How structural biology can influence data science and vice versa. Based on a forthcoming paper in Current Opinions in Structural Biology https://arxiv.org/abs/1807.09247 and presented as part of the University of Virginia Data Science Institute Lunch and Learn Series, August 31, 2018

1. Data Science Meets Structural
Biology
Philip E. Bourne, Cam Mura & Eli Draizen
(Open Team Science)
https://www.slideshare.net/pebourne
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https://arxiv.org/abs/1807.09247

2. We are more interested in having a
discussion than giving a lecture …
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3. Lets start with a couple of definitions…
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4. What Do We Mean by Data Science?
• Use of the ever increasing amount of open,
complex, diverse digital data
• Finding ways to ask and then answer relevant
questions by combining such diverse data sets
• Arriving at statistically significant conclusions
not otherwise obtainable
• Sharing such findings in a useful way
• Translating such findings into actions that
improve the human condition
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5. What Do We Mean by Structural
Biology?
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6. Structure… What’s it good for??
Classic structural biology example
A point mutation (E6→V) in the Hb β globin chain results in sickle
cell anemia

7. Structural biology success stories
microtubule
Atomic-resolution studies of cellular-scale systems have bec-
ome increasingly possible — immense explanatory power!
mid-1990s
1960-70s
early1990s
~2002
1986

8. Why Do We Care About this
Intersection?
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Stepping back…
Data are transforming how we think about
everything, including biomedical research…
Most folks just do not realize it yet…
Your reading of this slide relies on structural
biology (a photoreceptor called rhodopsin!)

9. Digitization
Deception
Disruption
Demonetization
Dematerialization
Democratization
Time
Volume,Velocity,Variety
Digital camera invented by
Kodak but shelved
Megapixels & quality improve slowly;
Kodak slow to react
Film market collapses;
Kodak goes bankrupt
Phones replace
cameras
Instagram,
Flickr become the
value proposition
Digital media becomes bona fide
form of communication
From a presentation to the Advisory Board to the NIH Director
Example - Photography
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10. How is the DSI Responding to this Change?
• Societal good
• Interdisciplinary
• Practical experience
• Ethical conduct
• Openness and transparency
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Surge in publications involving machine
learning in the biosciences ('J-curve')

11. Example of Why More Openness:
Diffuse Intrinsic Pontine Gliomas (DIPG)
• Occur 1:100,000
individuals
• Peak incidence 6-8 years
of age
• Median survival 9-12
months
• Surgery is not an option
• Chemotherapy ineffective
and radiotherapy only
transitive
From Adam Resnick
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12. Timeline of genomic studies in DIPG
• Landmark studies identify
histone mutations as
recurrent driver mutations in
DIPG ~2012
• Almost 3 years later, in
largely the same datasets,
but partially expanded, the
same two groups and 2
others identify ACVR1
mutations as a secondary, co-
occurring mutation
From Adam Resnick
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13. What do we need to do differently to
reveal ACVR1?
• ACVR1 is a targetable kinase
• Inhibition of ACVR1 inhibited tumor
progression in vitro
• ~300 DIPG patients a year
• ~60 are predicted to have ACVR1
• If large scale data sets were only
integrated with TCGA and/or rare
disease data in 2012, ACVR1 mutations
would have been identified
• 60 patients/year X 3 years = 180
children’s lives (who likely succumbed to
the disease during that time) could have
been impacted if only data were FAIR
From Adam Resnick
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14. 08/31/18 DSI Lunch & Learn 14
Working across the Grounds
to break down traditional silos

15. • Sustainable
• Designing for where the academical village meets Google – an
ecosystem in which students, faculty, staff, visitors, private sector
reps, entrepreneurs live and work
• Open UVA and open data – Wikimedian in Residence
• Collaboration
– Dual degrees
– Research projects across disciplines
– Sister institutions
• MS DS focusing on practical training
• PhD program
• Undergraduate major
• Undergraduate certificate
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Hallmarks
Reflecting Those
Principles
Under development

16. DSI Organization
Structural Biology is one of Many Cross Cutting Initiatives
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Data Integration
& Engineering
Machine Learning
& Analytics
Visualization
& Dissemination
Data Acquisition Ethics, Law,
Policy,
Social Implications
Structural Biology

17. DSI Organization
Structural Biology is one of Many Cross Cutting Initiatives
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Structural Biology mapped onto the five pillars of Data Science
Structural Biology

18. Lets Briefly Focus on those Five Points
of Intersection in the Context of
Structural Biology …
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19. Data Acquisition
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The data production issue (the V’s of Big Data)— Experimentally
• Estimated (2017) that ≈2.5 quintillion (2.5×1018) bytes of data generated daily, with 90%
of all the world’s data having been created in the past two years.
• Plaintext PDB files typically ≈ few 100s KB (…but, that’s just the start!)

20. Data Acquisition
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The data production issue (the V’s of Big Data)— Computationally
• Here are some 2D RMSD matrices from a µs-scale biomolecular simulation.
• Half a mole (6.02×1023) of calculations!

21. Data Acquisition
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The data reduction issue (the V’s of Big Data)— Computationally
• The produce/spawn/consume idiom (MapReduce)

22. Data Integration and
Engineering
• Data are structured
– Ontologies
– Object identifiers
– Indexing schemes
– Common data models
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23. Machine Learning &
Analytics
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• Structure->Function• Sequence->Structure
Protein•Protein
Protein•Ligand
Binding sites

24. Machine Learning &
Analytics
• Neural nets
• Deep learning
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25. Machine Learning &
Analytics
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• Deep Learning for Object Recognition/Segmentation
Features in
Image Slice
Predicted
Classes
Badrinarayanan, et al. 2016. arXiv:1511.00561v3

26. Machine Learning &
Analytics
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• Deep Learning for Object Recognition/Segmentation
Features in
Volume Slice
Predicted
Classes
Badrinarayanan, et al. 2016. arXiv:1511.00561v3

27. Visualization
• VR
• Networks
• Sonics
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Starting point: Structure of a bacterial
protein involved in RNA-associated
regulatory circuits (e.g., virulence)

28. Visualization
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29. Visualization
• VR
• Networks
• Sonics
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What about dynamics (life not at T=0)?

30. Visualization
• VR
• Networks
• Sonics
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What about dynamics (life not at T=0)?

31. Visualization
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What about physics (of RNA-binding)?

32. Visualization
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What about statistics (log-odds here)?

33. Visualization
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What about cellular-scale systems?
Kozlikova et al., 2016; Comp Graph Forum

34. Visualization
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What about cellular-scale systems?
Kozlikova et al., 2016; Comp Graph Forum

35. Ethics, Law,
Policy & Social
Implications
•A Story of Fraud
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36. Thank You
peb6a@virginia.edu
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