Variational auto-encoding of protein sequences
This work addresses the challenge of understanding protein sequence-function relationships for applications in biology and medicine, representing an incremental improvement over existing methods.
The authors tackled the problem of predicting how mutations affect protein function by embedding natural protein sequences using a Variational Auto-Encoder, achieving performance better than baseline methods and in some cases surpassing state-of-the-art inverse-Potts model approaches.
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.