細胞環境を考慮した生体分子シ
ミュレーションの現状と今後
PCクラスタシンポジウム
2013/12/13

理化学研究所杉田 有治

People who are working for the research
Sugita Group in RIKEN (Wako & Kobe)

Feig Group in Michigan State UniversityIssekiTakaharu

MichaelJaewoon••

RIKEN Theoretical Molecular Science Laboratory– Dr. Isseki Yu– Dr. Takaharu Mori (Visiting Scientist in MSU)RIKEN AICS
– Dr. Jaewoon Jung
– Dr. Ryuhei Harada (→ Programming
Environment Research Team)•Michigan State University– Prof. Michael Feig•

RIKEN QBiC (NMR experiment)– Dr. Takanori Kigawa– Dr. Naoya Tochio

Protein Crowding Simulations andComparison with NMR experiments

Why cellular environment is important ?
Inomata et al. Nature (2009)～10 fold
unstable !!

Folded state

Unfolded state

In-cell NMR suggests that conformational stability of ubiquitin incells is lower than in vitro.

≠Protein dynamics
is different from in vitro.

Macromolecular Crowding: Theory vs Experiment★Hard sphere model (Minton,1993)(Entropy-centered model)★NMR amide-exchange experiments ofCI2 (Pielak, 2011)

The limitation
ofthe model

ΔG=ΔH-TΔS stabilization no-effect destabilization
Compact native like structuresare stabilized

Crowding stabilizes or destabilizes
proteins☆Let’s start crowding simulations using atomistic models withexplicit solvents !

Protein Crowding Systems for comparing with NMR
experiments
Feig, Sugita: J. Phys. Chem B (2012) 116, 599-605

CI218K atoms
6K H2O
infinite dilution

CI2 + 8 lysozymes
184K atoms
56K H2O/64 Cl108 g lysozyme/L
7% vol fraction

CI2 + 8 BSAs
835K atoms
253K H2O/136 Na+
104 g BSA/L solution
7% vol. fraction~160 ns MD~250 ns MD~120 ns MD

Protein Stability in NMR vs Protein Fluctuation in MD

stabilization no-effect destabilization
A. Miklos, M. Sarkar, Y. Wang, G. Pielak: JACS (2011) 133, 7116-7120

increased/decreased fluctuations in MD
Feig, Sugita: J. Phys. Chem B (2012) 116, 599-605

Interaction between CI2 and crowder proteins (BSA orLysozyme)
BSAlysozyme

Feig, Sugita: J. Phys. Chem B (2012) 116, 599-605

Interaction between CI2 and lysozyme is stronger than that between CI2 and BSA.

CI2 interaction with crowder proteinson average
1.1 CI2-BSA contacts

2.6 CI2-lysozyme contacts

Feig, Sugita: J. Phys. Chem B (2012) 116, 599-605

Cellular Environment slow down diffusion of proteins
Feig, Sugita: J. Phys. Chem B (2012) 116, 599-605

D = 31.7

D=31.8
D=26.6
D=8.8

CI2WAT

D = 14.2

CI2BSAD = 7.4

D(exp)=15.5

D(exp)=8.2

D(exp)=5.1
CI2LYS

Feig, Sugita: J. Phys. Chem B (2012) 116, 599-605[μm2/s] obtained according to Einstein relationship from slope of mean squaredisplacement vs time.

Crowding Systems with different concentration of proteins

PG0:
1 Protein GDilute

PG1-4:
8 protein GCrowding

PG0

PGVH1-5:
4 protein G + 8 villin
Crowding

PGVH1

PGVH2

PGVH3

Harada, Sugita, Feig, J. Am. Chem. Soc. 2012, 134, 4842-4849

PGVH4

PGVH5

NPT(1bar, 298K, 300ns)

Available volume for water within the first and second
solvation shell vs bulk
Harada, Sugita, Feig, J. Am. Chem. Soc. 2012, 134, 4842-4849

2nd solvationshell(4 Å < r ≤ 7 Å)
[%]
14.4

Bulk
(r >7 Å)[%]0.10

1st solvation
shell(r ≤ 4 Å)
[%]
22.4

PG2

0.14

31.7

20.4

47.9PG3

0.20

44.0

24.6

31.5

PG4

0.30

65.6

26.7

7.7

PGVH1

0.12

28.1

16.3

55.6

PGVH2

0.17

37.7

19.1

43.3

PGVH3

0.25

53.4

22.2

24.5

PGVH4

0.37

76.2

19.6

4.2

PGVH5

0.43

86.4

13.0

0.6

SystemProtein
volumefraction

PG1

63.3

★Almost no room for bulk water in highly crowded conditions(PG4, PGVH4, PGVH5).

Diffusion of Water and Protein Molecules in the Crowding
SystemsMSD(t )  | r (t ' t )  r (t ') |2(Water:PG0-4)
crowdingt'(ProteinG:PG0-4)
dilutecrowding(ProteinG:PGVH1-5)(Water:PGVH1-5)

crowdingcrowding(Villin:PGVH1-5)
crowding★ In crowded environment, diffusion of water and protein significantly slow downHarada, Sugita, Feig, J. Am. Chem. Soc. 2012, 134, 4842-4849

Protein-Protein Interactions in Crowding Systems★Contact maps between protein G and villin ★Representative dimer structures(average minimum distances)(protein G and villin)

Acidic-Basic

crowding

Acidic-Basic

Hydrophobic-Polar

Hydrophobic-Hydrophobic
Harada, Tochio, Kigawa, Sugita, Feig, J. Am. Chem. Soc. 2013, 135, 3696-3701.

