Solubilization of designs by introducing new knowledge to the dataset. As we observed low solubility in the first round (see Section 3.5 Experimental characterization of expressed variants and Fig. ), we incorporated previous findings on the low solubility of HLD subfamilies III and IV in our workflow. To this end, we embarked on a third round of experiments restricted to the HLD subfamilies I and II. This round focused on training additional models (Models 2-3) and designed eight DhaA variants (AncDhaA13-20, Table ). The first candidate VAE (Model 2) achieved up to 97% similarity in query sequence reconstruction with satisfactory second-order statistics. Considering the often-disruptive impact of indels, we selected and curated five designs from the straight-line evolutionary strategy accumulating at most one indel (AncDhaA13-17). Another VAE trained from a different initialization (Model 3) showed similar trends but additionally had a curious pattern for the origin of the latent space: the model demonstrated a significant shift in sequence similarity towards DbjA. Therefore, we selected ancestor 99 (AncDhaA20) from this model for further experimental characterization to explore its unique shared sequence similarity to both DhaA (52%) and DbjA (93%).