Wires Within Wires A Minimal Model for Computational Bioelectronic - - PowerPoint PPT Presentation

▶
wires within wires
SMART_READER_LITE
LIVE PREVIEW

Wires Within Wires A Minimal Model for Computational Bioelectronic - - PowerPoint PPT Presentation

Sep 10, 2022 239 likes •609 views

Wires Within Wires A Minimal Model for Computational Bioelectronic Peptide Design R. A. Mansbach 1 A. L. Ferguson 2 1 Physics Department 2 Materials Science Department University of Illinois at Urbana-Champaign Blue Waters Symposium, Sunriver,

slide-1
SLIDE 1

Wires Within Wires

A Minimal Model for Computational Bioelectronic Peptide Design

  • R. A. Mansbach1
  • A. L. Ferguson2

1Physics Department 2Materials Science Department

University of Illinois at Urbana-Champaign

Blue Waters Symposium, Sunriver, OR, June 4, 2018

slide-2
SLIDE 2

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

π-conjugated self-assembling optoelectronic peptides

Wall, Brian D., et al. “Supramolecular Polymorphism: Tunable Electronic Interactions within π-Conjugated Peptide Nanostructures Dictated by Primary Amino Acid Sequence.” Langmuir30.20 (2014): 5946-5956. Galagan, Y.,& Andriessen, R. (2012). “Organic photovoltaics: technologies and manufacturing.” INTECH Open Access Publisher. topic-apple-watch-all.png?itok=OUtlCphV

2 / 15

slide-3
SLIDE 3

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

π-conjugated self-assembling optoelectronic peptides

Wall, Brian D., et al. “Supramolecular Polymorphism: Tunable Electronic Interactions within π-Conjugated Peptide Nanostructures Dictated by Primary Amino Acid Sequence.” Langmuir30.20 (2014): 5946-5956. Galagan, Y.,& Andriessen, R. (2012). “Organic photovoltaics: technologies and manufacturing.” INTECH Open Access Publisher. topic-apple-watch-all.png?itok=OUtlCphV

2 / 15

slide-4
SLIDE 4

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

π-conjugated self-assembling optoelectronic peptides

Wall, Brian D., et al. “Supramolecular Polymorphism: Tunable Electronic Interactions within π-Conjugated Peptide Nanostructures Dictated by Primary Amino Acid Sequence.” Langmuir30.20 (2014): 5946-5956. Galagan, Y.,& Andriessen, R. (2012). “Organic photovoltaics: technologies and manufacturing.” INTECH Open Access Publisher. topic-apple-watch-all.png?itok=OUtlCphV

2 / 15

slide-5
SLIDE 5

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

DXXX series demonstrates hierarchical assembly

Optical Clusters

3 / 15

slide-6
SLIDE 6

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

DXXX series demonstrates hierarchical assembly

Optical Clusters Contact Clusters

3 / 15

slide-7
SLIDE 7

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Reaching longer time and length scales

Minimal coarse-grained model Large computational infrastructure Do parameter sweep over well depths and radii to gain understanding of effect of different interaction parameters on assembly at mesoscale

4 / 15

slide-8
SLIDE 8

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Reaching longer time and length scales

Minimal coarse-grained model Large computational infrastructure Do parameter sweep over well depths and radii to gain understanding of effect of different interaction parameters on assembly at mesoscale

4 / 15

slide-9
SLIDE 9

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Understanding chemical interactions at low resolution

Minimal coarse-grained model Large computational infrastructure Do parameter sweep over well depths and radii to gain understanding of effect of different interaction parameters on assembly at mesoscale

5 / 15

slide-10
SLIDE 10

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Aggregate shape and fractal dimension match previous work

Ardona, Herdeline Ann M., and John D. Tovar. “Energy transfer within responsive π-conjugated coassembled peptide-based nanostructures in aqueous environments.” Chemical Science 6.2 (2015): 1474-1484.

6 / 15

slide-11
SLIDE 11

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Interaction parameters control aggregate morphology

Increasing side chain stickiness increases disorder Side chain size controls transition between flat ribbon and twisted fibril

7 / 15

slide-12
SLIDE 12

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Optical cluster growth is primarily controlled by side chain interactivity

Optical Cluster Growth Increasing side chain well depth increases favorability of side chain–side chain interactions Biggest increase as side chain interactivity decreases below core–core interactivity

8 / 15

slide-13
SLIDE 13

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Side chain radius affects contact cluster growth more strongly

Contact Cluster Growth Fewer configurations Increasing cross-section

9 / 15

slide-14
SLIDE 14

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Identification of optimal parameter sets

Pareto frontier Tradeoff between different objectives

10 / 15

slide-15
SLIDE 15

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Five candidates flagged for future study

11 / 15

slide-16
SLIDE 16

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

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

slide-17
SLIDE 17

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

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

slide-18
SLIDE 18

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

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

slide-19
SLIDE 19

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Why Blue Waters?

