Finding the Optimal Folding Routes of self-entangled Proteins via Coarse-Grained Molecular Dynamics

Among the known protein motifs, several structures exhibit a self-entangled backbone topology. Understanding how polypeptides can efficiently and reproducibly attain such topologies is a crucial biophysical challenge, which might shed new light on our general understanding of protein folding.
In this work we present a molecular dynamics methodology for the study of the possible folding pathways travelled by self-entangled proteins. The method is based on a Coarse-Grained, minimalistic representation of the polypeptide chain, driven by a structure-based angular potential. The heterogeneity of the potentials acting among residues is optimized by means of an evolutionary strategy, aimed at maximizing the folding probability of the model.
The purpose of our approach is two-fold. On the one hand we aim at constructing a simple protein model, capable of attaining the entangled structure in a reproducible and efficient way. On the other hand we mimic an evolutionary mechanism that might have selected a specific folding pathway among the possible routes.
Applying the method to relevant test cases we retrieve indications on the optimal folding pathways of self-entangled proteins, and useful guidelines for simulations via more detailed molecular models