In the last two decades, various PSSM-based evolutionary feature descriptors have been utilized in proteomics. This study is designed to find actionable feature descriptors for machine learning modeling. The R package PSSMCOOL is explored for identifying the actionable feature descriptor for the protein structural class prediction. The SCOPe 2.07 dataset and the XGBoost algorithm are used for machine learning. The actionable feature descriptors highlight local sequential information, global sequential information, sequence order information, evolutionary information, and information related to protein structural patterns for the protein sequence. The performance reported for the SCOPe 2.07 test dataset and ASTRAL 1.73, 25PDB, and FC699 benchmark datasets is rewarding. The identified feature descriptor can be put to use for other proteomics tasks such as protein fold prediction and protein-protein interaction prediction.