Simulation With Arena Kelton Solution Manual .pdf.rar

Fluctuating Finite Element Analysis (FFEA) is a software package designed to perform continuum mechanics simulations of proteins and other globular macromolecules. It combines conventional finite element methods with stochastic thermal noise, and is appropriate for simulations of large proteins and protein complexes at the mesoscale (length-scales in the range of 5 nm to 1 μm), where there is currently a paucity of modelling tools. It requires 3D volumetric information as input, which can be low resolution structural information such as cryo-electron tomography (cryo-ET) maps or much higher resolution atomistic co-ordinates from which volumetric information can be extracted. In this article we introduce our open source software package for performing FFEA simulations which we have released under a GPLv3 license. The software package includes a C ++ implementation of FFEA, together with tools to assist the user to set up the system from Electron Microscopy Data Bank (EMDB) or Protein Data Bank (PDB) data files. We also provide a PyMOL plugin to perform basic visualisation and additional Python tools for the analysis of FFEA simulation trajectories. This manuscript provides a basic background to the FFEA method, describing the implementation of the core mechanical model and how intermolecular interactions and the solvent environment are included within this framework. We provide prospective FFEA users with a practical overview of how to set up an FFEA simulation with reference to our publicly available online tutorials and manuals that accompany this first release of the package.

The physical properties of an FFEA protein model share some similarities with Gaussian or Elastic Networks [28], which use a network of beads and harmonic springs to represent the structure and dynamics of proteins, and which therefore capture approximately the elastic component of the FFEA viscoelastic constitutive model. This similarity between conventional finite element analysis and network models has been studied previously [29]. However, while many (although not all) Gaussian/Elastic Network models only include unbreakable harmonic interactions to simplify the solution of the equations of motion, within FFEA we can represent non-bonded interactions between and within individual proteins within a complex. Moreover, in FFEA the volumetric space within each finite element is filled with material, while in particle-spring models it remains empty. For very large macromolecules, especially those containing irregular shapes such as very long coiled-coil regions, it can be difficult to ensure that beads are sufficiently closely spaced within a Gaussian/Elastic network model to maintain the shape of the complex and to prevent steric overlap between the different proteins in the simulation. Continuum FFEA models also naturally include torsional rigidity, which can be particularly important to the dynamics of irregular and non-spherical proteins, and indeed such differences with Elastic Network Models have been shown in the case of Vacuolar-type ATPases [30].

simulation with arena kelton solution manual .pdf.rar

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The tutorial uses the molecular chaperone GroEL as an illustrative example to guide the user through setting up and performing an FFEA simulation. GroEL consists of two heptameric rings built of 14 identical subunits, in total weighing approximately 770kDa. With this weight, and at 12 14 14 nm in size, GroEL would certainly be considered large for conventional atomistic MD simulations with standard computational resources. Structural data is available both at atomic resolution (PDB entry 4HEL), and at lower resolution as an electron density map (EDM) (EMDB entry EMD-5403).

With this first release of the FFEA software, we aim to provide a multi-purpose simulation tool for the biomolecular modelling community to perform dynamic simulations of large protein assemblies based on low resolution structural information, such as is available in the EMDB. The software is released with a user-manual and tutorials, along with a test suite to validate every local install of the program. Planned future developments include improvements to the physics, such as inclusion of long-range hydrodynamics. To broaden the capabilities of FFEA, we are currently implementing the ability to switch between pre-defined protein conformational states, so that we can represent the power-stroke of molecular motors, for example. Moreover, we are introducing rod and sheet elements, in addition to tetrahedra, to facilitate FFEA modelling of one-dimensional objects such as coiled-coils, and two dimensional surfaces such as membranes. These additional capabilities will become available in subsequent software releases. There is also the potential to significantly improve code performance, such as through MPI parallelisation or porting to GPUs. Currently, a typical FFEA simulation running for several days on a desktop linux workstation will contain up to 10 interacting proteins, and will routinely explore μs timescales. In the future, when the software can make use of multiple (e.g 1000) cores or accelerator technologies, we anticipate that far larger systems sizes (e.g 1000 interacting proteins) and longer timescales (up to ms) will be accessible. Given the success that MD simulations at the atomistic level have had in improving our understanding of the biomolecular structures within the PDB, new computational tools that bear an equivalent relationship to the EMDB have the potential to be extremely useful in interpreting the new experimental data from the biological mesoscale. FFEA is released under GPL license, made publicly available at , documentation built automatically and stored at 2ff7e9595c