  Accurately finding proteins and genes that have a certain function is the
prerequisite for a broad range of biomedical applications. Despite the
encouraging progress of existing computational approaches in protein function
prediction, it remains challenging to annotate proteins to a novel function
that is not collected in the Gene Ontology and does not have any annotated
proteins. This limitation, a side effect from the widely-used multi-label
classification problem setting of protein function prediction, hampers the
progress of studying new pathways and biological processes, and further slows
down research in various biomedical areas. Here, we tackle this problem by
annotating proteins to a function only based on its textual description so that
we do not need to know any associated proteins for this function. The key idea
of our method ProTranslator is to redefine protein function prediction as a
machine translation problem, which translates the description word sequence of
a function to the amino acid sequence of a protein. We can then transfer
annotations from functions that have similar textual description to annotate a
novel function. We observed substantial improvement in annotating novel
functions and sparsely annotated functions on CAFA3, SwissProt and GOA
datasets. We further demonstrated how our method accurately predicted gene
members for a given pathway in Reactome, KEGG and MSigDB only based on the
pathway description. Finally, we showed how ProTranslator enabled us to
generate the textual description instead of the function label for a set of
proteins, providing a new scheme for protein function prediction. We envision
ProTranslator will give rise to a protein function "search engine" that returns
a list of proteins based on the free text queried by the user.
