HFSP: high speed homology-driven function annotation of proteins.

TitleHFSP: high speed homology-driven function annotation of proteins.
Publication TypeJournal Article
Year of Publication2018
AuthorsMahlich, Y, Steinegger, M, Rost, B, Bromberg, Y
Date Published2018 Jul 01

Motivation: The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. The increase in the number of available sequences, however, has drastically increased the search space, thus significantly slowing down alignment methods.

Results: Here we describe homology-derived functional similarity of proteins (HFSP), a novel computational method that uses results of a high-speed alignment algorithm, MMseqs2, to infer functional similarity of proteins on the basis of their alignment length and sequence identity. We show that our method is accurate (85% precision) and fast (more than 40-fold speed increase over state-of-the-art). HFSP can help correct at least a 16% error in legacy curations, even for a resource of as high quality as Swiss-Prot. These findings suggest HFSP as an ideal resource for large-scale functional annotation efforts.

Supplementary information: Supplementary data are available at Bioinformatics online.

Alternate JournalBioinformatics
PubMed ID29950013
PubMed Central IDPMC6022561
Grant ListU01 GM115486 / GM / NIGMS NIH HHS / United States