SDA 7: A modular and parallel implementation of the simulation of diffusional association software

Abstract

The simulation of diffusional association (SDA) Brownian dynamics software package has been widely used in the study of biomacromolecular association. Initially developed to calculate bimolecular protein–protein association rate constants, it has since been extended to study electron transfer rates, to predict the structures of biomacromolecular complexes, to investigate the adsorption of proteins to inorganic surfaces, and to simulate the dynamics of large systems containing many biomacromolecular solutes, allowing the study of concentration‐dependent effects. These extensions have led to a number of divergent versions of the software. In this article, we report the development of the latest version of the software (SDA 7). This release was developed to consolidate the existing codes into a single framework, while improving the parallelization of the code to better exploit modern multicore shared memory computer architectures. It is built using a modular object‐oriented programming scheme, to allow for easy maintenance and extension of the software, and includes new features, such as adding flexible solute representations. We discuss a number of application examples, which describe some of the methods available in the release, and provide benchmarking data to demonstrate the parallel performance. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Molecular basis of high viscosity in concentrated antibody solutions: Strategies for high concentration drug product development

“…These simulations are performed in absence of the solvent. The effect of solvent molecules is represented by an effective friction force, and a stochastic differential equation instead of Newtonian equation of motion is integrated [65][66][67][68] for the dynamics of the solute molecules. Additionally, a rigid body representation of the solute molecules and simplified forms of force-field parameters can also decrease the demands on computer resources and time.…”

“…Additionally, a rigid body representation of the solute molecules and simplified forms of force-field parameters can also decrease the demands on computer resources and time. 65 Brownian dynamics simulations have been employed to predict diffusion of antibodies in solution. Balbo et al 69 studied a murine antibody (PDB entry: 1IGT) solution at high concentrations (greater than 200 mg per mL) with fluorescence correlation spectroscopy and Brownian dynamics simulations.…”

“…These simulations can also estimate higher-order intermolecular interactions and pairwise potential of mean force. 65,70 For example, Wade and coworkers 70 have used Brownian dynamics simulations to detect concentration-dependent oligomer formation in the solutions of myoglobin, hemoglobin A, and sickle-cell hemoglobin S.…”

Computational Equilibrium Thermodynamic and Kinetic Analysis of K-Ras Dimerization through an Effector Binding Surface Suggests Limited Functional Role

“…We used Brownian dynamics (BD) simulations 31 conducted with the program SDA7 32 to investigate the kinetics of K-Ras dimerization via β 2. Specifically, we ran 1,000,000 independent BD trajectories to calculate the rate constant of dimerization ( k on ).…”

Human DPP III – Keap1 Interactions: A Combined Experimental And Computational Study

“…Rigid-body protein-protein docking was performed using two different web servers with default parameters: GRAMM-X (http://vakser.compbio.ku.edu/resources/gramm/grammx) [25] and webSDA (http://mcm.h-its.org/webSDA). [26,27] Two types of complexes were predicted: open and closed, with DPP III in its either open or closed form (PDB codes 3FVY and 3T6B, respectively), respectively, and the Keap1 Kelch domain (PDB code 1U6D). The best models according to the orientation of the DPP III flexible loop containing ETGE motif and the Keap1 Kelch domain were selected for further simulations.…”