Despite the recent progress in extracting evolutionary information from multiple sequence alignments (MSA) of homologous proteins, analyzing this variability is challenging. Historically, this data was used primarily by looking at only one or two positions at a time . More recent approaches extract patterns by deep neural networks, in particular algorithms that map the sequence space onto their internal low-dimensional representation, also referred to as latent spaces. Generative models trained on large datasets of tens of thousands of sequences have shown excellent results in producing highly interpretable embeddings and generating novel protein variants . Variational autoencoders (VAEs) show a particular promise in this domain due to the explicit modeling of the latent space . They have already proven useful in several applications, including predicting protein structures , discovering novel drugs , and predicting protein functions . By learning the latent space representation of a specific family, VAEs provide valuable insights into the evolution of protein families, as demonstrated in recent studies exploring the phylogenetic relationships within the latent space . In particular, Ding et al. showed that the latent space of the variational autoencoders can capture the biophysical properties of protein variants and the phylogenetic relationships within protein families . However, the study did not offer a strategy that would allow exploiting these relationships to generate new proteins from the latent space.