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

John Z Chen1,2, Barnabas Gall1,3, Sacha B Pulsford1,3

  • 1Research School of Chemistry, Australian National University, Canberra, Australia.

Molecular Biology and Evolution
|June 4, 2025
PubMed
Summary
This summary is machine-generated.

We developed a scalable protein sequence analysis pipeline to explore protein sequence-function relationships. Our method uses hierarchical visualization and protein language models for improved homology detection, aiding scientific discovery.

Keywords:
bioinformaticscomputational biologyfunctional annotationprotein evolutionsequence space explorationtools and pipelines

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Exploring protein sequence space is crucial for understanding protein function and relationships.
  • Traditional sequence similarity networks face limitations in scalability and hierarchical homology visualization.
  • Current methods struggle with analyzing very large protein sequence datasets.

Purpose of the Study:

  • To present an innovative sequence analysis pipeline addressing limitations of traditional methods.
  • To enable scalable exploration of large protein sequence datasets.
  • To enhance understanding of protein sequence-function relationships.

Main Methods:

  • Developed a hierarchical visualization approach for homology.
  • Utilized protein language model embeddings as an alternative homology metric to BLAST.
  • Employed HMMs or vector representations for unbiased representative sequence sampling.
  • Applied the pipeline to FMN/F420-binding split barrel and nuclear transport factor 2-like superfamilies.

Main Results:

  • Hierarchical visualization captures full homology ranges across protein superfamilies.
  • Protein language model embeddings provide comparable results to BLAST for identifying isofunctional families.
  • Unbiased sequence sampling improves phylogenetic analysis.
  • The pipeline is scalable to approximately 445,000 sequences on desktop computers.

Conclusions:

  • The developed pipeline offers a scalable solution for exploring large protein sequence datasets.
  • Innovations in visualization and homology metrics enhance protein sequence-function analysis.
  • Publicly available code (ProteinClusterTools) facilitates broader research applications.