  Here we present a novel approach to protein design and phenotypic inference
using a generative model for protein sequences. BioSeqVAE, a variational
autoencoder variant, can hallucinate syntactically valid protein sequences that
are likely to fold and function. BioSeqVAE is trained on the entire known
protein sequence space and learns to generate valid examples of protein
sequences in an unsupervised manner. The model is validated by showing that its
latent feature space is useful and that it accurately reconstructs sequences.
Its usefulness is demonstrated with a selection of relevant downstream design
tasks. This work is intended to serve as a computational first step towards a
general purpose structure free protein design tool.