Conformational Stability of Villin and Protein-G in different
crowding systems★Free energy landscapes of villin ★Free energy landscapes of protein G
dilute500K

dilute20%vol3%vol20%vol3%vol30%vol10%vol30%vol10%vol14%vol14%volGreen: native
Read: destabilized
Partially unfolded

Harada, Tochio, Kigawa, Sugita, Feig, J. Am. Chem. Soc. 2013, 135, 3696-3701.

Comparison of protein crowding systems
between MD and NMR
V10 and G34 changes their chemical shift due to crowding
Local environment ofV10 and G34 waschanged also in MD
simulations

Blue: Dilute
Red: Crowded

1H, 15N TROSY-HSQC spectra of 15N-labeled villin

1mM villin (green),
32mM villin (blue),
32mM villin with protein G (red)By Dr. Kigawa and Dr. Tochio (RIKEN SSBC)
Harada, Tochio, Kigawa, Sugita, Feig, J. Am. Chem. Soc. 2013, 135, 3696-3701.

New views on macromolecular crowding
Partially
unfolded

ε=80

ε=40-60
Harada, Tochio, Kigawa, Sugita, Feig, J. Am. Chem. Soc. 2013, 135, 3696-3701.

Development of Highly Parallelized
MD Software: GENESISK computer at RIKEN Advanced Institute for
Computational
K computer atScience
RIKEN(AICS)
AICS
Performance of SuperComputer(from Top500)

Recently, the performance ofsupercomputers has been improved
significantly. Top machine in 2013 ismore than 100,000 times faster thanthat in 1993.

K computer (The 4th fastestcomputer)
Peak Performance: 10.51 PFLOPS# of CPU Cores： 705,024
Total Memory：1.41PB（16GB per node）
Network: Tofu: 6D mesh / Torus

GENESIS (Generalized-Ensemble Simulation Systems)
1. Aims at developing efficient and accurate methodologies forfree-energy calculations in biological systems

2. Efficient Parallelization - Suitable for massively parallel
computers, in particular, K computer
3. Applicability for large scale simulation
4. Algorithms coupled with different molecular models such ascoarse-grained, all-atom, and hybrid QM/MM
5. Generalized ensembles like Replica-Exchange Molecular
Dynamics (T-REMD, REUS, MREM, Surface-tension REMD (New!
T.Mori et al. JCTC in press.)
6. Open Source Code from this December

GENESIS (Generalized-Ensemble Simulation Systems)
GENESIS (V1) Development Team• Project Leader: Yuji Sugita• Major Developers: Jaewoon Jung, Takaharu Mori
• Developers: Chigusa Kobayashi, Yasuhiro Matsunaga, Takashi Imai,
Takao Yoda (Nagahama Bio Institute), Norio Takase (Isogo Soft)• Other Contributors: Many members in Sugita Group

Y. Sugita

J. Jung

T. Mori

C. Kobayashi

Y. Matsunaga

New Features of GENESIS (V1)
• Inverse Lookup Table Scheme for Nonbonded Interaction•

J. Jung et al. J.Comp.Chem. 2013, 34, 2412-2420.

• Midpoint Cell Method for Hybrid Parallelization• Fast 3D-FFT Calculations

All-atom MD Simulations ofBacterial Cytoplasm on K computer

Our Target System for All-atom MD Simulation
100nm = 1/10,000mm

animal cell
10μm=1/100 mm

100x100x100 (nm3)100,000,000 atoms（including water）
thousands of proteins !
vast amounts of metabolites !
Mycoplasma
Genitalium（bacteria）

Cytoplasm
The first all-atom MD
simulation of bacteria
cytoplasm

300nm=3/10,000mm

23Mycoplasma genitalium: a bacterium with the smallest known
genomeKarr, J.R., Sanghvi, J.C., Macklin, D.N., Gutschow, M.V., Jacobs, J.M.,
Bolival, B., et al. A Whole-Cell Computational Model Predicts
Phenotype from Genotype. Cell. 2012, 150, 389-401.

Brownian Dynamics Simulations by Elcock et al.
••••
•
•
•

PLoS Computational Biology 2010
80 X 80 X 80 (nm3)
6 micro sec.
50 selected proteins from E. coli
No metabolites/solvents
Effective potential.
Diffusion coefficients are provided
as an input parameters.

•

Focus on Thermodynamics(Protein Stability)
Advanced treatment of molecules(full sampling and full scoring) isrequired to predict protein stability
in crowded conditions.

•Coarse-grained BD Simulations Without Hydrodynamic EffectBrownian Dynamics Simulations by Ando and Skolnick••••••PNAS 2010, 107: 18457-18462
100 X 100 X 100 (nm3).
A virtual cytoplasmic system of E.
coli
Over 1,000 macromolecules
consisting of 15 different
macromolecules in Brownian
Dynamics (BD) simulations (50micro sec)
Focus on the diffusion coefficient
Hydrodynamic interactions greatly
reduce the diffusion coefficientand create collective motions at
cellular concentrations.

BD simulations with hydrodynamic effect on Spherical ModelAcknowledgement
Sugita Group in RIKEN (Wako & Kobe)

Feig Group in Michigan State University

IssekiTakaharu
Jaewoon••Computational Resources– RIKEN RICC– K computer
Research Fund
– SPIRE Field 1– RIKEN Internal funds (QBiC, AICS)

Michael