  Protein is linked to almost every life process. Therefore, analyzing the
biological structure and property of protein sequences is critical to the
exploration of life, as well as disease detection and drug discovery.
Traditional protein analysis methods tend to be labor-intensive and
time-consuming. The emergence of deep learning models makes modeling data
patterns in large quantities of data possible. Interdisciplinary researchers
have begun to leverage deep learning methods to model large biological
datasets, e.g. using long short-term memory and convolutional neural network
for protein sequence classification. After millions of years of evolution,
evolutionary information is encoded in protein sequences. Inspired by the
similarity between natural language and protein sequences, we use large-scale
language models to model evolutionary-scale protein sequences, encoding protein
biology information in representation. Significant improvements are observed in
both token-level and sequence-level tasks, demonstrating that our large-scale
model can accurately capture evolution information from pretraining on
evolutionary-scale individual sequences. Our code and model are available at
https://github.com/THUDM/ProteinLM.
