Models are one of the central elements in VLMP. These components add particles to the simulation and
can also include potentials and new particle types.
VLMP offers a wide range of models, from simple polymer models like WLC to coarse-grained models for large protein complexes like viruses.
Let’s take the MADna model for coarse-grained DNA as an example:
As observed, this component takes the data necessary to generate the particles and the required potentials of the MADna model,
such as the sequence or Debye length. However, it’s important to note what is happening underneath.
The model defines the types. In this case, it adds the types of the beads in the model.
If the “basic” type is selected, the model will add the corresponding information for each bead, such as mass, radius, and charge.
Subsequently, it will add the particles, first their position and then the definition of the topology.
Each particle is assigned a type and structural information such as the base pair to which it belongs or the strand.
Once this is done, the model will add the potentials.
In this case, bonds for pairs, angular and dihedral potentials, and non-bonded interactions like the WCA or Debye-Huckel interaction.
All this information is necessary to conduct the simulation with the model.
An important feature of models is that they can define selections.
Selections can be used by other categories of components (modelOperations, modelExtensions, or simulationStep)
to apply transformations or measure properties of certain particle groups.
The MADna model defines several selections. For example, one of them is the “basePairIndex” selection that allows selecting
base pairs according to their pair indices. For instance, the basePairIndex 2 will be the particles belonging to the second base pair.
It is important to note that different models can be combined. We can even place the same model several times:
CORONAVIRUS model for simulating virus-like particles. This model combines two coarse-grained approaches to represent the complex structure of coronaviruses:
Shape-Based Coarse-Grained (SBCG) model for proteins: Used to represent the viral proteins, particularly the spike proteins. This approach maintains the overall shape and essential features of proteins while reducing computational complexity [Arkhipov2006].
One-particle-thick, solvent-free, coarse-grained model for lipid membranes: Employed to simulate the viral envelope. This model represents lipids as single particles, allowing for efficient simulation of membrane dynamics without explicit solvent [Yuan2010].
The combination of these models enables the simulation of large-scale viral structures and their interactions with the environment, balancing computational efficiency with biological accuracy.
The model allows customization of various parameters including the number of lipids, vesicle radius, and number of spike proteins. It also incorporates different interaction potentials for lipid-lipid, protein-protein, and lipid-protein interactions, as well as the option to include a simulated surface for studying virus-surface interactions.
This model is particularly useful for studying the structural dynamics of coronaviruses and interactions with cellular membranes or other surfaces.
Yuan, H., Huang, C., Li, J., Lykotrafitis, G., & Zhang, S. (2010). One-particle-thick, solvent-free, coarse-grained model for biological and biomimetic fluid membranes. Physical Review E, 82(1), 011905.
Arkhipov2006
Arkhipov, A., Freddolino, P. L., & Schulten, K. (2006). Stability and dynamics of virus capsids described by coarse-grained modeling. Structure, 14(12), 1767-1777.
Elastic Network Model (ENM) for protein simulations. This model implements a coarse-grained representation of proteins, typically using one bead per residue (usually the alpha-carbon). ENM is based on the assumption that protein dynamics can be described by harmonic potentials between nearby residues.
The model constructs a network of springs between residues within a certain cutoff distance. This approach allows for efficient simulation of large-scale protein motions and normal modes, making it particularly useful for studying protein flexibility and conformational changes.
The model can be customized through various parameters, including the spring constant (K) and the cutoff distance for interactions (enmCut). These parameters control the strength of the harmonic interactions and the connectivity of the network, respectively.
The protein structure is input via a PDB file, which can be either a local file or downloaded from the RCSB PDB database if a valid PDB ID is provided.
This implementation also includes options for centering the input structure and handling multiple chains, making it versatile for various protein simulation scenarios.
This model uses the [pyGrained] library to create the ENM representation.
Tirion, M. M. (1996). Large Amplitude Elastic Motions in Proteins from a Single-Parameter, Atomic Analysis. Physical Review Letters, 77(9), 1905–1908.
Atilgan2001
Atilgan, A. R., Durell, S. R., Jernigan, R. L., Demirel, M. C., Keskin, O., & Bahar, I. (2001). Anisotropy of Fluctuation Dynamics of Proteins with an Elastic Network Model. Biophysical Journal, 80(1), 505–515.
FILE model for loading pre-existing simulation configurations. This model allows users to import a complete simulation setup from a JSON file, including particle positions, types, and force field parameters. It’s particularly useful for continuing simulations from a previous state or for setting up complex initial configurations.
The model reads all necessary information from the input file, including: - Particle types and their properties
Particle positions and other state variables
Structure information (particle IDs, types, etc.)
Force field parameters and interactions
This approach provides flexibility in setting up simulations, as users can manually create or modify the input files to achieve specific initial conditions or system configurations. It also allows for easy sharing and reproduction of simulation setups.
The FILE model includes an option to selectively remove certain types of interactions from the imported force field.
HELIX model for simulating helical polymer structures. This model is designed to create and simulate helical polymers, with a particular focus on representing the structures which emerge from the self-assembly of helical monomers. The model uses a patchy-particle approach to represent the monomers, with each monomer having two patches which represent the interaction sites on the monomer surface. An additional patch is used to represent the surface interaction, this additional patch interacts only with the surface and not with other monomers.
The helical shape is achived fixing the relative orientation of the monomers, when patches are close enough.
The model generates a helical structure based on specified parameters such as the number of monomers, helix radius, pitch, and helicity. It can be used to study various properties and behaviors of helical polymers, including their dynamics, and the effectos of several polymer interactions.
Key features of the HELIX model include:
Flexible control over helical geometry (radius, pitch, helicity)
Various initialization options (random, line, or pre-formed helix)
Customizable monomer properties and interactions
Support for different variants of the model, including fixed and dynamic versions
Options for simulating interactions with surfaces or other environmental factors
ICOSPHERE model for creating spherical structures based on icosahedron subdivision. This model generates a highly uniform spherical distribution of particles, which is particularly useful for simulating spherical objects or creating starting configurations for various spherical systems.
The model uses the icosphere algorithm, which starts with a regular icosahedron and repeatedly subdivides its faces to create a more refined spherical approximation. This approach ensures a nearly uniform distribution of vertices on the sphere’s surface.
KB (Karanicolas Brooks) model for protein folding simulations. This model implements a structure-based coarse-grained representation of proteins, based on the work of Karanicolas and Brooks. It is particularly effective for studying protein folding mechanisms and dynamics.
The KB model represents each amino acid by a single bead located at the alpha-carbon position. The potential energy function is derived from the native structure of the protein and includes terms for bonds, angles, dihedrals, and non-bonded interactions. This approach allows for efficient simulations while maintaining the essential features of protein folding landscapes.
Key features of the model include:
Coarse-grained representation with one bead per residue
Native-structure-based potential energy function
Efficient simulation of large proteins and long timescales
Option to include solvent-accessible surface area (SASA) calculations
Ability to handle multi-chain proteins and protein complexes
The model reads protein structures from PDB files and can handle both local files and PDB IDs for direct download from the RCSB PDB database.
This model uses the [pyGrained] library to create the coarse-grained representation of the protein.
Karanicolas, J., & Brooks, C. L. (2002). The origins of asymmetry in the folding transition states of protein L and protein G. Protein Science, 11(10), 2351-2361.
Karanicolas2003
Karanicolas, J., & Brooks, C. L. (2003). Improved Gō-like models demonstrate the robustness of protein folding mechanisms towards non-native interactions. Journal of Molecular Biology, 334(2), 309-325.
