GENESIS Hands-on

Part 2-1

Coarse-Grained Simulations
in GENESIS
Cheng Tan
RIKEN Center for Computational Science
2024/07/01
IUPAB2024 Hands-on Training Program | CHARMM-GUI/GENESIS MD Tutorial

Schedule of GENESIS Tutorials (6/30-7/2)
06/30 Day 1
13:30 – 15:00

GENESIS basics and GENESIS on Fugaku (Kobayashi)
LectureHands-on tutorial on Fugaku

07/01 Day 2
14:30 – 15:30

Coarse-grained simulations in GENESIS (Tan)

15:30 – 16:30

High-performance computation with GENESIS (Jung)

07/02 Day 3
13:30 – 15:00

Generalized-ensemble simulations using GENESIS (Ito)Table of Contents
Part 2-1-I: Coarse-Grained (CG) Models in GENESIS• Introduction to CG• Popular CG Models• CG MD in GENESISPart 2-1-II: GENESIS CG Simulations Hands-on• GENESIS-cg-tool installation• Protein folding simulation• Simple CG MD data analysis

CG: Fewer Details in Picturing MoleculesPeter Wolynes, 2018
「Coarse-Grained Modeling of Biomolecules」???
Anonymous, ~15000 BC

The Young Bull
Paulus Potter, 1647

The Bull
Pablo Picasso, 1945

CG MD Simulations
Task of MD: numerically solving Newton’s equations of motion̈̇𝑀𝑋(𝑡)= −𝛻𝑼 𝑿 + 𝑓(𝑋, 𝑋)

Adapted from S Kmiecik et al. 2016, Chem. Rev.

S Kmiecik et al. 2016, Chem. Rev.;

S Takada et al. 2015 Accounts Chem. Res.

Classification of CG Models
Coarse-graining approaches:Resolution• Bottom-up• Top-down

Rosetta: all-atom bb, Cβ, sc.
PRIMO: 3-bead bb, 1~5-bead sc.
OPEP: all-atom bb, one-bead sc.

Baker

5

MARTINI: 2~4-atom/bead.

Marrink

4

CABS: Cα, Cβ, sc, center-of-bond. Kolinski

3AWSEM: Cα, Cβ, O.

Wolynes
Papoian

2

UNRES: Cα, sc.

Sheraga
Liwo1Kim-Hummer: Cα.
HPS, Mpipi: Cα.
Gō: Cα.

HummerMittalCollepard-Guevara
ClementiBrooks
Takada6• Dynamics-based (physics-based)
Systematic algorithms of AA -> CG mappingo Boltzmann Inversiono Inverse Monte Carlo• Knowledge-based
Fitting experimental macroscopic propertieso Miyazawa-Jernigan
o Kim-Hummer• Structure-based
Assumption of consistency principleo Network models
o Gō models
AJ Pak & GA Voth, Curr. Opin. Struct. Biol. 2018.

# beads per amino acid

Modeling strategies:

Residue-level CGFeigDerreumaux

Residue-Level CG ModelsResidue-level coarse-graining: ~10 atoms / CG particle
1 bead / amino acid

• Protein: AICG2+
W. Li et al. 2014, PNAS.

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

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

all-atomCG• IDP: HPSDignon et al. 2018 PLoS Comput. Biol.

3 beads / nucleotide: Phosphate, Sugar, Base (A,C,G,T)

all-atom• Protein-DNA: PWMcos

CGC.Tan, S. Takada, 2018, JCTC.

AICG2+ Protein Model
Atomic Interaction-based Coarse-Grained (AICG) model
W Li et al., PNAS 2011, 2012, 2014Bonded interactions:••••

Bond length (harmonic, structure based)
1-3 distance (Gaussian, physics + structure based)
Dihedral angle (Gaussian, physics + structure based)
Flexible local potentials (Boltzmann inv., statistical)• Angle• Dihedral

Nonbonded interactions:••

Native contact (12-10)• 𝑟!: native-structure based• 𝜀"# : physics + structure based
Nonnative contact (12-repulsion)• 𝜎$% : knowledge based𝜀!"# for 1-3 distance and dihedral angle, and 𝜀$" :• Energy decomposition from atomistic simulations• Count hydrogen-bond numbers

3SPN.2C DNA Model
3-Site-Per-Nucleotide (3SPN) model for double-stranded DNA (dsDNA)
NonbondedBonded

DM Hinckley et al., JCP 2013
JS Freeman et al., JCP 2014

Fitted experimental properties:•••••

Persistence length
Melting temperature
Hybridization rate constantSequence-dependent curvature
Local base-step flexibility

Weak points:••

High ionic concentration
Single-stranded DNA

HPS/KH IDP Model
Hydrophobicity-scale (HPS) and Kim-Hummer (KH) model for Intrinsically Disordered Protein (IDP)
Calibration: RgDignon et al. 2018 PLoS Comput. Biol.

Variations:Regi et al. 2021 Proteins
Dannenhoffer-Lafage et al. 2021 JPCB
Tesei et al. 2021 PNASRegi et al. 2020 NAR (protein and RNA)Electrostatic interaction

HPS potential
Ashbaugh-Hatch functional form

Lennard-Jones potential

Slab method: simulating multi-chain systems

CG Models and MD in GENESIS
C.Tan et al. 2022, PLoS Comput. Biol.

Available Models

Information flow

One-line preparation example

GENESIS Online Tutorials Sec. 11
https://www.r-ccs.riken.jp/labs/cbrt/tutorials2022/

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

••••Protein folding
DNA dynamics
Protein-DNA recognition
Phase behaviors of IDP

Possible applications

TDP43 + Hero11

Other possible applications: C.Tan et al. JACS 2016; PNAS 2020.

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

Extra: High-Performance CG MDThis CG tutorial only
applies to ATDYN!

beta version

Memory limit

Load imbalance

GDYN

Dynamic domain
decomposition scheme
to address thenonuniform distribution

Dr. Jaewoon Jung

Jung, Tan, Sugita, Nat. Commun. 2024