Next, the power of 3DDPDs to distinguish between mutants was tested. rs3DDPDs and ps3DDPDs were computed for the mutant trajectories and used to cluster the mutants based on the distance between descriptors. As rs3DDPDs are computed independently for each trajectory and reflect all atoms in the system, all mutants of the same target clustered together (Figure ). Within targets, clusters of mutants with similar overall dynamic behavior compared to wild type were observed, for example ADRB2 D79N 2.50 and D130N 3.49 , or with similar fluctuations from wild type in specific regions, such as AA1R R291C 7.56 and R296C 8.51 in TM7 and H8/C-terminus (Figure ). For targets with unique differential dynamic patterns from wild type for each mutant, like ACM2, the clusters discerned the most different patterns (e.g. D103N 3.32 shows certain receptor stabilization compared to D103E 3.32 and V421L 7.33 , and is therefore excluded from the cluster). These results supported the ability of rs3DDPDs to capture dynamic fluctuations in mutants. Nevertheless, the mutant discriminatory power of rs3DDPDs did not correlate directly to that of using directly RMSF (Figure ) or RMSF differences to wild type (Figure ), which reinforced the notion that rs3DDPDs are not merely a transform of RMSF and include other non-dynamic atomic information.