  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, an efficient model 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 efficiently utilise a novel message-passing method to
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. Code and models
are available at https://github.com/zhiqiangzhongddu/EvolMPNN.
