  Proteins are responsible for the most diverse set of functions in biology.
The ability to extract information from protein sequences and to predict the
effects of mutations is extremely valuable in many domains of biology and
medicine. However the mapping between protein sequence and function is complex
and poorly understood. Here we present an embedding of natural protein
sequences using a Variational Auto-Encoder and use it to predict how mutations
affect protein function. We use this unsupervised approach to cluster natural
variants and learn interactions between sets of positions within a protein.
This approach generally performs better than baseline methods that consider no
interactions within sequences, and in some cases better than the
state-of-the-art approaches that use the inverse-Potts model. This generative
model can be used to computationally guide exploration of protein sequence
space and to better inform rational and automatic protein design.
