As far as the geometric properties of the latent space are concerned, we aimed to preserve the fact that the latent space carries evolutionary information. Thus, while optimizing model properties, we kept track of the relationship between phylogeny and the geometry of latent space (Fig. ). For that purpose, we prepared phylogenetic trees with inferred ancestral sequences. Every tree branch from a leaf to the root was mapped into latent space. We further quantified the relationship between the latent space distances to the origin and the corresponding positions in the phylogenetic tree (Fig. ). To gain insight into individual tree branches, we also calculated the angle between two vectors: the one going from a leaf node to the origin in the latent space, and the other one defined by the first principal component of the latent coordinates of all the nodes on the corresponding phylogenetic branch (Fig. ). Our findings indicate that small dense architectures of encoder and decoder are effective in capturing evolutionary dependencies within the latent space structure, whereas deeper architectures disrupt these evolutionary patterns (Fig. ). The final width of the dense layer was therefore set to the length of the protein sequence and the latent space dimensionality of 2 was chosen. This statistical analysis was repeated for Models 2-4 in the third round of experiments (Appendix section 1), reconfirming our finding and showing improved secondary statistics with a correlation of 0.8. Consequently, we kept the rules behind the hidden layer width and latent space dimensionality.