Molecular dynamics (MD) simulations can in principle offer the opportunity of resolving structural, kinetic and thermodynamic characteristics of biological systems such as IDPs and IDP-rich bodies at the length and time scales that are not easily accessible experimentally . Recently, many protein folding problems were successfully solved by the advance of the deep learning method AlphaFold 53 . However, IDPs generally do not adopt a stably folded structure, featuring free energy landscapes containing a number of minima for different competing low-energy structures . Thus, exploring the conformations of IDPs still largely rely on molecular dynamic simulations . MD simulations complement state-of-the-art experimental methods such as NMR spectroscopy , single-molecule Förster resonance energy transfer (sm-FRET) , and small-angle X-ray and neutron scattering (SAXS and SANS) , which may exhibit challenges in measuring molecular motions at atomic resolution and conformational heterogeneity associated with structural disorder . Nevertheless, conformational sampling in classical molecular dynamics (MD) simulations can be restrained to local minima of free energy surfaces (FES), and accessing the full and complex landscape of disordered proteins can be nontrivial with standard unbiased simulations. To mitigate the problem, various enhanced sampling schemes have been developed to help probing regions of the configurational space that could be rarely explored otherwise. Unbiased enhanced samplings are largely based upon the concept of parallel-tempering (PT) . In temperature PT/replica-exchange algorithm, multiple replicas are simulated at different temperatures, in which conformations are exchanged at regular intervals, based on the Metropolis acceptance criterion. The stochastic nature of the method ensures generation of Boltzmann-weighted ensemble from which thermodynamic averages can be straightforwardly obtained. However, temperature differences between neighboring replicas must be moderate in order to yield practically large acceptance rates. For very large biomolecular systems simulated in explicit solvent, the number of replicas needed increases as O (f 1/2 ) with f as the system degrees of freedom, so reaching high temperature ensembles requires challenging computational costs . In Replica Exchange with Solute Tempering (REST2) , such issue can be overcome by scaling the protein intramolecular potential energy, in which case the acceptance probability depends only on the differences of intramolecular solute energy and intermolecular solute-water energy. Recently, Replica exchange with hybrid tempering (REHT) was developed to enhance exploring complex free energy landscape with large free energy barriers in the replica-exchange framework. At the same time, metadynamics-based techniques are powerful tools to accelerate the exploration of rare events such as protein folding and conformational transitions . These methods promote the exploration of free energy minima, by iteratively building a history-dependent bias potential as a sum of Gaussians defined as a function of a chosen set of collective variables (CVs). Boltzmann-weighted configuration ensemble can then be obtained via appropriate reweighting techniques . Among the many versions of metadynamics-based methods, well-tempered metadynamics (MetaD-WTE) allows the Gaussian height to be decreased with time so that the bias potential smoothly converges to the exact FE surface in the long-time limit , while Metadynamics with Adaptive Gaussians can adapt the Gaussian width on the fly to the local features of FES in order to improve sampling efficiency .