bourne et al. 1997 Meth. Enz. 277 571-590Developed under the auspices of the IUCR
- 8. save__atom_site.Cartn_x _item_description.description ; The x atom site coordinate in angstroms specified according to a set of orthogonal Cartesian axes related to the cell axes as specified by the description given in _atom_sites.Cartn_transform_axes. ; _item.name '_atom_site.Cartn_x' _item.category_id atom_site _item.mandatory_code no _item_aliases.alias_name '_atom_site_Cartn_x' _item_aliases.dictionary cifdic.c94 _item_aliases.version 2.0 loop_ _item_dependent.dependent_name '_atom_site.Cartn_y' '_atom_site.Cartn_z' _item_related.related_name '_atom_site.Cartn_x_esd' _item_related.function_code associated_esd _item_sub_category.id cartesian_coordinate _item_type.code float _item_type_conditions.code esd _item_units.code angstroms Bourne et al. 1997 Meth. Enz. 277 571-590
- 10. Gu & Bourne (Ed) 2009
- 11. Samish, Bourne & Najmanovich Bioinformatics 2015 31:146-150 ~~
- 13. Bernard M. Nat Rev Drug Disc 8(2009), 959-968
- 14. Can we predict drug efficacy and toxicity? Can we reuse old drugs? Can we design personalized medicines? ~200 drugs with identified effects http://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.htm
- 16. Output: arrhythmia Xie et al 2015 PLOS Comp Biol 10(5):e1003554
- 18. 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
- 19. 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
- 20. 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
- 21. 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
- 22. 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
- 24. Muller et al. 2015 Nature Chemical Biology 11, 818-821
- 25. • 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 10/16/13 ACSSA 25
- 26. PKA Phosphoinositide-3 Kinase (D) and Actin- Fragmin Kinase (E) ChaK (“Channel Kinase”) PKA Scheeff & Bourne 2005 PLOS Comp Biol 1(5):e49
- 27. Zhao et al 2016 J. Med. Chem. 12:59(9) 4326-41
- 28. Zhao et al 2016 J. Med. Chem. 12:59(9) 4326-41
- 32. Side effect prediction Xie et al. PLoS Comp. Biol., 3(2007):e217 Xie et al. PLoS Comp. Biol., 5(2009):e1000387 Drug repurposing Kinnings et al. PLoS Comp. Biol., 5(2009):e1000423 Xie et al. PLoS Comp. Biol. 7(2011): e1002037 Ng. et al. PSB Symposium (2014) Polypharmacological drug design Durant et al. PLoS Comp. Biol. 6(2010):e100648 Chang et al. BMC Sys. Biol. 7(2013):102 Personalized medicine Xie et al. BMC Genomics 14(2013):S9
- 34. Brunk et al 2016 BMC Sys Biol, 10:26
- 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
- 37. Data and tools are not easily used (aka not FAIR) Only 12% of output is even reported What is available is siloed It is not easy to stand on the shoulders of giants Cloud environments are potential technical solutions Genomics is leading the way eg GA4GH We need similar approached for systems pharmacology
- 38. Services: APIs, Containers, Indexing, Software: Services & Tools scientific analysis tools/workflows App store/User Interface Shared Research Objects Structure Space Cell ModelsChemical Space Phenotypes
- 39. “… and hopefully get to say I told you so when they are shown to improve the human condition.” -- Phil Bourne
- 40. All 135 previous lab members Lei Xie Zheng Zhao PDB Team Roger Chang (Palsson Lab)