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Wires Within WiresA Minimal Model for Computational Bioelectronic Peptide Design
R. A. Mansbach1 A. L. Ferguson2
1Physics Department
2Materials Science DepartmentUniversity of Illinois at Urbana-Champaign
Blue Waters Symposium, Sunriver, OR, June 4, 2018
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
π-conjugated self-assembling optoelectronic peptides
Wall, Brian D., et al. “Supramolecular Polymorphism:Tunable Electronic Interactions within π-ConjugatedPeptide Nanostructures Dictated by Primary Amino AcidSequence.” Langmuir30.20 (2014): 5946-5956.
www.imore.com/sites/imore.com/files/styles/large/
public/topic_images/2015/
Galagan, Y.,& Andriessen, R. (2012).“Organic photovoltaics: technologies andmanufacturing.” INTECH Open AccessPublisher.
topic-apple-watch-all.png?itok=OUtlCphV2 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
π-conjugated self-assembling optoelectronic peptides
Wall, Brian D., et al. “Supramolecular Polymorphism:Tunable Electronic Interactions within π-ConjugatedPeptide Nanostructures Dictated by Primary Amino AcidSequence.” Langmuir30.20 (2014): 5946-5956.
www.imore.com/sites/imore.com/files/styles/large/
public/topic_images/2015/
Galagan, Y.,& Andriessen, R. (2012).“Organic photovoltaics: technologies andmanufacturing.” INTECH Open AccessPublisher.
topic-apple-watch-all.png?itok=OUtlCphV2 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
π-conjugated self-assembling optoelectronic peptides
Wall, Brian D., et al. “Supramolecular Polymorphism:Tunable Electronic Interactions within π-ConjugatedPeptide Nanostructures Dictated by Primary Amino AcidSequence.” Langmuir30.20 (2014): 5946-5956.
www.imore.com/sites/imore.com/files/styles/large/
public/topic_images/2015/
Galagan, Y.,& Andriessen, R. (2012).“Organic photovoltaics: technologies andmanufacturing.” INTECH Open AccessPublisher.
topic-apple-watch-all.png?itok=OUtlCphV2 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
DXXX series demonstrates hierarchical assembly
Optical Clusters
Contact Clusters
3 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
DXXX series demonstrates hierarchical assembly
Optical Clusters Contact Clusters
3 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Reaching longer time and length scales
Minimal coarse-grained model
Large computational infrastructure
Do parameter sweep over welldepths and radii to gainunderstanding of effect of differentinteraction parameters on assemblyat mesoscale
4 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Reaching longer time and length scales
Minimal coarse-grained model
Large computational infrastructure
Do parameter sweep over welldepths and radii to gainunderstanding of effect of differentinteraction parameters on assemblyat mesoscale
4 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Understanding chemical interactions at low resolution
Minimal coarse-grained model
Large computational infrastructure
Do parameter sweep over welldepths and radii to gainunderstanding of effect of differentinteraction parameters on assemblyat mesoscale
5 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Aggregate shape and fractal dimension match previous work
Ardona, Herdeline Ann M., and John D. Tovar. “Energy transfer within responsiveπ-conjugated coassembled peptide-based nanostructures in aqueous
environments.” Chemical Science 6.2 (2015): 1474-1484.6 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Interaction parameters control aggregate morphology
Increasing side chain stickiness increases disorder
Side chain size controls transition between flat ribbon and twisted fibril
7 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Optical cluster growth is primarily controlled by side chaininteractivity
Optical Cluster Growth
Increasing sidechain well depthincreasesfavorability of sidechain–side chaininteractions
Biggest increase asside chaininteractivitydecreases belowcore–coreinteractivity
8 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Side chain radius affects contact cluster growth more strongly
Contact Cluster Growth Fewer configurations
Increasing cross-section
9 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Identification of optimal parameter sets
Pareto frontier
Tradeoff between different objectives10 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Five candidates flagged for future study
11 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Next steps: Active Learning
Brochu, Eric, Vlad M. Cora, and Nando De Freitas. “A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and
hierarchical reinforcement learning.” arXiv preprint arXiv:1012.2599 (2010).12 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Next steps: Active Learning
Brochu, Eric, Vlad M. Cora, and Nando De Freitas. “A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and
hierarchical reinforcement learning.” arXiv preprint arXiv:1012.2599 (2010).12 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Next steps: Active Learning
Brochu, Eric, Vlad M. Cora, and Nando De Freitas. “A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and
hierarchical reinforcement learning.” arXiv preprint arXiv:1012.2599 (2010).12 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Why Blue Waters?
Scale of problem
300 simulations of 10,648monomersEach simulation requires multipleGPU acceleration and produces10-20 gigabytes of data to beanalyzed
Big data research infrastructure
Access to broader big datacommunity
https://www.slideshare.net/sergejsgroskovs/
pragmatism-philosophy-of-science-lecture-slides
13 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Why Blue Waters?
