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.
KOBRA (KirchOff Biological Rod Algorithm) is an algorithm and software package designed to perform dynamical simulations of elongated biomolecules such as those containing alpha-helices and coiled-coils. It represents these as...
Rapid advances in experimental biophysical techniques are generating a wealth of information about the mechanical operation of the cellular cytoskeleton and its motors. However, each of these tools typically provides only a limited piece of a highly complex puzzle. There is a need to develop new computational tools that can integrate these data together into a central model.Here we discuss the experimental advances alongside the computational tools, and propose how these could be developed to successfully combine the emerging structural and dynamic experimental data on cytoskeletal motors. We consider examples of both single motors and arrays of motors within a biological cell.
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