Coarse-Grained (CG) MD Simulation

From Million-Atom Limit to Billion-Bead Scale. Coarse-Grained. Self-Assembled. Atomistically Bridged.
Martini/SIRAH CG Large-Scale Assembly Multiscale Bridging

CG simulations that capture membrane remodeling, viral particle assembly, and protein complex formation at microsecond-millisecond scales impossible for all-atom methods.

Why Coarse-Grained MD Simulation Is the Critical Foundation

All-atom MD hits a wall at ~500k atoms and microseconds. Seed-stage biotechs studying membrane protein oligomerization or viral envelope dynamics cannot afford the compute or time. Pharma teams need to see large-scale protein-protein assembly, lipid raft formation, or nanoparticle interactions that atomistic methods cannot reach. We deploy Martini and SIRAH coarse-grained models to access milliseconds and millions of beads, then bridge to all-atom for binding site refinement.

What Sets the Platform Apart

Million-Bead Scale

CG resolution accesses systems of 10⁶–10⁷ beads (viral particles, organelle mimics) on standard HPC, reaching millisecond timescales.

Martini + SIRAH Dual Force Fields

Martini 3 for biomolecular self-assembly; SIRAH for water-explicit CG with ionization, enabling pH-dependent and membrane potential simulations.

Systematic Back-Mapping

Automated reverse-mapping from CG to all-atom followed by MD refinement delivers atomistic binding poses for docking and FEP.

Technology Suite

Contents

Coarse-Grained System Construction (Martini, SIRAH)

Martini coarse-grained model showing atom-to-bead mapping of protein, lipid, and membrane components.

Key Features:

  • Martini 3 Mapping — Four-to-one atom-to-bead mapping with optimized interaction matrices for proteins, lipids, nucleic acids, and carbohydrates; validated against thermodynamic data.
  • SIRAH Water-Explicit CG — Coarse-grained model with explicit solvent and ionization, capturing electrostatics and pH effects absent in implicit-solvent Martini.
  • Automated Back-Mapping — Systematic reconstruction of all-atom coordinates from CG trajectories using backward/inflateGRO protocols, followed by energy minimization.
  • Membrane & Vesicle Builder — Automated generation of asymmetric bilayers, nanodiscs, and liposome systems with physiological lipid compositions.

Ideal For: Membrane protein oligomerization; viral particle assembly; lipid nanoparticle formulation; protein-membrane interaction studies.

What We Offer:
Stable, production-ready CG systems with validated mapping and back-mapping protocols. Seed-stage biotechs receive self-assembly trajectories and oligomerization reports without managing million-bead simulations. Pharma teams get back-mapped atomistic structures for binding site analysis.

Large-Scale Dynamics & Self-Assembly

Large-scale CG self-assembly simulation of membrane remodeling and protein complex formation on microsecond timescales.

Key Features:

  • Membrane Remodeling — Simulation of membrane fusion, fission, pore formation, and protein-induced curvature at millisecond scales.
  • Protein Complex Assembly — Spontaneous oligomerization and PPI interface formation from separated subunits, validated against Cryo-EM class averages.
  • Viral Envelope & Capsid Dynamics — Large-scale CG simulations of viral membrane proteins and capsid assembly for antiviral target assessment.
  • Lipid Raft & Nanodomain Formation — Tracking cholesterol and saturated lipid clustering to understand membrane organization and drug partitioning.

Ideal For: Antiviral programs targeting envelope proteins; oncology programs studying receptor tyrosine kinase oligomerization; drug delivery nanoparticle optimization.

What We Offer:
Self-assembly trajectory movies, oligomerization kinetics, and Cryo-EM-correlated structural models. For lead optimization, we identify whether compounds stabilize or disrupt target oligomerization.

Multiscale Bridging (CG to All-Atom)

Multiscale bridging workflow converting coarse-grained trajectories back to all-atom resolution for binding site refinement.

Key Features:

  • Systematic Back-Mapping — Automated conversion of CG trajectories to all-atom resolution with side-chain reconstruction and energy minimization.
  • All-Atom Refinement — GROMACS/AMBER microsecond-scale refinement of back-mapped structures to equilibrate atomistic details.
  • Binding Site Reconstruction — Focused all-atom refinement around ligand binding pockets identified in CG simulations, delivering docking-ready and FEP-ready models.
  • Resolution Exchange MD — Hybrid simulations where binding sites run all-atom while distal regions remain coarse-grained, optimizing cost and accuracy.

