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Deciphering the biosynthetic potential of microbial genomes using a BGC language processing neural network model.

Qilong Lai1, Shuai Yao1, Yuguo Zha1

  • 1MOE Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.

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Summary

BGC-Prophet, a novel language model, efficiently predicts biosynthetic gene clusters (BGCs) in microbial genomes and metagenomes. This tool enhances the discovery of microbial secondary metabolites and aids synthetic biology applications.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Microbiology

Background:

  • Biosynthetic gene clusters (BGCs) are crucial for microbial secondary metabolite production but are often undiscovered in genomic and metagenomic data.
  • Existing methods for BGC prediction lack the efficiency and fidelity required for large-scale analysis.

Purpose of the Study:

  • To introduce BGC-Prophet, a transformer-based language model for accurate and efficient prediction and classification of BGCs.
  • To enable comprehensive screening of BGCs across diverse microbial lineages and metagenomes.

Main Methods:

  • Development of BGC-Prophet, a transformer encoder-based language model.
  • Application of BGC-Prophet for ultrahigh-throughput analysis of 85,203 genomes and 9,428 metagenomes.

Main Results:

  • BGC-Prophet significantly outperforms existing methods in BGC prediction efficiency and accuracy.
  • Analysis revealed a vast number of BGCs, with notable enrichment in Actinomycetota and widespread distribution of polyketide, NRP, and RiPP BGCs.
  • Identified BGC enrichment patterns correlated with geological events, suggesting environmental impacts on BGC evolution.

Conclusions:

  • BGC-Prophet is a powerful tool for discovering BGCs and understanding their evolutionary patterns.
  • The findings contribute to a deeper comprehension of microbial secondary metabolites and their potential in synthetic biology.