Further down in our pipeline, PCA is used to reduce dimensionality, which is a common resource in protein descriptor calculation. However, for non-dynamic one-hot encoded descriptors it is often used to calculate fixed features for each residue type (e.g. Zscale, MS-WHIM, Stscale ) rather than specific features for each residue in the sequence, as was done for rs3DDPDs given the heavy influence of the environment in the dynamic behavior of single residues. On the other hand, protein embeddings are often the byproduct of a machine or deep learning model using a protein sequence as input , unlike the approach followed for ps3DDPDs. Here, instead, a common main framework was kept to increase interpretability and interoperability of the resulting descriptors. This allowed us to follow a similar optimization route for both descriptor types (Figure ). In terms of residue composition, for our particular dataset the full sequence was favored. In a less diverse GPCR set, however, the use of family-or subfamily-specific alignments and binding pocket selections would provide more relevant information to the model given the differential activation-induced conformational changes reported for GPCRs binding different ligand types .