Presentation at the 50th Anniversary of the University of Virginia's Biomedical Engineering (BME) Department, Charlottesville VA, November 3, 2017

1. Biomedical Engineering in a
Changing Scholarly Landscape
Philip E. Bourne, PhD, FACMI
Stephenson Chair of Data Science
Director Data Science Institute
Professor of Biomedical Engineering
peb6a@virginia.edu
Celebrating the 50th Anniversary of the University of Virginia’s Biomedical
Engineering Department
https://www.slideshare.net/pebourne
BME 50th Anniversary 1

2. BME 50th Anniversary 2
The past 50 years has seen science and
technology bring about profound
change…
What can we learn from that and how
can we (BME) be part of the even
more profound change yet to come?
Here are a few answers from my own
biased view

3. I was 14 when BME started …
BME 50th Anniversary 3
The subsequent 50 years of science..

4. The best of times….
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5. BME 50th Anniversary 5
~1975
3 months
170 MB
~103 atoms
118 ms (107)
256 GB (103)
2017
~107 atoms
Life is 3-D and it begins with molecules
10.1371/journal.pbio.2002041

6. We now have a usable structural
proteome of model organisms
BME 50th Anniversary 6
Example - Photography
Brunk et al. 2016 Systems Biology of the Structural Proteome
doi: 10.1186/s12918-016-0271-6
Zhang Zhao

7. All available PDB structures mapped to
the network of E. coli metabolism
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Brunk et al. 2016 Systems Biology of the Structural Proteome doi: 10.1186/s12918-016-0271-6

8. The worst of times …
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9. Source Michael Bell http://homepages.cs.ncl.ac.uk/m.j.bell1/blog/?p=830
On November 6, 2012, Donald Trump
tweeted: "The concept of global warming
was created by and for the Chinese in
order to make U.S. manufacturing non-
competitive."
BME 50th Anniversary 9

10. Source Michael Bell http://homepages.cs.ncl.ac.uk/m.j.bell1/blog/?p=830
Source Washington Post
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11. Message 1.
Going forward we have a responsibility
to promote good science not only
through our own work but through
what we do collectively…
This action can come in many forms …
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12. My own recent effort (excuse the self
promotion)
BME 50th Anniversary 12
Famous scientists
Scientists
known by
those who care
about science
Average scientists

13. Illustrations by Jason McDermott
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14. Message 2.
I believe upcoming changes in science
will be profound
BME 50th Anniversary 14

15. Disruption:
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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16. Disruption: Biomedical Research
Digitization of Basic &
Clinical Research & EHR’s
Deception
We Are Here
Disruption
Demonetization
Dematerialization
Democratization
Open
science
Patient centered health care
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17. BME 50th Anniversary 17
1667
WOS: 123,763/1,839
2017
Daniel Mietchen

18. Disruption because…
• We cant keep up with the literature, let alone
available data, analytical tools, predictive
models etc.
• In a digital world there are new (and better?)
ways to encode knowledge and learn from it
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19. Consider an example:
Small beta barrels - a structural building block
SCOP folds
b.38
b.34
b.87
b.36
b.40
b.136
b.137
b.35
b.55
b.41
b.138
b.39
pseudo-symmetry of the framework no pseudo-symmetry of the frameworkBME 50th Anniversary 19

20. 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
Same structural framework, lots of structural and functional variations
Knowledge is spread over 1,000’s of papers
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21. SM-like (b.38)
OB (b.40)
SplicingSignal transduction
Genome integrity
β-strands SH3-like (b.34) SM-like (b.38) OB (b.40)*
α/β0-helix-β1 N-term loop L1
β1-β2 RT L2 L12
β2-β3 n-Src L3 L23
β3-β4 Distal L4 L3α*, Lα4*
β4-β5 3-10 helix L5 L45
SH3-like (b.34)
Those papers use variable
nomenclature
Strongly bent 5-stranded
antiparallel β-sheet
2 antiparallel β-sheets
packed against each other
5-stranded β-sheet that
is coiled to form a closed
β-barrel
Two 3-stranded β-sheets
packed orthogonally to
form somewhat flattened
β-barrel
SCOP Barrel, partly open n=4, S=8 Barrel, open n=4, S=8
Barrel, closed or partly
open n=5, S=10 or S=8
DescriptionofthestructureNamingofloops
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22. It is years of work to pull all this
together …
Hard to publish …
When published the collective
knowledge is not very usable
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Stella Veretnik
Philippe
Youkharibache

