Exploring homology detection via k-means clustering of proteins embedded with a large language model.

ABSTRACT:

Motivation

Inferring protein homology from sequence information is essential for understanding species evolution and enabling functional annotation transfer. Besides similarity-based methods, several machine learning approaches have been developed using various ways of representing protein data.

Results

Here, we represent proteins with a biologically oriented large language model and apply k-means clustering to the embedded data to extract homology relationships. Although our approach lacks the sensitivity of other tools, we obtain better precision for the detection of n:m orthologs. Furthermore, we successfully reconstruct full orthologous groups from scratch, highlighting the growing potential of using large language models in combination with clustering algorithms for the

SUBMITTER: Minotto T 

PROVIDER: S-EPMC12517335 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature