Martin Brieg

SIMONA 1.0: An efficient and versatile framework for stochastic simulations of molecular and nanoscale systems

Molecular simulation methods have increasingly contributed to our understanding of molecular and nanoscale systems. However, the family of Monte Carlo techniques has taken a backseat to molecular dynamics based methods, which is also reflected in the number of available simulation packages. Here, we report the development of a generic, versatile simulation package for stochastic simulations and demonstrate its application to protein conformational change, protein–protein association, small‐molecule protein docking, and simulation of the growth of nanoscale clusters of organic molecules. Simulation of molecular and nanoscale systems (SIMONA) is easy to use for standard simulations via a graphical user interface and highly parallel both via MPI and the use of graphical processors. It is also extendable to many additional simulations types. Being freely available to academic users, we hope it will enable a large community of researchers in the life‐ and materials‐sciences to use and extend SIMONA in the future. SIMONA is available for download under http://int.kit.edu/nanosim/simona.

PowerBorn: A Barnes-Hut tree implementation for accurate and efficient born radii computation

Implicit solvent models are one of the standard tools in computational biophysics. While Poisson-Boltzmann methods offer highly accurate results within this framework, generalized Born models have been used due to their higher computational efficiency in many (bio)molecular simulations, where computational power is a limiting factor. In recent years, there have been remarkable advances to reduce some deficiencies in the generalized Born models. On the other hand, these advances come at an increased computational cost that contrasts the reasons for choosing generalized Born models over Poisson-Boltzmann methods. To address this performance issue, we present a new algorithm for Born radii computation, one performance critical part in the evaluation of generalized Born models, which is based on a Barnes-Hut tree code scheme. We show that an implementation of this algorithm provides accurate Born radii and polar solvation free energies in comparison to Poisson-Boltzmann computations, while delivering up to an order of magnitude better performance over existing, similarly accurate methods. The C++ implementation of this algorithm will be available at http://www.int.kit.edu/nanosim/ .

SLIM: An improved generalized Born implicit membrane model

In most implicit continuum models, membranes are represented as heterogeneous dielectric environments, but their treatment within computationally efficient generalized Born (GB) models is challenging. Despite several previous attempts, an adequate description of multiple dielectric regions in implicit GB‐based membrane models that reproduce the qualitative and quantitative features of Poisson–Boltzmann (PB) electrostatics remains an unmet prerequisite of qualitatively correct implicit membrane models. A novel scheme (SLIM) to decompose one environment consisting of multiple dielectric regions into a sum of multiple environments consisting only of two dielectric regions each is proposed to solve this issue. These simpler environments can be treated with established GB methods. This approach captures qualitative features of PB electrostatic that are not present in previous models. Simulations of three membrane proteins demonstrate that this model correctly reproduces known properties of these proteins in agreement with experimental or other computational studies.