Overall we observe it is possible to construct linear projections of model latent spaces with comparatively low overall distortions; however, the model that has thus far performed particularly highly on other metrics (the 128-dimensional Transformer) has the greatest distortion, particularly in terms of cohesiveness, meaning it potentially introduces a significant number of false groupings. The Transformer's comparatively high distortion overall underscores one of the traditional trade-offs of machine learning: with greater power comes lessened interpretability. Now that we have an idea of how confident we may be in the representation of the PCs for the model latent space, we investigate the identification of "bridge variables" for further interpretation of the latent space. Bridge variables are known quantities of relevance in a scientific problem that show correlations with unknown variables, allowing heightened interpretability of many nonlinear problems . We consider a series of physicochemical properties of peptides that are measurable from sequence alone: Aliphatic Index, Boman Index, Isoelectric Point, Charge pH=3, Charge pH=7, Charge pH=11, Hydrophobicity, Instability Index, and Molecular Weight. We measure each of these properties for the test set sequences using the peptides python package . We employ the Silhouette Score in one dimension to measure correlation between individual PCs and verified-AMP/nonverified-AMP labeling, and we use the Pearson correlation coefficient (r) to measure correlation between individual PCs and the continuous physicochemical properties.