# Improving Conformational Ensembles of Folded Proteins in Go̅Martini

## TL;DR

Researchers improved the accuracy of protein simulations using a new optimization method for coarse-grained models.

## Contribution

A fully automated perturbation-based optimization method, PoGo̅, is introduced to refine nonuniform Go̅ networks for better conformational sampling.

## Key findings

- Martini 3 with ENMs or Go̅ models fails to sample conformational space as well as atomistic simulations.
- PoGo̅ rapidly converges to produce coarse-grained ensembles matching atomistic simulations closely.
- Optimization with PoGo̅ also improves root-mean-square fluctuation profiles.

## Abstract

The Martini coarse-grained (CG) force field enables efficient
simulations
of biomolecular systems but cannot reliably maintain folded protein
structures. To stabilize proteins during simulation, Martini is typically
combined with structure-based force fields such as elastic network
models (ENMs) or Go̅ models. While these approaches preserve
global folds and capture protein flexibility, their ability to reproduce
conformational dynamics remains unclear. Here, we evaluate Martini
3 combined with ENMs or Go̅ models on three folded proteins
and show that both approaches struggle to sample the conformational
space observed in atomistic simulations, even when uniform interaction
strengths or equilibrium bond distances are adjusted. This limitation
arises from the assumption of a uniform interaction network, in which
all Go̅-bonds are assigned the same ϵ value, and therefore
have the same potential depth. To overcome this, we present a fully
automated, perturbation-based optimization approach for Go̅
networks, PoGo̅, that iteratively refines a nonuniform Go̅
network against a precomputed atomistic free-energy landscape in essential
conformational space. Moreover, we demonstrate that our approach can
also be used to optimize ENMs. In both cases, convergence is rapid
and yields CG ensembles in close agreement with reference atomistic
simulations. As a cross-validation, the optimization also improves
the root-mean-square fluctuation profile.