Embedding-based alignment: combining protein language models with dynamic programming alignment to detect structural similarities in the twilight-zone.

ABSTRACT:

Motivation

Language models are routinely used for text classification and generative tasks. Recently, the same architectures were applied to protein sequences, unlocking powerful new approaches in the bioinformatics field. Protein language models (pLMs) generate high-dimensional embeddings on a per-residue level and encode a "semantic meaning" of each individual amino acid in the context of the full protein sequence. These representations have been used as a starting point for downstream learning tasks and, more recently, for identifying distant homologous relationships between proteins.

Results

In this work, we introduce a new method that generates embedding-based protein sequence alignments (EBA) and show how these capture structural similarities even in the twilight zone,

SUBMITTER: Pantolini L 

PROVIDER: S-EPMC10792726 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature