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Protein is linked to almost every life process. Therefore, analyzing thebiological structure and property of protein sequences is critical to theexploration of life, as well as disease detection and drug discovery.Traditional protein analysis methods tend to be labor-intensive andtime-consuming. The emergence of deep learning models makes modeling datapatterns in large quantities of data possible. Interdisciplinary researchershave begun to leverage deep learning methods to model large biologicaldatasets, e.g. using long short-term memory and convolutional neural networkfor protein sequence classification. After millions of years of evolution,evolutionary information is encoded in protein sequences. Inspired by thesimilarity between natural language and protein sequences, we use large-scalelanguage models to model evolutionary-scale protein sequences, encoding proteinbiology information in representation. Significant improvements are observed inboth token-level and sequence-level tasks, demonstrating that our large-scalemodel can accurately capture evolution information from pretraining onevolutionary-scale individual sequences. Our code and model are available athttps://github.com/THUDM/ProteinLM.

Modeling Protein Using Large-scale Pretrain Language Model