Karanicolas2003b
Karanicolas, J., & Brooks, C. L. (2003). The structural basis for biphasic kinetics in the folding of the WW domain from a formin-binding protein: Lessons for protein design? Proceedings of the National Academy of Sciences, 100(7), 3954-3959.
MADna model for DNA simulation. This model implements a coarse-grained representation of DNA based on the MADna force field, which provides accurate sequence-dependent conformational and elastic properties of double-stranded DNA. The model offers a balance between computational efficiency and accuracy in representing DNA structure and dynamics.
The model allows for customization of electrostatic interactions through the Debye length and dielectric constant parameters. The debyeFactor can be used to adjust the cutoff distance for these interactions.
A ‘fast’ variant of the model is available, which can be used to speed up simulations at the cost of some accuracy. This variant modifies how non-bonded interactions are computed. Non bonded interactions (WCA and Debye-Hückel) are using bonds. This means that the neighbor list is not used and the interactions pairs are precomputed. Thus, this approach is valid when beads far away in the sequence are kept separated during the simulation. For example, when pulling a DNA strand.
The model reads its core parameters from a JSON file, which can be specified using the inputModelData parameter. This allows for easy modification and extension of the model’s base parameters.
For more details on the underlying force field, see [Assenza2022].
Assenza, S., & Pérez, R. (2022). Accurate Sequence-Dependent Coarse-Grained Model for Conformational and Elastic Properties of Double-Stranded DNA. Journal of Chemical Theory and Computation, 18(5), 3239-3256.
MAGNETICNP model for simulating magnetic nanoparticles. This model implements a coarse-grained representation of magnetic nanoparticles, allowing for the simulation of their behavior under various conditions, including the application of external magnetic fields.
The model represents each nanoparticle as a single entity with properties such as size (hydrodynamic radius), magnetic moment, and anisotropy. This coarse-grained approach enables efficient simulations of large systems of magnetic nanoparticles, making it suitable for studying collective behaviors and responses to external stimuli.
Key features of the model include: - Customizable particle properties (size distribution, magnetic moment, anisotropy)
Options for initial particle orientations (random or aligned)
Flexibility in defining the number of particles and simulation box size
The model allows for easy integration with external field models and can be extended to include various inter-particle interactions.
PARTICLE model for creating a single particle in a simulation. This simple model allows users to add a single particle with specified properties to the simulation environment. It’s particularly useful for creating reference points, probes, or simple objects within a larger simulation context.
The model allows customization of various particle properties including:
Name (type) of the particle
Mass
Radius
Charge
Initial position
This model can be used in conjunction with other models to create more complex systems. It’s especially useful for testing and debugging purposes, or for creating simple scenarios to study specific interactions or behaviors.
SBCG (Shape-Based Coarse-Grained) model for protein simulations. This model implements a coarse-grained representation of proteins that maintains the overall shape and essential features while reducing computational complexity.
The SBCG approach represents proteins using a reduced number of beads, typically one bead for hundreds of atoms, capturing the protein. This reduction in degrees of freedom allows for simulations of larger systems and longer timescales compared to all-atom models.
Key features of the SBCG model include:
Shape-preserving coarse-graining based on the input protein structure
Flexible parameterization of the coarse-graining process
Automatic generation of bonded and non-bonded interactions
Support for multi-chain proteins and protein complexes
The model takes a PDB file as input and generates the coarse-grained representation based on the specified parameters. It can handle various levels of coarse-graining and allows for customization of the interaction potentials.
This model uses the [pyGrained] library to create the SBCG representation.
Arkhipov, A., Freddolino, P. L., & Schulten, K. (2006). Stability and dynamics of virus capsids described by coarse-grained modeling. Structure, 14(12), 1767-1777.
SIMULATION model for loading pre-existing simulation configurations. This model allows users to import a complete simulation setup from a pyUAMMD simulation object, including particle positions, types, and force field parameters. It’s particularly useful for continuing simulations from a previous state or for setting up complex initial configurations.
