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
EvolMPNNcan 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.

为了预测蛋白质属性，我们提出了 EvolMPNN，即进化感知的消息传递神经网络，通过采样锚定蛋白质集合、计算进化信息以及不可微分的进化感知聚合方案，学习进化感知的蛋白质嵌入，并将其与序列嵌入相结合，生成综合蛋白质嵌入，该模型在性能和推理速度方面优于现有方法。