Ideal For: Membrane protein drug design where large-scale dynamics matter; protein-protein interface refinement; nanoparticle-protein interaction studies.

What We Offer:
A multiscale pipeline: CG for large-scale sampling, back-mapping for atomistic detail, and all-atom refinement for binding site accuracy. Deliverables include CG trajectories, back-mapped PDBs, and refined docking grids.

Platform Instrumentation

Core Instruments

Instrument Capability
NVIDIA DGX H100 Million-bead CG simulations and back-mapping at scale
GROMACS/AMBER HPC Cluster Martini 3 and SIRAH production runs with automated back-mapping
NVIDIA RTX A6000 Cluster Real-time CG visualization and self-assembly monitoring
Thermo Fisher Krios G4 Cryo-EM validation of CG-predicted oligomeric states
Bruker AVANCE NEO 800 MHz NMR of back-mapped structures for force-field validation
Waters ACQUITY UPLC H-Class Lipid and ligand purity profiling for membrane systems

Standardized Workflow

Project Workflow

A milestone-driven execution system from sequence to multiscale model.

01 Target Review Week 1
02 CG System Setup Week 1–2
03 CG Production Week 2–6
04 Back-Mapping Week 6–7
05 Downstream Handoff Week 7–8

01 Target Review

Deliverable: CG simulation plan + mapping rationale

02 CG System Setup

  • CG mapping and system building (membrane, vesicle, or complex)
  • Solvation and equilibration

Deliverable: Equilibrated CG system

03 CG Production

  • Self-assembly or large-scale dynamics production
  • Oligomerization or remodeling monitoring

Deliverable: Raw CG trajectory + assembly kinetics

04 Back-Mapping

Deliverable: Back-mapped + refined PDBs

05 Downstream Handoff

Deliverable: Downstream package + final report

Sample Requirements

  • Protein/Complex Structure: PDB/AF model or Cryo-EM map
  • Membrane/Lipid Context: Desired lipid composition, asymmetry, or curvature
  • Assembly Goal: Oligomeric state, vesicle size, or nanoparticle formulation
  • Experimental Data: Cryo-EM class averages or SAXS profiles for validation (optional)

Standard Deliverables

  • CG production trajectory (Martini 3 or SIRAH)
  • Self-assembly or oligomerization kinetics report
  • Back-mapped all-atom structures (refined binding sites)
  • Docking-ready and FEP-ready input files
  • Final technical report with SAR recommendations

Frequently Asked Questions

Case Study

Case Study: Reparameterized Martini 3 for Predicting Protein Self-Interaction and Colloidal Stability

Goal: Benchmark a refined Martini 3 coarse-grained force field against experimental second virial coefficients (B22) and NMR diffusion data, validating improved accuracy in predicting protein self-interaction strength, aggregation propensity, and solubility for biopharmaceutical screening.

Key Data:

  • Interaction rescaling: Systematic downscaling of Martini 3 bead parameters (α = 0.8 non-ionic; β = 0.85 ionic) corrected systematic overestimation of protein-protein attraction.
  • Thermodynamic alignment: The reparameterized force field reproduced experimental B22 trends across varying salt concentrations for lysozyme and subtilisin, eliminating the non-physical attraction predicted by the original model.
  • Dynamic validation: Concentration-dependent NMR diffusion coefficients (2–150 mg/mL) were accurately captured by the refined model, while the original force field produced artificial clustering and anomalous diffusion plateaus.

Why it matters: This independent study offers third-party validation that experimentally calibrated coarse-grained MD can reliably predict colloidal stability and self-association behavior—key developability parameters for antibodies and therapeutic proteins. The direct benchmarking against B22 and NMR diffusion metrics establishes a rigorous foundation for deploying CG-MD in early-stage biologics formulation and aggregation-risk screening.

Second virial coefficients (B22) of lysozyme at 0, 100, and 200 mM NaCl

Figure 1. Second virial coefficients (B22) of lysozyme at 0, 100, and 200 mM NaCl. (Binder J.; et al. 2025)

Reference

Binder J.; et al. Enhancing Martini 3 for protein self-interaction simulations. Eur J Pharm Sci. 2025 Jun 1;209:107068.

Ready to Scale Beyond Atomistic Limits?
From million-atom walls to billion-bead landscapes — without a supercomputer budget.
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