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Related Concept Videos

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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The term ribozyme is used for RNA that can act as an enzyme. Ribozymes are mainly found in selected viruses, bacteria, plant organelles, and lower eukaryotes. Ribozymes were first discovered in 1982 when Tom Cech’s laboratory observed Group I introns acting as enzymes. This was shortly followed by the discovery of another ribozyme, Ribonulcease P, by Sid Altman’s laboratory. Both Cech and Altman received the Nobel Prize in chemistry in 1989 for their work on ribozymes.
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RACE - Rapid Amplification of cDNA Ends02:35

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Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific...
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The organelle-specific signaling sequences direct proteins synthesized in the cytosol to their final destination like ER, mitochondria, peroxisomes, etc. Some of the proteins directed to ER are then trafficked via vesicles to other organelles within the cell or the extracellular environment through the Golgi complex. For example, the rough ER synthesizes soluble proteins for transportation to the lysosomes or secretion out of the cell. It can also synthesize transmembrane proteins that can...
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Updated: Jan 11, 2026

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq
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Structure-based discovery and definition of RiPP recognition elements.

Miriam H Bregman1, Dillon P Cogan2, Kyle E Shelton1

  • 1Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

Msystems
|November 18, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances RiPP discovery by combining structure-based searching with AlphaFold predictions, identifying over 90,000 RiPP Recognition Elements (RREs) and 13 recognition motifs. This improves genome mining accuracy and expands access to hidden RiPP biosynthetic pathways.

Keywords:
RiPPsgenome miningmolecular recognitionnatural productsprotein:peptide interactionsribosomal peptides

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Area of Science:

  • Natural product discovery
  • Bioinformatics
  • Structural biology
  • Genomics

Background:

  • Ribosomally synthesized and post-translationally modified peptides (RiPPs) are diverse natural products crucial in various biological processes.
  • RiPP Recognition Elements (RREs) are key peptide-binding domains essential for RiPP biosynthesis and genome mining.
  • Existing RRE-Finder tools face limitations due to high false-positive rates and difficulty in identifying sequence-divergent RREs.

Purpose of the Study:

  • To improve the accuracy and scope of RiPP genome mining by enhancing RRE identification.
  • To leverage structure-based searching (Foldseek) and AlphaFold predictions to discover sequence-divergent RREs and their associated precursor peptides.
  • To refine bioinformatic tools for more comprehensive identification of RRE-dependent biosynthetic pathways.

Main Methods:

  • Employed Foldseek for structure-based searching of the AlphaFold database to identify divergent RREs.
  • Developed 11 new Foldseek-derived Hidden Markov Models (HMMs) and refined existing models for RRE-Finder.
  • Utilized AlphaFold 3 to predict RRE-peptide complexes, enabling the mapping of recognition sequences.

Main Results:

  • The updated workflow identified over 90,000 high-confidence RREs, nearly doubling the retrieval rate from UniProt compared to original models.
  • Discovered novel RRE domain fusions and 5,000 previously unidentified RRE domains, retaining canonical folds but offering new bioinformatic handles.
  • Mapped 13 distinct recognition sequence motifs across RiPP classes by predicting RRE-precursor peptide interactions.

Conclusions:

  • The integration of structure-based searching and advanced modeling significantly enhances the accuracy and efficiency of RiPP genome mining.
  • This improved approach expands the known landscape of RRE-dependent biosynthetic pathways, providing access to previously hidden natural products.
  • The findings streamline the identification of RiPP precursor peptides and their cognate RREs, facilitating further research into RiPP diversity and function.