2023年度 第2回計算科学フォーラム

Developments and Applications in Multiscale
Molecular Dynamics Simulations for
Biomolecular Condensation Using GENESIS
Cheng Tan
Computational Biophysics Research Team, RIKEN Center for Computational Science
2024-03-22 Friday

Outline

Led by Dr. Yuji Sugita• Developments: A User-Friendly, High-Performance CG MD Software.
C.Tan et al. JCP, 2020; PLoS Comput. Biol., 2022.

J. Jung, C.Tan, Y. Sugita, in revision, 2024.

• Applications: A Comprehensive Exploration of the Regulation of LLPS.
C.Tan, A. Niitsu, Y. Sugita, JACS Au, 2023.

Liquid-Liquid Phase Separation (LLPS) in Biology
Physics

Biology

What is LLPS? 「相分離」

Olive oil with Balsamic Vinegar
Wikipedia, by Leon Brocard

Brangwynne CP, Hyman AA, et al. 2009 Science

Same thing, different names:Banani et al. 2017 Nature Rev. Mol. Cell Biol.

“droplet”, “condensate”, “granule”, “membraneless organelle”

Biological Problem: Condensation Regulation“Good” phase separation“Bad” phase separation

Regulation by “Hero” proteins

A. Prasad et al. 2018, Front. Mol. Neurosci.

Tsuboyama et al. 2020 PLoS Biol.

Removepositivecharges

S. Alberti et al. 2019, Cell.

Question 1: What’s the driving force of the passive regulation?

Theoretical Frameworks of Biomolecular LLPS
“Stickers-and-spacers”“Scaffold-and-client”

Choi et al. 2020 Annu Rev Biophys

Banani et al. 2016 Cell

Martin et al. 2020 Science

Clients are not always facilitators Ruff et al. 2021 PNAS
Most existing theories only talk about attractive interactions!

Question 2: What’s the role of repulsive interactionsin the regulation of LLPS?

Multi-scale Simulations of Biomolecular Systems
Task of MD: numerically solving Newton’s equations of motion̈ = −𝛻𝑼 𝑿 + 𝑓(𝑋, 𝑋)
̇𝑀𝑋(𝑡)
Potential energy 𝑈(𝑋) determines accruracy and efficiency.

A. Kolinski et al. 2016, Chem. Rev.;

S Takada et al. 2015 Accounts Chem. Res.

Choose a suitable model for target system
Liquid-Liquid Phase Separation(LLPS)•••
Adapted from A. Kolinski et al. 2016, Chem. Rev.

Length-scale: 0.1-10μm
Time-scale: ms-s
Number of proteins: 103-105

C.D. Crowe et al. 2018, Interface Focus

Popular Coarse-grained Models for Biomolecules
Residue-level coarse-graining: ~10 atoms / CG particle• Protein: AICG2+

1 bead / amino acidW. Li et al. 2014, PNAS.

• DNA: 3SPN.2C
G. Freeman et al. 2014, JCP.

• RNA: Go-like
N. Hori et al. 2012, JCTC.

3 beads / nucleotide: Phosphate, Sugar, Base
3 beads / nucleic acid

Popular Coarse-grained Models for Biomolecules
Protein-DNA:PWMcosC.Tan, S. Takada, 2018, JCTC.

Residue-level CG models: protein-DNA systems~500 particles~2,000 particles

C.Tan, et al. 2016, JACS.

~1,000 particles

K. Kamagata et al. 2020, NAR; 2021, NAR.

Thanks to:

Shoji Takada
Giovanni Brandani@ Kyoto Univ.
C.Tan, S. Takada, 2020, PNAS.

HPS/KH Models for IDP

Dignon et al. 2018 PLoS Comput. Biol.

Model resolution and potential function

Calibration: Rg

Electrostatics

Hydrophobicity scale (HPS) model
Ashbaugh-Hatch functional form

Lennard-Jones potential

Slab method: simulating multi-chain systems

FUS WT and mutantsDifficulties of Carrying out Coarse-grained MD“Barriers” of running CG MD simulations
1. To combine different models••••

Protein model: by Takada-group@Kyoto U.
DNA model: by de Pablo-group@U. Chicago
IDP model: by Mittal-group@Texas A&M U.
…

2. To apply to large-scale simulations•••

Memory limit using atomic decomposition
Computational efficiency
Robustness and accuracy

Part I: Implementation of CG ModelsinATDYN
Jung et al. 2015 WIREs Comput. Mol. Sci.
Kobayashi et al. 2017 J. Comput. Chem.

Implementation of CG Models in GENESISInteraction potentials

Information flow of CG Sim.

C.Tan et al. 2022, PLoS Comput. Biol.

File preparation example

Improve Computational Efficiency in CG Simulations
Neighbor list design
Cell-linked lists

𝑶(𝑵𝟐 ) → 𝑶(𝑵)

Memory and CPU benchmark

C.Tan et al. 2022, PLoS Comput. Biol.

Applications

Popular Coarse-grained Models for Biomolecules“Barriers” of running CG MD simulations

Tsuboyama et al. 2020 PLoS Biol.

