OpenPathSampling: A Python Framework for Path Sampling Simulations. 1. Basics

Abstract

Transition path sampling techniques allow molecular dynamics simulations of complex systems to focus on rare dynamical events, providing insight into mechanisms and the ability to calculate rates inaccessible by ordinary dynamics simulations. While path sampling algorithms are conceptually as simple as importance sampling Monte Carlo, the technical complexity of their implementation has kept these techniques out of reach of the broad community. Here, we introduce an easy-to-use Python framework called OpenPathSampling (OPS) that facilitates path sampling for (bio)molecular systems with minimal effort and yet is still extensible. Interfaces to OpenMM and an internal dynamics engine for simple models are provided in the initial release, but new molecular simulation packages can easily be added. Multiple ready-to-use transition path sampling methodologies are implemented, including standard transition path sampling (TPS) between reactant and product states and transition interface sampling (TIS) and its replica exchange variant (RETIS), as well as recent multistate and multiset extensions of transition interface sampling (MSTIS, MISTIS). In addition, tools are provided to facilitate the implementation of new path sampling schemes built on basic path sampling components. In this paper, we give an overview of the design of this framework and illustrate the simplicity of applying the available path sampling algorithms to a variety of benchmark problems.

Approximating free energy and committor landscapes in standard transition path sampling using virtual interface exchange

“…8), thus giving a rate of 0.009 ps −1 , which is only a factor of two different from the respective rate of 0.004 ps −1 coming from brute force MD. These results are in fairly good agreement with Refs [40,45]. With this rates at hand, the free energy difference between stable states α and β, estimated as ∆G αβ =-log( k βα k αβ ), is 2.04 kT and 1.72 kT from MD and VIE-TPS respectively.…”

“…TIS also enables a reweighting of the path ensemble, giving access to the free energy landscapes, and committor surfaces [38]. While there are now software packages that can compute the path ensembles in TIS, this requires a more involved set up, compared to straightforward TPS [39][40][41]. Indeed, standard transition path sampling has been the entry point for most studies, and it is the first approach one should try, in particular when confronted with a complex transition for which no detailed mechanistic picture is available.…”

OpenPathSampling: A Python Framework for Path Sampling Simulations. 2. Building and Customizing Path Ensembles and Sample Schemes

“…In Paper 1, we introduced the basic concepts and structure of the OPS framework, discussed its ingredients, and gave a tutorial on how to conduct some standard simulations using the OPS framework. 24 The current work builds heavily on this companion paper, and we refer the reader to ref (24) for full details. The OPS framework facilitates implementation of the three stages in any path sampling study: initialization, sampling, and analysis.…”

“…OPS provides tools for the initialization step and for the analysis. For more details we refer to Paper 1 24 and to the online documentation at .…”

Path sampling simulations reveal how the Q61L mutation restricts the dynamics of KRas

“…The TPS simulations were performed with 166 OpenPathSampling(0.1.0.dev-c192493) [38,39]. Multiple state TPS (MSTPS) [21] was 167 performed with an all-to-all flexible length ensemble, excluding self-transitions.…”