University of Virginia, August 30, 2016

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)