  Self-supervised protein language models have proved their effectiveness in
learning the proteins representations. With the increasing computational power,
current protein language models pre-trained with millions of diverse sequences
can advance the parameter scale from million-level to billion-level and achieve
remarkable improvement. However, those prevailing approaches rarely consider
incorporating knowledge graphs (KGs), which can provide rich structured
knowledge facts for better protein representations. We argue that informative
biology knowledge in KGs can enhance protein representation with external
knowledge. In this work, we propose OntoProtein, the first general framework
that makes use of structure in GO (Gene Ontology) into protein pre-training
models. We construct a novel large-scale knowledge graph that consists of GO
and its related proteins, and gene annotation texts or protein sequences
describe all nodes in the graph. We propose novel contrastive learning with
knowledge-aware negative sampling to jointly optimize the knowledge graph and
protein embedding during pre-training. Experimental results show that
OntoProtein can surpass state-of-the-art methods with pre-trained protein
language models in TAPE benchmark and yield better performance compared with
baselines in protein-protein interaction and protein function prediction. Code
and datasets are available in https://github.com/zjunlp/OntoProtein.
