Coarse-grained molecular dynamics simulation is a simulation method between all-atom simulation and mesoscopic simulation. Compared with all-atom simulation, it can simulate large-scale dynamic behavior; compared with mesoscopic simulation, it can reflect more microscopic levels. Therefore, coarse-grained molecular dynamics simulation compensates for their respective shortcomings to a certain extent, and is a "bridge" between all-atom simulation and mesoscopic simulation.
Coarse-grained simulation aims to simulate the behavior of complex systems using its coarse-grained (simplified) representation. Coarse-grained models are widely used in molecular modeling of biomolecules of various particle sizes. Various coarse-grained models have been proposed. They are usually dedicated to the computational modeling of specific molecules: proteins, nucleic acids, lipid membranes, carbohydrates or water. In these models, molecules are not represented by individual atoms, but by "pseudo atoms" that approximate groups of atoms, such as entire amino acid residues. By reducing the degrees of freedom, longer simulation times can be studied at the expense of molecular details. Coarse-grained models have found practical applications in molecular dynamics simulations.
Figure 1. Illustration of replica exchange molecular dynamics (REMD) method.
In the latter case, the time step is limited to 1-2 fs, but our CG simulation can be run with a time step of 25-50 fs (because the particle mass is larger and the interaction potential is smoother). The possible speedup depends on the computer power supply at hand. For example, simulating an all-atomic system with 300,000 particles on 48 processors, we obtain dynamics of 0.1 ns in one day. The same system in the CG representation contains 30,000 particles, and the performance achieved in one day is 150 ns. This is because the number of particles per processor is small and the integration time step is large. Therefore, the speedup in this case is 1500 times.
CD ComputaBio established four different AI-based regression models: artificial neural networks, k-nearest neighbors, Gaussian process regression and random forests to map coarse-grained models to all atomic models. The model shows better predictions than existing reverse mapping methods for selected structures, which indicates the application of our model in reverse mapping.
|Coarse-grained dynamics simulations
Self-assembly of lipid molecules
Interaction between lipid bilayer and protein
Membrane protein accumulation on lipid bilayer
|Depends on the time you need to simulate and the time required for the system to reach equilibrium.
|Product delivery mode
|The simulation results provide you with the raw data and analysis results of mbrella sampling simulation.
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