The model takes all necessary information from imported simulation object, including: - Particle types and their properties
Particle positions and other state variables
Structure information (particle IDs, types, etc.)
Force field parameters and interactions
The SIMULATION model includes an option to selectively remove certain types of interactions from the imported simualtion.
SOP (Self-Organized Polymer) model for protein folding and dynamics simulations. This model implements a coarse-grained representation of proteins based on the self-organized polymer concept, which captures the essential features of protein structure and dynamics while allowing for efficient simulations of large systems and long time scales.
The SOP model represents each amino acid by a single bead, located at the alpha-carbon position. The potential energy function includes terms for native contacts, chain connectivity, and excluded volume interactions. This approach allows for the study of protein folding, unfolding, and large-scale conformational changes.
Key features of the model include: - Coarse-grained representation with one bead per residue
Native-contact-based potential energy function
Efficient simulation of large proteins and long timescales
Ability to handle multi-domain proteins and protein complexes
Option to include solvent-accessible surface area (SASA) calculations
Flexibility for customizing the energy scale of native contacts
The model reads protein structures from PDB files and can handle both local files and PDB IDs for direct download from the RCSB PDB database.
This model uses the [pyGrained] library to create the coarse-grained representation of the protein.
Hyeon, C., & Thirumalai, D. (2006). Forced-unfolding and force-quench refolding of RNA hairpins. Biophysical Journal, 90(10), 3410-3427.
Zhmurov2010
Zhmurov, A., Dima, R. I., Kholodov, Y., & Barsegov, V. (2010). SOP-GPU: Accelerating biomolecular simulations in the centisecond timescale using graphics processors. Proteins: Structure, Function, and Bioinformatics, 78(14), 2984-2999.
Hyeon2011
Hyeon, C., & Thirumalai, D. (2011). Capturing the essence of folding and functions of biomolecules using coarse-grained models. Nature Communications, 2(1), 1-11.
SPHEREMULTIBLOB model for creating spherical multiblob structures. This model generates a spherical particle represented by multiple smaller particles (blobs) arranged on its surface. The spherical particle can be created using either icosphere (a sphere placing blobs on the vertices of an icosahedron, or icosahedron iteratively subdivided) or icododecahedral (a sphere placing blobs on the vertices of an icosidodecahedron) geometry.
The model allows for the creation of spherical structures with varying levels of detail, making it useful for representing large spherical objects in coarse-grained simulations, such as colloidal particles, nanoparticles, or simplified representations of complex biological structures like virus capsids.
Key features of the SPHEREMULTIBLOB model include:
Flexible control over the number of particles representing the sphere
Option to use either icododecahedral or icosphere geometry
Automatic generation of bonds between particles to maintain the spherical structure
Optional steric interactions between particles
This model is particularly useful for studying the behavior of large spherical objects in various environments, their interactions with other particles or surfaces, and for simulations where the internal structure of the sphere needs to be represented explicitly.
STERIC_LAMBDA_SOLVATION model for simulating solvation effects with steric interactions and lambda coupling. This model implements a coarse-grained representation of solvent particles interacting with solute molecules, incorporating both steric repulsion and a lambda parameter for coupling strength.
The model uses a soft-core potential to represent steric interactions, which allows for smooth transitions in free energy calculations. The lambda parameter can be used to gradually turn on or off the interactions, making this model particularly useful for free energy perturbation and thermodynamic integration studies.
WLC (Worm-Like Chain, [WLC]) model for simulating polymer chains, particularly suitable for modeling DNA or other semi-flexible biopolymers. This model implements a discretized version of the continuous worm-like chain, representing the polymer as a series of connected beads with bending rigidity.
The WLC model captures the essential physics of semi-flexible polymers, including their entropic elasticity and persistence length.
Key features of the model include:
Customizable number of beads to represent the polymer chain
Adjustable bond length and bending rigidity
Option to add excluded volume interactions (not included by default)
