In addition to physics-based models, statistics-based protein CG models, such as the CABS potential, represent also an alternative in modelling large conformational changes in protein. The CABS-fold were used in protein prediction competitions (e.g. CASP ) and their extension to capture flexibility (i.e. CABS-flex) in peptide-protein recognition and protein complex enabling extensive rearrangements of the protein chain. These CG models rely on extensive experimental data, including folded protein structures deposited in the protein data bank (PDB), to derive potential energy functions. The functions are based on the observed frequency of structural features such as bond distances, angles, dihedral torsions, and residue-residue contacts. The CABS model excels in structure prediction of proteins and can even perform unassisted folding simulations with greater success compared to previous CG approaches. However, their performance is limited by a lack of transferability to systems outside their original parametrization, especially in interactions involving proteins and other biomolecules.