biomedical engineering in a changing scholarly landscape
TRANSCRIPT
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
Celebrating the 50th Anniversary of the University of Virginia’s Biomedical Engineering Department
https://www.slideshare.net/pebourne
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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
I was 14 when BME started …
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The subsequent 50 years of science..
The best of times….
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~1975
3 months
170 MB
~103 atoms
118 ms (107)
256 GB (103)
2017~107 atoms
Life is 3-D and it begins with molecules10.1371/journal.pbio.2002041
We now have a usable structural proteome of model organisms
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Example - Photography
Brunk et al. 2016 Systems Biology of the Structural Proteome doi: 10.1186/s12918-016-0271-6
Zhang Zhao
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
The worst of times …
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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."
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Source Michael Bell http://homepages.cs.ncl.ac.uk/m.j.bell1/blog/?p=830
Source Washington Post
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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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My own recent effort (excuse the self promotion)
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Famous scientists
Scientists known by
those who care about science
Average scientists
Illustrations by Jason McDermott
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Message 2.
I believe upcoming changes in science will be profound
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Disruption:
DigitizationDeception
Disruption
Demonetization
Dematerialization
Democratization
Time
Volu
me,
Velo
city,
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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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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1667
WOS: 123,763/1,839
2017
Daniel Mietchen
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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Consider an example:Small beta barrels - a structural building block
SCOP folds
b.38
b.34
b.87
b.36
b.40b.136
b.137b.35
b.55
b.41
b.138
b.39
pseudo-symmetry of the framework no pseudo-symmetry of the frameworkBME 50th Anniversary 19
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 variationsKnowledge is spread over 1,000’s of papers
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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
Des
crip
tio
n o
f th
e st
ruct
ure
Nam
ing
of
loo
ps
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It is years of work to pull all this together …
Hard to publish …
When published the collective knowledge is not very usable
BME 50th Anniversary 22Stella Veretnik
Philippe Youkharibache
Message 3.Platforms will emerge that enable
better semantic reasoning across the scientific knowledge base
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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
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 ProjectGoogle Drive
CourseraResearchgateAcademia.eduOpen Science Framework
SynapseF1000
Rio
Educator Student
Pilot Open Data Lab Underway
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Message 4.New tools will take advantage of such
platforms and accelerate discovery
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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.
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?BME 50th Anniversary 28
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
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Example: Histone H2B binding site for histone H4
H2BH4 H2B:H4 Binding Site
Nucleosome Core Particle3WKJ
3WKJ.H:3WKJ.F
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Can we predict the binding site given the structure of only one
partner?
H2B H2B:H4 Binding Site
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Idea: Voxelize protein to find binding sites with 3D convolutional neural
networks1) 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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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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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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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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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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