EvolMPNN: Predicting Mutational Effect on Homologous Proteins by Evolution Encoding
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Keywords: Mutation Prediction, Protein Property Prediction, Homologous Protein Sequence Modelling, Message Passing Neural Network
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TL;DR: We propose a novel message-passing framework to model protein mutations for protein property prediction.
Abstract: Predicting protein properties is paramount for biological and medical advancements. Current protein engineering mutates on a typical protein, called the wild-type, to construct a family of homologous proteins and study their properties. Yet, existing methods easily neglect subtle mutations, failing to capture the effect on the protein properties. To this end, we propose EvolMPNN, Evolution-aware Message Passing Neural Network, to learn evolution-aware protein embeddings. EvolMPNN samples sets of anchor proteins, computes evolutionary information by means of residues and employs a differentiable evolution-aware aggregation scheme over these sampled anchors. This way EvolMPNN can capture the mutation effect on proteins with respect to the anchor proteins. Afterwards, the aggregated evolution-aware embeddings are integrated with sequence embeddings to generate final comprehensive protein embeddings. Our model shows up to 6.4% better than state-of-the-art methods and attains 36X inference speedup in comparison with large pre-trained models. The code and models are available at https://anonymous.4open.science/r/EvolMPNN.
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Submission Number: 4020
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