to study the various conformational states of biomolecules . Integrating experimental data, such as NMR and X-ray crystallographic data, with MD simulations has led to improved conformational heterogeneity evaluation. MD has been combined with X-ray crystallography data to generate multiplestate models that have improved fit to X-ray data compared to single-structure models . However, conventional MD simulations often fall short in capturing slower motions, particularly allosteric conformational changes. Approaches have also been developed to integrate experimental NMR data with MD simulations. By incorporating time-averaged distance restraints from NOE data , MD simulations can better model conformational distributions consistent with experimental data, as in the case of the DNA-binding loops of E. coli tryptophan repressor . Bayesian inference and ensemble fitting approaches, which leverage experimental data alongside MD simulations, can also generate multiple-state models . Similarly, chemical shift data have been used to guide or interpret computational methods. For example, NMR chemical shift perturbation analysis using programs like CHESCA has been used to characterize allosteric conformational switching upon ligand binding by chemical shift covariance analysis . Alternative conformational state modeling with Ohm, a structural perturbation propagation method, for a set of ~ 20 allosterically-modulated proteins was observed to provide excellent predictions of CHESCA-based chemical shift changes . Accelerated MD methods have also proven effective in modeling multiple conformational states of proteins, which align well with NMR data .