In this study, we utilized variational autoencoders (VAEs) to map functionally related haloalkane dehalogenase sequences from EnzymeMiner onto VAE latent spaces, following an approach inspired by Ding et al. . This process revealed that the latent space could capture the phylogenetic relationships of the sequences. Motivated by these insights, we employed the VAE framework for reconstructing sequences, aiming to create ancestral-like variants of the haloalkane dehalogenase DhaA. We tuned the network hyperparameters to accurately reflect the statistical frequencies of the input while maintaining the relationship between evolutionary trajectories and latent spaces. We discovered that a simple feed-forward neural network with a single dense layer matched to the input MSA columns and two-dimensional latent space was enough for this task.