The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction.

ABSTRACT: Computationally annotating proteins with a molecular function is a difficult problem that is made even harder due to the limited amount of available labeled protein training data. Unsupervised protein embeddings partly circumvent this limitation by learning a universal protein representation from many unlabeled sequences. Such embeddings incorporate contextual information of amino acids, thereby modeling the underlying principles of protein sequences insensitive to the context of species. We used an existing pre-trained protein embedding method and subjected its molecular function prediction performance to detailed characterization, first to advance the understanding of protein language models, and second to determine areas of improvement. Then, we applied the model in a transfer learning

SUBMITTER: van den Bent I 

PROVIDER: S-EPMC8647222 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature