.png){kind=icon}
.png){kind=icon}
Return
Domain-PFP allows protein function prediction using function-aware domain embedding representations
.png){kind=icon}
OA
AI
DOI：10.1038/s42003-023-05476-9.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Domains are functional and structural units of proteins that govern various biological functions performed by the proteins. Therefore, the characterization of domains in a protein can serve as a proper functional representation of proteins. Here, we employ a self-supervised protocol to derive functionally consistent representations for domains by learning domain-Gene Ontology (GO) co-occurrences and associations. The domain embeddings we constructed turned out to be effective in performing actual function prediction tasks. Extensive evaluations showed that protein representations using the domain embeddings are superior to those of large-scale protein language models in GO prediction tasks. Moreover, the new function prediction method built on the domain embeddings, named Domain-PFP, substantially outperformed the state-of-the-art function predictors. Additionally, Domain-PFP demonstrated competitive performance in the CAFA3 evaluation, achieving overall the best performance among the top teams that participated in the assessment. A self-supervised learning method to generate functionally informed embedding representations for protein domains, capable of performing protein function prediction with state-of-the-art accuracy.
Keywords:
LARGE-SCALE
FAMILIES
ANNOTATIONS
LANGUAGE
DATABASE
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.1
Papers:
1.0W
Citations:
3.2W
