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BEST: Basic Embedding Search Tool Enhancing Discovery of Novel Enzyme.

Yuxuan Wu1, Xiao Yi1, Yang Tan1

  • 1School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China.

Interdisciplinary Sciences, Computational Life Sciences
|August 11, 2025
PubMed
Summary

The Basic Embedding Search Tool (BEST) identifies protein homologs with enhanced sensitivity and speed. This new method uses protein language models to find remote homologs missed by traditional sequence alignment tools.

Keywords:
Dense vector accelerationEnzyme miningHomology detectionProtein language model

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Protein homology identification is crucial for biological research.
  • Traditional methods like sequence alignment struggle to detect remote homologs.
  • There is a need for faster and more sensitive homology search tools.

Purpose of the Study:

  • To develop a novel tool, Basic Embedding Search Tool (BEST), for fast and sensitive protein homology identification.
  • To leverage protein language models for improved detection of remote homologs.
  • To accelerate sequence encoding and vector retrieval for efficient database searching.

Main Methods:

  • Utilized protein language models to generate sequence embeddings incorporating evolutionary and structural data.
  • Implemented a segmented distillation pruning technique for accelerated sequence encoding.
  • Developed a multi-layer acceleration structure for rapid dense vector access and retrieval.
  • Conducted extensive experiments on real datasets to evaluate BEST's performance.

Main Results:

  • BEST demonstrated over 20% increased sensitivity compared to existing methods, maintaining high precision and recall.
  • Achieved a 4290.86-fold speedup in dense vector access and retrieval.
  • Operated 23.41 times faster than PSI-BLAST and 3.92 times faster than Foldseek.
  • Successfully identified homologous sequences missed by conventional homology search tools.

Conclusions:

  • BEST offers a significant advancement in protein homology identification, outperforming traditional methods in both sensitivity and speed.
  • The tool's ability to detect remote homologs and its efficiency make it valuable for enzyme mining and broader biological research.
  • An open-access web server and publicly available code facilitate the adoption and application of BEST in the scientific community.