Scale of problem
300 simulations of 10,648monomersEach simulation requires multipleGPU acceleration and produces10-20 gigabytes of data to beanalyzed
Big data research infrastructure
Access to broader big datacommunity
https://www.slideshare.net/sergejsgroskovs/
pragmatism-philosophy-of-science-lecture-slides
13 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Broader Impact
Created a patchy model thatrecapitulates DXXX properties andreaches mesoscopic scale
Showed effects of changingparameter space
Identified potential ways to designfor optimal parameters
Part of a multiscale model forrational peptide design
14 / 15
Wires WithinWires
Mansbach,Rachael
Motivation
Patchy Model
Results
Conclusionsand FutureWork
Acknowledgments
∗ *This research is part of the Blue Waterssustained-petascale computing project,which is supported by the National ScienceFoundation(awards OCI-0725070 and ACI-1238993)and the state of Illinois. Blue Waters is ajoint effort of the University of Illinois at
Urbana-Champaign and its National
Center for Supercomputing Applications.
15 / 15
Wires WithinWires
Mansbach,Rachael
Choice ofparameterspace forsweep
Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Backup Slides
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Wires WithinWires
Mansbach,Rachael
Choice ofparameterspace forsweep
Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Initial Parameter Sweep: Aromatic Cores
Non cofacial aromatic εBB
Set to 1 kBT
Cofacial aromatic εA
Cv
~ 18 kT
Sweep over 2.5-7.5kBT depth2 / 13
Wires WithinWires
Mansbach,Rachael
Choice ofparameterspace forsweep
Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Initial Parameter Sweep: Side Chains
Side chain εSC
~ 2 kT
Sweep over 0.2-10 kBT
Side chain σSC
Sweep over 1.0 -1.75 nm
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Wires WithinWires
Mansbach,Rachael
Choice ofparameterspace forsweep
Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Example of growth rate calculations
εA = 2.5 kBT
σSC = 1.5 nm
Main Text Backups
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Wires WithinWires
Mansbach,Rachael
Choice ofparameterspace forsweep
Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Dependence of fractal dimension on parameter space
Fractal dimension of region II-A
Approximate length scale of fibril width and monomer packingModerately (anti)correlated with optical cluster growth rate
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Wires WithinWires
Mansbach,Rachael
Choice ofparameterspace forsweep
Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Optical versus contact cluster growth rate
Optical Cluster Growth Rate
Optical vs Contact Cluster Growth Rate
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Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Pareto Optimization
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Wires WithinWires
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Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Mathematical Formulation
Separate contributions
S({nk}) =∑k
nkkB ln
(Ve5/2
Λ3knk
)+∑k
nksk + Ssolv, (1)
U({nk}) =∑k
nkuk + Uinter + Usolv, (2)
Probability of a microstate
P({nk}) =e−β(Usolv−TSsolv)
Q
[∏k
(Ve5/2
Λ3knk
)nk]e−β
∑k nkgk , (3)
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Mansbach,Rachael
Choice ofparameterspace forsweep
Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Probability with respect to a reference state
Reference State
N isolated monomers: ({n1 = N, ni = 0}, i > 1)
Probability
P({nk})
P(n1 = N)=
[∏k
(Ve5/2
Λ3knk
)nk]e−β
∑k nk gk
(Ve5/2
Λ31N
)Ne−βNg1
(4)
= e−β(∑
k nk gk−Ng1) NN ∏k k
32nk
e52 (N−
∑k nk ) ∏
k nnkk
(Λ1
L
)3(N−∑
k nk )
, (5)
ln
[P({nk})
P(n1 = N)
]=− β
∑k
nkgk − Ng1
+ 3
N −∑k
nk
ln
(Λ1
L
)
+ N ln N −5
2
N −∑k
nk
+∑k
3
2nk ln k −
∑k
nk ln nk .
(6)
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Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Thermodynamic limit
Fixed number concentration
ρ ≡ NV
Mass fraction
fk ≡ knkN∑
k fk = 1, fk ∈ [0, 1]∀k
Probability
ln
[P({fk})P(f1 = 1)
]=− Nβ
(∑k
fkgk
k− g1
)+ 3N
(1−
∑k
fk
k
)ln(ρ1/3Λ1
)
+ N∑k
fk
kln
(k5/2
fk
)−
5
2N
(1−
∑k
fk
k
).
(7)
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Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Constrained optimization
Free energy of formation
gk = ∆gk + kg1, (8)
∑k
fkgk
k− g1 =
∑k
fk∆gk
k. (9)
Most probable mass fraction in the thermodynamic limit
{fk}∗ = max{fk}
−β∑k
fk∆gk
k+∑k
fk
k ln
(k5/2e5/2
fkρΛ31
) + ln(ρΛ3
1
)−
5
2
(10)
= max{fk}
[−β∑k
fk∆gk
k+∑k
fk
kln
(k5/2e5/2
fkρΛ31
)], (11)
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Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Approximation of most probable mass fraction for DFAG
Model Parameters
∆g1 ≡ 0 (12)
∆g2 = −14.5 kBT (13)
∆gk = ∆g2 + (k − 2)(−25 kBT )(14)
ρ = 2.6497× 1027 m−3 (15)
T = 298K (16)
mmon = 1151.2 g-mol−1 (17)
Λ1 = 2.9807× 10−12 m−1 (18)
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Examples ofsingle-parametercomputations
Additional data
Ideal Gas Modelof Aggregation
Dependence of growth and alignment on free energies
Threshold of large-scale aggregation may coincide with good core alignment
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