NeuRiPP

NeuRiPP is a machine learning-based software tool to identify precursor peptide (PP) sequences in ribosomally synthesized and post-translationally modified peptides (RiPPs) biosynthetic gene clusters (BGCs). Unlike existing tools, NeuRiPP is not limited to specific RiPP classes and does not require information about the genetic context surrounding the putative PP sequences.

NeuRiPP utilizes neural network architectures suitable for peptide classification, with weights trained on a comprehensive dataset of high-confidence putative PP sequences derived from existing programs and experimentally verified PPs from RiPP databases. This approach enables NeuRiPP to identify both known and likely PP sequences accurately.

Topic

Machine learning;Small molecules;Molecular interactions, pathways and networks

Detail

  • Operation: Enrichment analysis;Peptide identification

  • Software interface: Command-line interface

  • Language: Python

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: BBSRC and EPSRC.

  • Input: -

  • Output: -

  • Contact: Emmanuel L. C. de los Santos emzodls@gmail.com

  • Collection: -

  • Maturity: -

Publications

  • NeuRiPP: Neural network identification of RiPP precursor peptides.
  • de Los Santos ELC. NeuRiPP: Neural network identification of RiPP precursor peptides. NeuRiPP: Neural network identification of RiPP precursor peptides. 2019; 9:13406. doi: 10.1038/s41598-019-49764-z
  • https://doi.org/10.1038/S41598-019-49764-Z
  • PMID: 31527713
  • PMC: PMC6746993

Download and documentation

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