✅ 1. To combine different models••••

Protein model: by Takada-group@Kyoto U.
DNA model: by de Pablo-group@U. Chicago
IDP model: by Mittal-group@Texas A&M U.
…

✅ 2. To apply to large-scale simulations•••

Memory limit for atomistic decomposition
Computational efficiency
Robustness and accuracy

Part I: Implementation of CG Models in GENESISPart II: Application of GENESIS MD to HERO11on supercomputers (Fugaku, Hokusai BW)

C.Tan, A. Niitsu, Y. Sugita, JACS Au, 2023.

Testify Hero11 α-helices Stability using Atomistic MD
Hero11-WT by AlphaFold2

N-term

IDR propensity by IUPred3, flDPnn, PONDR

Jumper et al. 2021 Nature

AF Database ID: Q9UNZ5

α1: 17-25

Dr. Ai Niitsu

C-termα2: 38-72

Erdos et al. 2021 Nucleic Acids Res.
Hu et al. 2021 Nature Commun.
Peng et al. 2006 BMC Bioinfo.

• AF-Predicted α-helix
can be unstable• Convergence inatomistic MD takes
long timeAll-atom MD simulations: CHARMM36m Force Field Huang et al. 2017 Nature Methods
T=300K, IC=150mM

CG Modeling of Hero11 and TDP-43

Hero11C.Tan, A. Niitsu, Y. Sugita, JACS Au, 2023.

TDP-43-LCD

regulate

CG Models:• IDR: HPS(main contributors for LLPS)
Dignon et al. 2018 PLoS Comput Biol.

• folded domains: AICG2+(maintain α-helical structure)
Li et al. 2014 PNAS

Hero11-WT-α

Hero11-WT-noα

Homotypic Condensation of TDP-43
C.Tan, A. Niitsu, Y. Sugita, JACS Au, 2023.

••••

100 TDP-43
180×180×3000Å3T=250~350K
IC=150mM“Chain Collapse Theory”
Consistent with Conicella et al. 2020 PNAS

Chou et al. 2006 J. Phys. Chem. Lett.

Homotypic Condensation of Hero11 WT-α and WT-noα••••

100 Hero11-WT-α180×180×2000Å3
T=50~350K
IC=150mM

𝑻𝑪 ≪ 𝟐𝟗𝟓𝑲

Hero11: low critical temperature

C.Tan, A. Niitsu, Y. Sugita, JACS Au, 2023.

Heterotypic Condensation of TDP-43 + Hero11-no-α
C.Tan, A. Niitsu, Y. Sugita, JACS Au, 2023.

Hero11 lowers TC of TDP-43

Structure, dynamics, interaction of Hero11-no-α
C.Tan, A. Niitsu, Y. Sugita, JACS Au, 2023.
and TDP-43 in two phases

Effects of α-helical secondary structure in Hero11
C.Tan, A. Niitsu, Y. Sugita, JACS Au, 2023.

Distribution of Hero11:• α-helical model:interial of condensate• IDP model:
surface of condensate

Surface electrostatics & fusion propensity
Welsh et al. 2022 Nano Lett.

Summary: Hero11’s LLPS-Regulation Mechanisms
C.Tan, A. Niitsu, Y. Sugita, JACS Au, 2023.

Welsh et al. 2022 Nano Lett.

Remaining Question:
How to verify mechanism no. 3?
• We need a more powerful tool!

Improve Computational Efficiency in CG Simulations

GDYN

Dynamic domain
decomposition scheme
to address thenonuniform distribution

Dr. Jaewoon Jung

Jung, Tan, & Sugita, bioRxiv, 2023

CG MD of Droplets Going to Ultra-Large ScalesComponent: TDP-43-LCD• 𝑛"#$%& = 1,000• 1000Å×1000Å×1500Å

Jung, Tan, & Sugita, bioRxiv, 2023

Simulations carried out on Fugaku• 𝑛"#$%& = 16,657• 2087Å×2067Å×2077Å

CG MD of Droplets Going to Ultra-Large Scales

The first direct observation of“Ostwald ripening”

• Developments of CG in GENESIS
User-friendly high-performance simulation tool

C.Tan et al. PLoS Comput. Biol., 2022.

• Regulation of biomolecular condensation
High-charged IDPs and their interactions regulate LLPS

C.Tan, A. Niitsu, Y. Sugita, JACS Au, 2023.

Reconstruction of Atomistic Models fromCG Simulations with GENESIS

Many tools: PULCHRA,BBQ, PD2, SCWRL…

Mori et al. JCIM (2021)

C. Tan, Y. Sugita, in preparation

All-atom simulations of TDP43-Hero11 Condensates

Movie length: ~1.94 μs
Box size: 180Å × 180Å × 600Å
TDP43 shown with PBC images

Composition:• 100 TDP43• 17 Hero11• 563,247 waters• 1,756 Na• 2,447 Cl

Acknowledgements
RIKEN• Yuji Sugita• Jaewoon Jung
• Chigusa Kobayashi• Diego Ugarte La Torre• Ai Niitsu• Shintaro Iwasaki
U. Tokyo• Yukihide Tomari• Kotaro Tsuboyama• Selena Setsu• Andy Lam

Kyoto U.
• Shoji Takada• Giovanni Brandani• Azuki Mizutani
Hokkaido U.
• Shinichi Nakagawa
Nagahama Inst. Bio-Sci. Tech.
• Takao Yoda