CG potentials have demonstrated their versatility in modeling large-scale biomolecular systems and capturing long-time dynamics approaching biological timescales. However, their parametrization remains a challenge, particularly for intrinsically disordered proteins (IDPs), as it often relies on a combination of experimental data and all-atom simulations. Many CG approaches, such as GōMartini, impose rigid restraints to maintain secondary and tertiary structures, limiting their ability to accurately represent disordered states. To better capture protein flexibility, a refined backbone representation is essential. For instance, SIRAH improves flexibility modeling by employing a backbone with three CG beads, whereas GōMartini 3 and SPICA use simplified representations that restrict their applicability, particularly in studying dynamic folding pathways.