Workflow in Simulation and ReaDDy Code Design.

<p><b>A:</b> Typical workflow and interplay between file input, file output and modules of ReaDDy. The left side of part A describes input and output functionalities of ReaDDy (sketched files) and how they interplay with code modules (squares). Among these modules, white drawn squares have access to both the particle level but also to information how particles are formed to groups. Grey squares are only based on particles to guarantee high computational efficiency. Modules communicate via interfaces, making them exchangeable. Currently two ReaDDy Core implementations exist, a Brownian dynamics based <i>BD Core</i> and a Monte Carlo based <i>MC Core</i>. The design is intended to encourage the incorporation of third party software to play the <i>Core</i>-role in the ReaDDy framework. <b>B:</b> Detailed view of the interplay between <i>Group/Reaction Module</i> (<i>Gr/Rk Module</i>), the <i>Core</i> module and their submodules during the main iteration loop. Most of the simulation time is spend on incrementing particle positions. As a result, the algorithm will circle between Particle Configuration, Neighbor List and Diffusion Engine (thick black arrows) to propagate diffusing particles. If a possible reaction event between two particle arises, this information is passed to the <i>Gr/Rk Module</i> module and is handled there before according changes of the Particle Configuration end that cycle (dashed arrows).</p

Computation time for selected molecular systems.

<p>Computation time for selected molecular systems with domains. Computations were done on a usual desktop computer with [email protected] GHz and 6.5 GB Ram, time is given in seconds.</p

ReaDDy - A Software for Particle-Based Reaction-Diffusion Dynamics in Crowded Cellular Environments

<div><p>We introduce the software package ReaDDy for simulation of detailed spatiotemporal mechanisms of dynamical processes in the cell, based on reaction-diffusion dynamics with particle resolution. In contrast to other particle-based reaction kinetics programs, ReaDDy supports particle interaction potentials. This permits effects such as space exclusion, molecular crowding and aggregation to be modeled. The biomolecules simulated can be represented as a sphere, or as a more complex geometry such as a domain structure or polymer chain. ReaDDy bridges the gap between small-scale but highly detailed molecular dynamics or Brownian dynamics simulations and large-scale but little-detailed reaction kinetics simulations. ReaDDy has a modular design that enables the exchange of the computing core by efficient platform-specific implementations or dynamical models that are different from Brownian dynamics.</p></div

Clustering error of Ala for and corresponding coarse-grain structures for domains.

<p>The decrement of the clustering error is very steep for and relatively flat afterwards, suggesting that is a good choice for the number of domains (). The molecule is partitioned into its four peptide planes and two end groups containing the C- and N-terminus respectively.</p

Particle Parameters and Resulting Properties of the Benchmark System.

<p>Parameters for particle types , and . : collision radius defining the onset of particle-particle repulsion. : apparent collision radius that arises from both the collision radius and the chosen inter-particle repulsion force constant . : interaction radius for particle-particle reactions. : microscopic diffusion constant.</p

Determination of the Brownian Dynamics Time Step Length .

<p><b>A:</b> Dependency of the computed radial distribution function for different time step lengths . The black line shows the exact of -particles computed by Monte Carlo. The interaction potential was chosen to be a softcore repulsion potential () when their distance is closer than the sum of their collision radii . The colored lines show ’s computed from time discretized Brownian dynamics simulations with different timesteps. <b>B:</b> Root mean square error of the difference between Monte Carlo derived g(r) and the discretized diffusion simulation (displayed in same color code as A).</p

Clustering error for the heptameric subunit of GroEL for .

<p>The cartoon representation of three important structures ( and ) is colored according to the identified domains. For the three functional domains (apical, intermediate and equatorial) in the GroEL subunit are found.</p

CPU Runtimes to Simulate 1

<p>CPU runtimes in hours to simulate 1000 particles at densities of 10% and 30% occupied volume fraction in 3D box- or 2D disk geometry with for 10,000,000 steps.</p>*<p>2D systems will likely represent membrane models of higher viscosity, usually resulting in one order of magnitude smaller diffusion constants. This enables the system to be integrated with a one order of magnitude larger timestep.</p

3D-Benchmark System Setups used in this Study.

<p>The occupied volume fraction ranges from 1% to 50% within a cube of 100 nm edge length. The 30% occupied volume fraction best resembles cytoplasm conditions.</p

Distance deviation matrix for native TTR (top) compared to variants 58Arg (middle) and 58His (bottom).

<p>The mean row value of each matrix indicates flexible regions around reference residues 11, 77, 105, 191 and 220. The corresponding structures are color coded according to the average row value of and show the location of residue 58 (purple). Large values (red) indicate flexible regions, while small values (blue) indicate rigid regions in the dimer. The data suggests that the dimer interface is destabilized for both amylogenic TTR variants of the protein.</p