  Transitions between different conformational states are ubiquitous in
proteins, being involved in signaling, catalysis and other fundamental
activities in cells. However, modeling those processes is extremely difficult,
due to the need of efficiently exploring a vast conformational space in order
to seek for the actual transition path for systems whose complexity is already
high in the steady states. Here we report a strategy that simplifies this task
attacking the complexity on several sides. We first apply a minimalist
coarse-grained model to Calmodulin, based on an empirical force field with a
partial structural bias, to explore the transition paths between the apo-closed
state and the Ca-bound open state of the protein. We then select representative
structures along the trajectory based on a structural clustering algorithm and
build a cleaned-up trajectory with them. We finally compare this trajectory
with that produced by the online tool MinActionPath, by minimizing the action
integral using a harmonic network model, and with that obtained by the PROMPT
morphing method, based on an optimal mass transportation-type approach
including physical constraints. The comparison is performed both on the
structural and energetic level, using the coarse-grained and the atomistic
force fields upon reconstruction. Our analysis indicates that this method
returns trajectories capable of exploring intermediate states with physical
meaning, retaining a very low computational cost, which can allow systematic
and extensive exploration of the multi-stable proteins transition pathways.
