Finally, the use of 3DDPDs beyond wild type proteins was showcased by applying them to GPCR mutant MD simulations computed for a selection of 28 variants from five targets in our set with varied in vitro effects on ligand binding (Figure ). The analysis of the MD trajectories showed major dynamic fluctuations compared to wild type across the protein sequence, and not necessarily in the vicinity of the amino acid change, contrary to expectation (Figure ). Such allosteric effects on the protein dynamics dependent on the 3D organization of the protein have been previously shown to be able to explain the pathogenic mechanism of disease-driving variants , as well as cancer mutational drivers , and are therefore relevant to encode. Since 3DDPDs could not be applied to predict mutant bioactivity due to the lack of available data for our set, the power of the dynamic descriptors to discriminate between variants was investigated by clustering them based on the distance between descriptor vectors. To this end, rs3DDPDs were able to cluster all variants of the same target together, and smaller clusters were formed for mutants with similar dynamic behaviors compared to wild type (Figure , Figure ). Nevertheless, the clusters created based on rs3DDPDs did not fully represent the clusters based on RMSF (Figure ), further supporting that 3DDPDs include non-dynamic information on top of dynamic information.