16.2 Coarse-graining methods and force field development

Coarse-graining methods simplify complex molecular systems by grouping atoms into larger units. This approach reduces computational costs, allowing simulations of larger systems over longer timescales. It's a key technique in multiscale modeling, bridging atomic and macroscopic scales.

Force field development is crucial for accurate coarse-grained simulations. It involves creating effective potentials that capture essential interactions between coarse-grained particles. These force fields, like Martini, are optimized using various methods to balance accuracy and transferability across different systems.

Coarse-Grained Models

Bead and United-Atom Models

Top images from around the web for Bead and United-Atom Models

• 

  Labels | Computational Chemistry Resources View original

  Is this image relevant?

• 

  Frontiers | Computational Identification of Functional Centers in Complex Proteins: A Step-by ... View original

  Is this image relevant?

• 

  Frontiers | MiMiC: Multiscale Modeling in Computational Chemistry | Molecular Biosciences View original

  Is this image relevant?

• 

  Labels | Computational Chemistry Resources View original

  Is this image relevant?

• 

  Frontiers | Computational Identification of Functional Centers in Complex Proteins: A Step-by ... View original

  Is this image relevant?

1 of 3

Top images from around the web for Bead and United-Atom Models

• 

  Labels | Computational Chemistry Resources View original

  Is this image relevant?

• 

  Frontiers | Computational Identification of Functional Centers in Complex Proteins: A Step-by ... View original

  Is this image relevant?

• 

  Frontiers | MiMiC: Multiscale Modeling in Computational Chemistry | Molecular Biosciences View original

  Is this image relevant?

• 

  Labels | Computational Chemistry Resources View original

  Is this image relevant?

• 

  Frontiers | Computational Identification of Functional Centers in Complex Proteins: A Step-by ... View original

  Is this image relevant?

1 of 3

• Bead models represent groups of atoms as single particles, reducing computational complexity
• Simplifies molecular structures while preserving essential features
• United-atom models treat non-polar hydrogen atoms and their bonded carbons as single units
• Reduces degrees of freedom in simulations, enabling longer timescales and larger systems
• Bead models often used for proteins, lipids, and polymers (DNA, RNA)
• United-atom models commonly applied to hydrocarbons and organic molecules

Mapping Schemes and Effective Potentials

• Mapping schemes define how atomic-level structures translate to coarse-grained representations
• Center of mass mapping assigns beads to centers of mass of atomic groups
• Geometric center mapping places beads at geometric centers of atomic clusters
• Effective potentials describe interactions between coarse-grained particles
• Bonded potentials maintain molecular structure (bond stretching, angle bending, torsions)
• Non-bonded potentials capture intermolecular forces (van der Waals, electrostatics)
• Potentials often derived from atomistic simulations or experimental data

Force Field Development

Martini Force Field

• Martini force field widely used for biomolecular simulations
• Employs a four-to-one mapping scheme, grouping four heavy atoms into one bead
• Classifies beads into four main types: polar, nonpolar, apolar, and charged
• Subtypes within each category account for hydrogen bonding capabilities
• Includes specific bead types for ring structures and ions
• Martini 3.0 introduces enhanced bead types and improved protein modeling

Parameterization and Transferability

• Parameterization involves determining optimal parameters for coarse-grained force fields
• Bottom-up approach derives parameters from atomistic simulations
• Top-down approach fits parameters to reproduce experimental data
• Hybrid methods combine bottom-up and top-down approaches for balanced accuracy
• Transferability measures a force field's applicability across different molecules and conditions
• Highly transferable force fields reduce need for system-specific reparameterization
• Trade-off exists between transferability and accuracy for specific systems

Optimization Methods

Iterative Boltzmann Inversion

• Iterative method to derive coarse-grained potentials from target distribution functions
• Starts with initial guess for potential, often from atomistic simulations
• Iteratively updates potential to match target distribution function
• Commonly used target functions include radial distribution functions and angle distributions
• Convergence typically achieved within 5-10 iterations for simple systems
• Can be computationally expensive for complex, multi-component systems

Force Matching and Relative Entropy Minimization

• Force matching aims to reproduce forces from atomistic simulations in coarse-grained models
• Minimizes difference between coarse-grained and atomistic forces using least squares optimization
• Applicable to both bonded and non-bonded interactions
• Relative entropy minimization optimizes coarse-grained models by minimizing information loss
• Measures difference between coarse-grained and atomistic probability distributions
• Combines aspects of both structural and thermodynamic property matching
• Provides systematic framework for developing transferable coarse-grained models