The primary challenge with using Al-phaFold2, ESM2, and ProtBert is the high dimensionality of their per-residue embeddings, which are impractical to use in PCM methods. Although our use of a convolutional autoencoder helped reduce dimensions and capture essential relational data, the resulting embeddings still lacked necessary information to improve model performance.