Evolutionary Switches Structural Transitions via Coarse-Grained Models

Abstract

Transitions between different conformational states are ubiquitous in proteins. A vast class of conformation-changing proteins includes evolutionary switches, which vary their conformation as an effect of few mutations or weak environmental variations. However, modeling those processes is extremely difficult due to the need of efficiently exploring a vast conformational space to look for the actual transition path. In this study, we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to the protein, based on an empirical force field with a partial structural bias toward one or both the reference structures. We then explore the transition paths by means of stochastic molecular dynamics and select representative structures by means of a principal path-based clustering algorithm. We finally compare this trajectory with that produced by independent methods adopting a morphing-oriented approach. Our analysis indicates that the minimalist model 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 multistable proteins transition pathways.

Heat‐Driven Iontronic Nanotransistors

“…Atomistic trajectories were then used to evaluate the pair distribution functions to calibrate a Coarse Grained Force Field (CG-FF) based on a single bead per monomer. [32] The CG-FF was parameterized based on a multi-scale approach previously used for biopolymers [33,34] and illustrated in Section S2, Supporting Information. The results of these simulations were employed for the finite element modeling (performed with COMSOL Multiphysics) of the temperature and mass density profiles in the polyelectrolyte as reported in Section S3, Supporting Information.…”

Aggregation behavior of nanoparticles: Revisiting the phase diagram of colloids

“…In this context, because atomistic simulations may not suffice to reach the very large time and size scales into play, the recursion to coarse-grained (CG) or multiscale models ( Palermo et al, 2020 ), ( Tavanti and Tozzini, 2014 ) emerges naturally. Computationally cheap implicit solvent, single-residue-level-based [“minimalist” ( Trovato and Tozzini, 2012 )] models for proteins have been optimized during these years ( Di Fenza et al, 2009 ), ( Delfino et al, 2020 ), ( Delfino et al, 2019 ), using parameterization strategies that typically combine bottom-up with top-down approaches, i.e., including data from atomistic simulations, as far as thermo-statistic data or large dataset ( Maccari et al, 2013 ; Spampinato et al, 2014 ) of structural data from the experiment, possibly with the aid of evolutionary algorithms ( Mereghetti et al, 2017 ; Leonarski et al, 2013 ). Low resolution models for functionalized NPs appeared more recently ( Angioletti-Uberti, 2017 ; Brancolini and Tozzini, 2019a ) and displayed a large variety of approaches.…”