Later on, based on the low solubility of the design observed in the first two rounds of experimental characterization, we applied a stricter protocol for creating the initial MSA. In particular, inspired by the conclusions in Vasina et al. about the low solubility of members from HLD subfamilies III and IV, we created a much smaller MSA, instructing EnzymeMiner to search among HLD subfamilies I and II only. This dataset includes the DhaA enzyme from Rhodococcus sp. with UniProt ID P0A3G3, wellcharacterized in our lab, which we then used as an updated query for the MSA preprocessing with additional filters to further reduce the gap frequencies in comparison to previous rounds: we lowered the threshold for gap column removal and included additional filtering of columns with frequent gaps, even when the query position had an amino acid in that position. These removed positions and corresponding amino acids were then added back to the reconstructed sequences at the end of the pipeline, i.e., we treated them as invariable. By this, we lowered the width of MSA to 293 positions with 4,053 sequences. Advanced sequence search of homologous proteins using the EnzymeMiner tool . (B) Optimization of the variational autoencoder architecture to capture the sequence distribution of the MSA and phylogenetic dependencies within the latent space. (C) Exploration of the evolutionary dependencies between the sequences extracted from the variational autoencoder and its low-dimensional latent space. This representation is then used to guide the protein design strategy and generate sequences along the trajectory from the query to the latent space origin. The generated sequences are characterized based on their statistical and sequential properties to produce the evolutionary profile. This profile serves as a guide for selecting designs. (D) The experimental characterization of the proposed designs is conducted. The orange frames represent the computational steps and the blue frame is the experimental step.