A Minimal Model for Computational Bioelectronic Peptide ...A Minimal Model for Computational...

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

www.imore.com/sites/imore.com/files/styles/large/public/topic_images/2015/

www.imore.com/sites/imore.com/files/styles/large/public/topic_images/2015/

topic-apple-watch-all.png?itok=OUtlCphV

Wires WithinWires

Mansbach,Rachael

Motivation

Patchy Model

Results


DXXX series demonstrates hierarchical assembly

Optical Clusters

Contact Clusters

3 / 15

Wires WithinWires

Mansbach,Rachael

Motivation

Patchy Model

Results


DXXX series demonstrates hierarchical assembly

Optical Clusters Contact Clusters

3 / 15

Wires WithinWires

Mansbach,Rachael

Motivation

Patchy Model

Results


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


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


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


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


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


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


Identification of optimal parameter sets

Pareto frontier

Tradeoff between different objectives10 / 15

Wires WithinWires

Mansbach,Rachael

Motivation

Patchy Model

Results


Five candidates flagged for future study

11 / 15

Wires WithinWires

Mansbach,Rachael

Motivation

Patchy Model

Results


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


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

https://www.slideshare.net/sergejsgroskovs/pragmatism-philosophy-of-science-lecture-slides

https://www.slideshare.net/sergejsgroskovs/pragmatism-philosophy-of-science-lecture-slides

Wires WithinWires

Mansbach,Rachael

Motivation

Patchy Model

Results


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


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

1 / 13

Wires WithinWires

Mansbach,Rachael



Additional data


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



Additional data


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

3 / 13

Wires WithinWires

Mansbach,Rachael



Additional data


Example of growth rate calculations

εA = 2.5 kBT

σSC = 1.5 nm

Main Text Backups

4 / 13

Wires WithinWires

Mansbach,Rachael



Additional data


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

Main Text Backups5 / 13

Wires WithinWires

Mansbach,Rachael



Additional data


Optical versus contact cluster growth rate

Optical Cluster Growth Rate

Optical vs Contact Cluster Growth Rate

Main Text Backups

6 / 13

Wires WithinWires

Mansbach,Rachael



Additional data


Pareto Optimization

Main Text Backups 7 / 13

Wires WithinWires

Mansbach,Rachael



Additional data


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)


Wires WithinWires

Mansbach,Rachael



Additional data


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)


Wires WithinWires

Mansbach,Rachael



Additional data


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)

Main Text Backups

10 / 13

Wires WithinWires

Mansbach,Rachael



Additional data


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)

Main Text Backups 11 / 13

Wires WithinWires

Mansbach,Rachael



Additional data


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)

Main Text Backups

12 / 13

Wires WithinWires

Mansbach,Rachael



Additional data


Dependence of growth and alignment on free energies

Threshold of large-scale aggregation may coincide with good core alignment

Main Text Backups

13 / 13

A Minimal Model for Computational Bioelectronic Peptide ...A Minimal Model for Computational...

Documents

Transcript of A Minimal Model for Computational Bioelectronic Peptide ...A Minimal Model for Computational...