Scale of problem

300 simulations of 10,648 monomers Each simulation requires multiple GPU acceleration and produces 10-20 gigabytes of data to be analyzed

Big data research infrastructure Access to broader big data community

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

13 / 15

slide-20
SLIDE 20

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Why Blue Waters?

Scale of problem

300 simulations of 10,648 monomers Each simulation requires multiple GPU acceleration and produces 10-20 gigabytes of data to be analyzed

Big data research infrastructure Access to broader big data community

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

13 / 15

slide-21
SLIDE 21

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Broader Impact

Created a patchy model that recapitulates DXXX properties and reaches mesoscopic scale Showed effects of changing parameter space Identified potential ways to design for optimal parameters Part of a multiscale model for rational peptide design

14 / 15

slide-22
SLIDE 22

Wires Within Wires Mansbach, Rachael Motivation Patchy Model Results Conclusions and Future Work

Acknowledgments

∗

*This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.

15 / 15

slide-23
SLIDE 23

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f Aggregation

Backup Slides

1 / 13

slide-24
SLIDE 24

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f Aggregation

Initial Parameter Sweep: Aromatic Cores

Non cofacial aromatic ǫBB Set to 1 kBT Cofacial aromatic ǫA

Cv

~ 18 kT

Sweep over 2.5-7.5kBT depth

2 / 13

slide-25
SLIDE 25

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f Aggregation

Initial Parameter Sweep: Side Chains

Side chain ǫSC

~ 2 kT

Sweep over 0.2-10 kBT Side chain σSC Sweep over 1.0 -1.75 nm

3 / 13

slide-26
SLIDE 26

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f Aggregation

Example of growth rate calculations

ǫA = 2.5 kBT σSC = 1.5 nm

Main Text Backups 4 / 13

slide-27
SLIDE 27

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f Aggregation

Dependence of fractal dimension on parameter space

Fractal dimension of region II-A Approximate length scale of fibril width and monomer packing Moderately (anti)correlated with optical cluster growth rate

Main Text Backups 5 / 13

slide-28
SLIDE 28

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f Aggregation

Optical versus contact cluster growth rate

Optical Cluster Growth Rate Optical vs Contact Cluster Growth Rate

6 / 13

slide-29
SLIDE 29

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f Aggregation

Pareto Optimization

Main Text Backups 7 / 13

slide-30
SLIDE 30

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f Aggregation

Mathematical Formulation

Separate contributions S({nk}) =

  • k

nkkB ln

  • Ve5/2

Λ3

knk

  • +
  • 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

Λ3

knk

nk e−β

k nkgk,

(3)

Main Text Backups 8 / 13

slide-31
SLIDE 31

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f 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

Λ3 k nk

nk e−β

k nk gk

  • Ve5/2

Λ3 1N

N e−βNg1 (4) = e−β(

  • k nk gk −Ng1)

NN

k k 3 2 nk

e

5 2 (N− k nk ) k nnk k

Λ1 L 3(N−

k nk )

, (5) ln

  • P({nk })

P(n1 = N)

  • = − β

 

k

nk gk − Ng1   + 3  N −

  • k

nk   ln Λ1 L

  • + N ln N −

5 2  N −

  • k

nk   +

  • k

3 2 nk ln k −

  • k

nk ln nk . (6) Main Text Backups 9 / 13

slide-32
SLIDE 32

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f Aggregation

Thermodynamic limit

Fixed number concentration ρ ≡ N

V

Mass fraction fk ≡ knk

N

  • 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 k ln

  • k5/2

fk

  • − 5

2 N

  • 1 −
  • k

fk k

  • .

(7)

Main Text Backups 10 / 13

slide-33
SLIDE 33

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f 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

fk ρΛ3

1

+ ln

  • ρΛ3

1

  • − 5

2     (10) = max

{fk }

  • −β
  • k

fk∆gk k +

  • k

fk k ln

  • k5/2e5/2

fkρΛ3

1

  • ,

(11)

Main Text Backups 11 / 13

slide-34
SLIDE 34

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f Aggregation

Approximation of most probable mass fraction for DFAG

Model Parameters ∆g1 ≡ (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

slide-35
SLIDE 35

Wires Within Wires Mansbach, Rachael Choice of parameter space for sweep

Examples of single-parameter computations Additional data Ideal Gas Model

  • f Aggregation

Dependence of growth and alignment on free energies

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

Main Text Backups 13 / 13