Nearest neighbor search on embeddings rapidly identifies distant protein relations.

TitleNearest neighbor search on embeddings rapidly identifies distant protein relations.
Publication TypeJournal Article
Year of Publication2022
AuthorsSchütze, K, Heinzinger, M, Steinegger, M, Rost, B
JournalFront Bioinform
Date Published2022

Since 1992, all state-of-the-art methods for fast and sensitive identification of evolutionary, structural, and functional relations between proteins (also referred to as "homology detection") use sequences and sequence-profiles (PSSMs). Protein Language Models (pLMs) generalize sequences, possibly capturing the same constraints as PSSMs, e.g., through embeddings. Here, we explored how to use such embeddings for nearest neighbor searches to identify relations between protein pairs with diverged sequences (remote homology detection for levels of <20% pairwise sequence identity, PIDE). While this approach excelled for proteins with single domains, we demonstrated the current challenges applying this to multi-domain proteins and presented some ideas how to overcome existing limitations, in principle. We observed that sufficiently challenging data set separations were crucial to provide deeply relevant insights into the behavior of nearest neighbor search when applied to the protein embedding space, and made all our methods readily available for others.

Alternate JournalFront Bioinform
PubMed ID36466147
PubMed Central IDPMC9714024