23. Message 3.
Platforms will emerge that enable
better semantic reasoning across the
scientific knowledge base
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24. Platforms will ultimately digitally
integrate the scholarly workflow for
human and machine analysis
Should biomedical research be Like Airbnb?
doi: 10.1371/journal.pbio.2001818 BME 50th Anniversary 24Vivien Bonazzi

25. Paper Author Paper Reader
Data Provider Data Consumer
Employer Employee
Reagent Provider Reagent Consumer
Software Provider Software Consumer
Grant Writer Grant Reviewer
Supplier Consumer Platform
MS Project
Google Drive
Coursera
Researchgate
Academia.edu
Open Science
Framework
Synapse
F1000
Rio
Educator Student
Pilot Open Data Lab
Underway
BME 50th Anniversary 25gDOC

26. Message 4.
New tools will take advantage of such
platforms and accelerate discovery
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27. BME 50th Anniversary 27
At DeepMind, which is based in London,
AlphaGo Zero is working out how proteins
fold, a massive scientific challenge that
could give drug discovery a sorely needed
shot in the arm.

28. Engineering proteins nature has
missed?
There are ~ 20300 possible proteins
>>>> all the atoms in the Universe
96M protein sequences from
73,000 species (source RefSeq)
135,000 protein structures
yield 1221 folds (SCOPe 2.06)
Are their new scaffolds out there Nature has yet to discover that AI could?
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29. Example: Can deep neural networks
be used on protein structures?
Typical use cases
involve segmenting 2D
images to find which
pixels belong to a
certain class, i.e. dog
Can 3D image
segmentation be
used to find binding
sites on a protein
structure?
H2B Binding site in H2B:H4 PPI (3WKJ.H)
https://m2dsupsdlclass.github.io/lectures-labs/slides/04_conv_nets_2/images/dog_segment.jpg
Eli Draizen 29

30. Example: Histone H2B binding site
for histone H4
H2B
H4 H2B:H4 Binding Site
Nucleosome Core
Particle
3WKJ
3WKJ.H:3WKJ.F
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31. Can we predict the binding site
given the structure of only one
partner?
H2B H2B:H4 Binding Site
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32. Idea: Voxelize protein to find binding
sites with 3D convolutional neural
networks
1) Convert structure into “3D Image” where each atom is 1x1x1
Å box to perform image segmentation
H2B H2B:H4 Binding Site
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33. Convolutional Neural Networks
Downsample Information (Channels
or Features) to make it more
interpretable
Convolutional
Layers
Max Pooling
Layers
2) “Convolute” around image or volume taking small regions and multiple each
value in the region by the filter and adding all neighboring values in the region
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34. Features
For each voxel, create a 52-vector:
● Atom (Boolean, One-hot 12-vector)
● VDW
● Atom charge, +, - (Boolean)
● Hydrophobicity (KD)
● Accessible Surface Area
● Residue (Boolean, One-hot 20-vector)
● SS (E/H/X; Boolean, One-hot 3-vector)
● Train: Is binding site boolean
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35. Training Data: Clustered binding sites
from one taxonomic branch, using the
LUCA structure as the representative
# of Eukaryotic clusters (n>1):
4578
Use representative sequence of
cluster (LUCA) and train for 2
classes (0=not binding site,
1=binding site)
Goncearenco A, Shaytan AK, Shoemaker BA, Panchenko AR. Biophysical Journal. 2015
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36. Overall message for the coming years–
BME can lead change
• Engage with the Data Science Institute
• Experiment with platforms - participate in the
Open Data Lab
• Use the SIF fund to drive change
• Use the cluster hires to drive a focus on deep
learning and other emergent approaches
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37. Thank You
peb6a@virginia.edu
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