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Vmd measure mean square displacement9/22/2023 The flexibility and efficiency of low-granularity CG has enabled the study of flagellar motility 20, as well as large-scale DNA dynamics via alternate levels of theory such as worm-like chain modeling 21. Particularly adept at modeling large assemblies 15 as well as processes of self-assembly 16, 17, 18, UCG simulations commonly employ large integration time steps 19 that increase sampling efficiency and aid the resolution of long timescale behavior. On the low-granularity end of the spectrum, so-called ultra coarse-grained (UCG) models map many atoms, sometimes entire protein domains in biomolecular simulation, to a single bead 14. The MARTINI force field contains bond, angle, dihedral, and nonbonded interaction terms that govern model behavior. Parameterized empirically, MARTINI maps four atoms to a single bead. On the high-granularity end of the spectrum, MARTINI 11, 12, 13 is a popular offering. Granularity depends on several factors and is, in general, established by scientists based on the nature of their system and the questions they seek to investigate. In the present context, the term granularity refers to the degree of reduction, i.e., the coarseness of a given model, or how many atoms are mapped to a single bead. In general, coarse-graining refers to mapping, by various criteria, groups of atoms in \(\) to a single position, or bead. Considering our growing understanding of climate change and the extreme energy costs of supercomputing, the latter of which continues to balloon with ever-increasing computing power, we anticipate that dimensionality reduction via CG modeling will remain a staple for decades to come. While the geometric increase in computing power of the late 20th century until the present has made atomistic simulation more computationally tractable, CG modeling has remained a staple of computational science. The scope and strategy of CG simulations have been revolutionized many times over in the last ~50 years and CG modeling has been successfully applied to gas, liquid, and condensed phase systems 5, 6, 7, 8, 9, 10. This practice, referred to generally as coarse-graining (CG), produces models that seek to accurately represent chemical systems with far fewer degrees of freedom than the 3( N atoms − 1) present at atomistic resolution (with periodic boundary conditions) 4. A widely-recognized challenge in the domains of computational biochemistry and physics, which has driven the development of novel hardware 2 and software alike, is the computational complexity of biomolecular systems.Īs early as 1975, dimensionally-reduced descriptions of molecular systems have been employed to lessen the computational cost associated with protein folding simulation 3. Since its inception as an investigatory method, MD simulation has provided high spatial and temporal resolution data of materials, surfaces, and biomolecular systems that complement experimentally derived information. Molecular dynamics (MD) simulations evolve chemical systems over time via integration of Newton’s equations of motion 1. Our approach is available in the Visual Molecular Dynamics (VMD) software suite, and employs a CHARMM-compatible Hamiltonian that enables high-performance simulation in the GPU-resident NAMD3 molecular dynamics engine. We demonstrate our approach with the conical HIV-1 capsid and heteromultimeric cofilin-2 bound actin filaments. Further, we present a method of granularity selection based on charge density Fourier Shell Correlation and have additionally developed a refinement method to optimize, adjust and validate high-granularity models. SBCG2 utilizes a revitalized formulation of the topology representing network which makes high-granularity modeling possible, preserving atomistic details that maintain assembly characteristics. Here, we present substantial advances to the shape based coarse graining (SBCG) method, which we refer to as SBCG2. For large assemblies, ultra coarse models are often knowledge-based, relying on a priori information to parameterize models thus hindering general predictive capability. Dimensionality reduction via coarse grain modeling is a valuable tool in biomolecular research.
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