PLUMED is a widely used and versatile rare-event sampling and analysis code that can be used with various Molecular Dynamics (MD) engines. It has a very intuitive and versatile syntax for the definition of Collective Variables (CVs), and a wide variety of sampling methods, which accounts for its widespread use. The present module allows PLUMED and OPS to be used together. More details on the module can be found here.
Practical application and exploitation of the code
Transition path sampling simulations and analysis rely on accurate state definitions. Such states are typically defined as volumes in a Collective Variables space. OPS already supports a number of CVs, including the ones defined in the MDTraj python library. PLUMED offers a wide variety of extra CVs, which are enabled in OPS by this module. Many of PLUMED’s dozens of CVs have a biomolecular focus, but they are also general enough for other applications. PLUMED’s popularity (over 500 citations in 4 years after the release of PLUMED2 ) is greatly based on the fact that it works with many MD codes. OPS is now added to that list. The PLUMED code is well-maintained and documented for both users and developers. Several tutorials and a mailing list are available to address FAQs. More information about PLUMED is available here.
Transition path sampling is most efficient when paths are generated from the top of the free energy barrier. However, complex (biomolecular) activated processes, such as nucleation or protein binding/unbinding, can have asymmetric and peaked barriers. Using uniform selection on these type of processes will not be efficient, as it, on average, results in selected points that are not on the top of the barrier. Paths generated from these points have a low acceptance probability and accepted transition paths decorrelate slowly, resulting in a low overall efficiency. The Spring shooting module was developed to increase the efficiency of path sampling of these types of barriers, without any prior knowledge of the barrier shape. The spring shooting algorithm uses a shooting point selector that is biased with a spring potential. This bias pulls the selection of points towards the transition state at the top of the barrier. The paths that are generated from points selected by this biased selector therefore have an increased acceptance probability and the decorrelation between accepted transition paths is also increased. This results in a higher overall efficiency. The spring shooting algorithm is described in more detail in a paper by Brotzakis and Bolhuis.  This module was developed during the ESDW on classical molecular dynamics held in Amsterdam.