Variational auto-encoding of protein sequences

Abstract

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.

Abstract for oral presentation at NIPS 2017 Workshop on Machine Learning in Computational Biology

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Cited by in corpus (9)

• Machine learning-assisted directed protein evolution with combinatorial libraries
• Design of metalloproteins and novel protein folds using variational autoencoders
• Generative Models for Automatic Chemical Design
• Generative Capacity of Probabilistic Protein Sequence Models
• AdaLead: A simple and robust adaptive greedy search algorithm for sequence design
• Deep Learning in Protein Structural Modeling and Design
• A primer on model-guided exploration of fitness landscapes for biological sequence design
• Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World
• Combination of digital signal processing and assembled predictive models facilitates the rational design of proteins