April 16, 2020 BME 8315, University of Virginia

1. Philip E. Bourne, PhD, FACMI
peb6a@virginia.edu
http://www.slideshare.net/pebourne
April 16, 2020 BME 8315

3.  What macromolecular structure brings to modeling
 What data science brings to modeling
 Taken together
 A new appreciation of data itself
 New methodologies
 A new translational emphasis
 Use cases
 Drug refinement and repurposing
 Multi-scale modeling
 The future – one person’s perspective

4. Structural Bioinformatics, Second Edition Edited by Jenny Gu and Philip E. Bourne

5. Volume Complexity
Integration

6. Sci Data. 2016 Mar 15;3:160018
Bioinformatics. 2000 Feb;16(2):159-68
The FAIR Principles
Rigorous Ontological
Representation

7. BMC Bioinformatics. 2005; 6: 21.
• What you see is not
what you get
• Good software has
real academic value
• Good design
transcends the
transience of
programming
technologies

8. Chromatin restructuring
RNA Splicing
Signal
transduction in
kinases
RNA interference
(RNAi)
pre-tRNA processing
Genome integrity: RPA,
TEBP
Signal transduction (various
pathways)
Transcriptional
regulation
RNA processing and degradation
What Lessons 3D Structure Brings to Modeling
Same structural framework, lots of structural and functional variations
Knowledge is spread over 1,000’s of papers
BME 50th Anniversary 8Structure. 2019 Jan 2;27(1):6-26

9. Protein Sci. 2019 Dec; 28(12): 2119–2126
Urfold Ur – to combine

10.  Value – assuring societal
benefit
 Design - Communication
of the value of data
 Systems – the means to
communicate and convey
benefit
 Analytics – models and
methods
 Practice – where
everything happens
02/04/20 Pune
[From Raf Alvarado]

11.  Value + Design = Openness,
responsibility
 Value + Analytics = Human
centered AI, algorithmic bias
 Value + Systems =
sustainability, access,
environmental impact
 Design + Analytics = literate
programming, visualization
 Design + Systems =
dashboards, engineering
design
 Analytics + Systems = ML
engineering
02/04/20 Pune
[From Raf Alvarado]
Thinking of data as a science unto itself is actually quite novel

12. 02/04/20 Pune

13. 02/04/20 Pune
Mura, Draizen, Bourne Curr Op in Struc Bio 2018, 52:95–102

14. 02/04/20 Pune

15. 02/04/20 Pune
https://www.sciencemag.org/news/2018/12/google-s-deepmind-aces-protein-folding
https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/

17.  What macromolecular structure brings to modeling
 What data science brings to modeling
 Taken together
 A new appreciation of data itself
 New methodologies
 A new translational emphasis
 Use cases
 Drug refinement and repurposing
 Multi-scale modeling
 The future – one person’s perspective

19. PKA
Phosphoinositide-3 Kinase (D) and Actin-
Fragmin Kinase (E)
ChaK (“Channel Kinase”)
PKA
Scheeff & Bourne 2005 PLOS Comp Biol 1(5):e49

20. • 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 20

21.  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

23. Output: arrhythmia
Xie et al 2015 PLOS Comp Biol 10(5):e1003554

25. 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 2017 Ann Rev Pharm & Tox 6;57:245-262

26.  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

27.  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

28. 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

29. 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

31. Zhao et al 2016 J. Med. Chem. 12:59(9) 4326-41

32. Crizotinib (1o)
ALK Kinase (wt)
Ceritinib (2o)
ALK Kinase (L1196M)
ALK Kinase (wt)
ALK Kinase (L1196M)
X
X

33. http://www.rcsb.org/pdb/pathway/pw.do Brunk et al 2016 BMC Sys Biol, 10:26

34.  What macromolecular structure brings to modeling
 What data science brings to modeling
 Taken together
 A new appreciation of data itself
 New methodologies
 A new translational emphasis
 Use cases
 Drug refinement and repurposing
 Multi-scale modeling
 The future – one person’s perspective

35. https://en.wikipedia.org/wiki/Jim_Gray_(computer_scientist)
https://www.microsoft.com/en-us/research/wp-
content/uploads/2009/10/Fourth_Paradigm.pdf
https://twitter.com/aip_publishing/status/856825353645559808
02/04/20 Pune
Of course this was all predicted
by smart people ..

36. Model
Transportability
Horizontal
Integration
Multi-scale
Integration
human
mouse
zebrafish
DNA
Gene/Protein
Network
Cell
Tissue
Organ
Body
Population
CNV SNP methylation
3D structure Gene
expression Proteomics
Metabolomics
MetabolicSignaling
transduction
Gene
regulation
Hepatic Myoepithelial Erythrocyte
Epithelial Muscle Nervous
Liver Kidney Pancreas Heart
Physiologically based
pharmacokinetics
GWASPopulation
dynamics
Microbiota
From Harnessing Big Data for Systems Pharmacology 2017
https://doi.org/10.1146/annurev-pharmtox-010716-104659
Current roadblocks are more cultural than technical
02/04/20 Pune

37. Gohlke et al. 201902/04/20 Pune
EHR
Animal Models
Pathways

38. Thank You!
Questions?
peb6a@virginia.edu @pebourne
02/04/20 Pune
.. And the ~160 who went before