generating potential energy landscapes for membrane proteins...saucer 𝐸 𝑢𝑛𝑛 ... nsf...

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θ θ' 0 360 360 180 180 0 −1500 −750 0 Min kJ.mol 1 Generating potential energy landscapes for membrane proteins Nandhini Rajagopal and Shikha Nangia Department of Biomedical and Chemical Engineering, Syracuse Biomaterials Institute, Syracuse University Lipid Bilayer Top view Protein-protein interactions have high clinical significance Especially, integral membrane proteins occupy 30-50% of the cell surface area in mammals Mutations in proteins affect protein associations Resulting pathological conditions include neurodegenerative diseases, cancers, autoimmune diseases, genetic diseases, and many more Experimental methods - Resolution - Extraction methods for membrane proteins Methods Results & Analysis θ θ' 0 360 360 180 180 1 110 55 0 Ma x Potential energy 1D variable Funnel Saucer <0 Protein LJ+Coulombic interaction energies for 360×360 rotational states Relative thermodynamic stabilities Visiting frequency of 360×360 rotational states Relative kinetic stabilities Funnels and Saucers References 1. Wassenaar, T. A., Pluhackova, K., Moussatova, A., et. al. High-Throughput Simulations of Dimer and Trimer Assembly of Membrane Proteins. The DAFT Approach. J. Chem. Theory Comput. 2015, 11, 2278−2291. 2. Suzuki, H., Nishizawa, T., Tani, K., Yamazaki, Y., Tamura, A., Ishitani, R., Dohmae, N., Tsukita, S., Nureki, O., Fujiyoshi, Y. Crystal structure of a claudin provides insight into the architecture of tight junctions. Science 2014, 344: 304-307. Syracuse University Research Computing: Academic Virtual Hosting Environment (AVHE) Acknowledgements 3. Hiroshi S., Kazutoshi T., Atsushi T., Sachiko T., Yoshinori FModel for the architecture of claudin-based paracellular ion channels through tight junctions. J. Mol. Biol. 2015, 427(2):291-7. 4. Tamura A, Hayashi H, Imasato M, et al. Loss of claudin-15, but not claudin-2, causes Na+ deficiency and glucose malabsorption in mouse small intestine. Gastroenterology. 2011, 140(3):913-923. 5. Rajagopal, N., Nangia, S., Obtaining Protein Association Energy Landscape (PANEL) for Integral Membrane Proteins. J. Chem. Theory & Comp. 2019. 6. Rajagopal N., Nangia S. Exploring the “Funnels” and “Saucers” of Protein Interaction Landscapes. Manuscript in preparation. θ θ' S1 S3 S4 S5 F2 F1 S3 S4 S2 S2 -1 -0.5 0 0.5 1 S1 S2 S3 S4 S5 F1 F2 Relative Frequency Relative Energy S1 S4 S5 S1 S4 S5 Crystal lattice-based claudin-15 strand Motivation PANEL (Protein AssociatioN Energy Landscape) 360˚ 90˚ 180˚ 270˚ P1 (θ) P2 (θ') Rotational space schematic (Proteins as seen from the top view) Claudin-15 Representative protein 90˚ 180˚ 360˚ 270˚ Uniform stochastic distribution of seed geometries (schematic) Often due to local landscape features, energy and frequency landscapes exhibit mismatch in dimer favorability. Funnels”— thermodynamically favored states Saucers”— kinetically favored states Significant dimer regionsGrids retaining seed geometries greater than initial value throughout the MD simulation Min Energy Max Frequency Computational methods: - Difficult to obtain comprehensive data - High computational cost This work: Exhaustively explore membrane protein interactions Quantify the interaction stability Computationally affordable Identify key dimers Using coarse-grained MD simulations Approach : Rotational space: Defined by axial rotation of an interacting pair of proteins (θ,θ'), in the membrane plane Uniform stochastic distribution: Rotational space (θ,θ’) divided into equal, non-overlapping segments (Ω)each segment Ω i comprised of a selected number of random seed configurations. Equilibrium conformations: Independent and parallel unconstrained MD simulations of seed geometries Non-bonded interaction energy: Computed between P1 and P2 proteins for each equilibrium conformation PANEL Landscapes: Minimum energy and Frequency contour plots for each degree rotation of P1 and P2 S4: Crystal lattice protomeric unit S1 and S5: Dimer orientations across antiparallel strand assembly Role of other Saucers need to be explored. Comprehensive, robust and versatile method to generate potential energy and frequency landscapes Highly parallelized workflow Computationally inexpensive short simulationshigh throughput. Validated for dimer and trimer interactions Enabled Identification physiologically relevant key dimer interfaces Enabled identification of aggregation patterns Applicable to any membrane protein Key dimer orientations Critical need to understand protein interactions Limitations: Funnels and Saucers Minimum Energy Landscape Frequency Landscape Membrane Protein Ranking Conclusions NSF CAREER CBET-1453312

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Page 1: Generating potential energy landscapes for membrane proteins...Saucer 𝐸 𝑢𝑛𝑛 ... NSF CAREER CBET-1453312. Title: PowerPoint Presentation Author: Nandhini Rajagopal Created

θ

θ'

0

360

360

180

180

0

−1500

−750

0

Min

kJ.mol−

1

Generating potential energy landscapes for membrane proteins Nandhini Rajagopal and Shikha Nangia

