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Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions.

Grímur Hjörleifsson Eldjárn1, Andrew Ramsay1, Justin J J van der Hooft2

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Linking microbial biosynthetic gene clusters (BGCs) to specialized metabolites is crucial for discovering new antibiotics. NPLinker improves this process by integrating novel scoring methods for accurate genomic and metabolomic data analysis.

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Area of Science:

  • Microbiology
  • Metabolomics
  • Genomics
  • Natural Product Drug Discovery

Background:

  • Microbial specialized metabolites are vital for biomedical applications, especially antibiotics.
  • Linking microbial genomes to their metabolites (Biosynthetic Gene Clusters - BGCs) aids in discovering novel compounds.
  • Current methods struggle to automatically and accurately link BGCs to metabolites due to biosynthetic knowledge gaps.

Purpose of the Study:

  • To develop and present NPLinker, a software framework for linking genomic and metabolomic data.
  • To improve the accuracy and efficiency of identifying true links between BGCs and metabolites.
  • To address the bottleneck in natural product research caused by manual verification.

Main Methods:

  • Utilizing paired omics data sets (genomic and metabolomic).
  • Developing and applying multiple link-scoring functions, including a novel Input-Output Kernel Regression score.
  • Standardizing and improving commonly used scoring metrics.
  • Validating results using publicly available datasets with known links.

Main Results:

  • Demonstrated that combining multiple link-scoring functions enhances the prioritization of true BGC-metabolite links.
  • Introduced a novel, effective scoring method using Input-Output Kernel Regression.
  • NPLinker successfully links genomic and metabolomic data, verified by existing validated datasets.

Conclusions:

  • NPLinker provides an effective computational framework for linking microbial BGCs to specialized metabolites.
  • The developed scoring functions significantly improve the accuracy of identifying novel natural products.
  • This approach accelerates natural product research by automating and refining the discovery process.