Structural transition states explored with minimalist coarse grained models: applications to Calmodulin

TL;DR

This paper introduces a minimalist coarse-grained modeling approach to efficiently explore conformational transition pathways in proteins, demonstrated on Calmodulin, combining structural clustering and comparison with other methods to identify meaningful intermediate states.

Contribution

The study presents a novel, low-cost strategy for modeling protein conformational transitions using minimalist coarse-grained models and trajectory refinement techniques.

Findings

Method effectively explores intermediate states with physical relevance.

Trajectory comparison shows consistency across different modeling approaches.

Approach significantly reduces computational cost for studying protein transitions.

Abstract

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.