Department of Biomedical and Chemical Engineering, Syracuse Biomaterials Institute, Syracuse University

Lip

id B

ilaye

r

Top view

• Protein-protein interactions have high clinical significance

• Especially, integral membrane proteins occupy 30-50% of

the cell surface area in mammals

• Mutations in proteins affect protein associations

• Resulting pathological conditions include

neurodegenerative diseases, cancers, autoimmune

diseases, genetic diseases, and many more

• Experimental methods

- Resolution

- Extraction methods

for membrane proteins

Methods

Results & Analysis

θ

θ'

0

360

360

180

180

1

110

55

0

Ma

x

Po

ten

tia

l e

ne

rgy

1D variable

Funnel

Saucer

𝐸𝑓𝑢𝑛𝑛𝑒𝑙 − 𝐸𝑠𝑎𝑢𝑐𝑒𝑟 < 0

• Protein LJ+Coulombic interaction energies

for 360×360 rotational states

• Relative thermodynamic stabilities

• Visiting frequency of 360×360 rotational

states

• Relative kinetic stabilities

Funnels and Saucers

References1. Wassenaar, T. A., Pluhackova, K., Moussatova, A., et. al. High-Throughput Simulations of Dimer and Trimer

Assembly of Membrane Proteins. The DAFT Approach. J. Chem. Theory Comput. 2015, 11, 2278−2291. 2. Suzuki, H., Nishizawa, T., Tani, K., Yamazaki, Y., Tamura, A., Ishitani, R., Dohmae, N., Tsukita, S., Nureki, O.,

Fujiyoshi, Y. Crystal structure of a claudin provides insight into the architecture of tight junctions. Science 2014, 344: 304-307.

Syracuse University Research

Computing: Academic Virtual

Hosting Environment (AVHE)

Acknowledgements3. Hiroshi S., Kazutoshi T., Atsushi T., Sachiko T., Yoshinori FModel for the architecture of claudin-based

paracellular ion channels through tight junctions. J. Mol. Biol. 2015, 427(2):291-7.4. Tamura A, Hayashi H, Imasato M, et al. Loss of claudin-15, but not claudin-2, causes Na+ deficiency and

glucose malabsorption in mouse small intestine. Gastroenterology. 2011, 140(3):913-923.5. Rajagopal, N., Nangia, S., Obtaining Protein Association Energy Landscape (PANEL) for Integral Membrane

Proteins. J. Chem. Theory & Comp. 2019. 6. Rajagopal N., Nangia S. Exploring the “Funnels” and “Saucers” of Protein Interaction Landscapes. Manuscript

in preparation.

θ

θ'

S1

S3

S4

S5

F2

F1

S3

S4

S2

S2

-1

-0.5

0

0.5

1

S1 S2 S3 S4 S5 F1 F2

Re

lative

Fre

qu

en

cy

Re

lative

En

erg

y

S1 S4 S5

S1

S4S5

Crystal lattice-based claudin-15 strand

Motivation

PANEL

(Protein AssociatioN Energy Landscape)

360˚

90˚

180˚

270˚

P1 (θ) P2 (θ')

Rotational space schematic (Proteins as seen

from the top view)

Claudin-15 – Representative protein

90˚

180˚360˚

270˚

Uniform stochastic distribution of seed

geometries (schematic)

Often due to local landscape features,

energy and frequency landscapes exhibit

mismatch in dimer favorability.

• “Funnels”— thermodynamically

favored states

• “Saucers”— kinetically favored states

• Significant dimer regions—Grids

retaining seed geometries greater than

initial value throughout the MD

simulation

Min

Energy

Max

Frequency

• Computational methods:

- Difficult to obtain

comprehensive data

- High computational cost

This work:

• Exhaustively explore

membrane protein

interactions

• Quantify the interaction

stability

• Computationally

affordable

• Identify key dimers

• Using coarse-grained

MD simulations

Approach:

• Rotational space: Defined by axial rotation of an interacting pair of

proteins (θ,θ'), in the membrane plane

• Uniform stochastic distribution: Rotational space (θ,θ’) divided into

equal, non-overlapping segments (Ω)—each segment Ωi comprised of a

selected number of random seed configurations.

• Equilibrium conformations: Independent and parallel unconstrained MD

simulations of seed geometries

• Non-bonded interaction energy:

Computed between P1 and P2 proteins

for each equilibrium conformation

• PANEL Landscapes: Minimum energy

and Frequency contour plots for each

degree rotation of P1 and P2

• S4: Crystal lattice protomeric unit

• S1 and S5: Dimer orientations across antiparallel

strand assembly

• Role of other Saucers need to be explored.

• Comprehensive, robust and versatile method

to generate potential energy and frequency

landscapes

• Highly parallelized workflow

• Computationally inexpensive short

simulations—high throughput.

• Validated for dimer and trimer interactions

• Enabled Identification physiologically

relevant key dimer interfaces

• Enabled identification of aggregation

patterns

• Applicable to any membrane protein

Key dimer orientations

Critical need to understand protein interactions

Limitations:

Funnels and SaucersMinimum Energy Landscape Frequency Landscape

Membrane

Protein

Ranking

Conclusions

NSF CAREER

CBET-1453312