Exploring Large Protein Sequence Space through Homology- and Representation-based Hierarchical Clustering.

ABSTRACT: Exploration of protein sequence space can offer insight into protein sequence-function relationships, benefitting both basic science and industrial applications. The use of sequence similarity networks is a standard method for exploring large sequence datasets, but is currently limited when scaling to very large datasets and when viewing more than one level (hierarchy) of homology. Here, we present a sequence analysis pipeline with a number of innovations that address some limitations of traditional sequence similarity networks. First, we develop a hierarchical visualization approach that captures the full range of homologies across protein superfamilies. Second, we leverage representations embedded by protein language models as an alternative homology metric to the Basic Local Alignment S

SUBMITTER: Chen JZ 

PROVIDER: S-EPMC12204192 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature