Bioinformatic prediction and experimental validation of RiPP recognition elements.

Area of Science:

• Natural Product Discovery
• Molecular Biology
• Bioinformatics

Background:

• Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a diverse class of natural products with growing discovery efforts.
• Half of prokaryotic RiPP classes utilize a RiPP Recognition Element (RRE) domain for post-translational modification of precursor peptides.
• The RRE domain typically binds the N-terminal leader region, enabling modification of the C-terminal core region.

Purpose of the Study:

• To develop bioinformatic models for predicting and annotating RRE domains in a class-specific manner.
• To outline experimental methods for validating precursor peptide binding and RRE:substrate interactions.
• To facilitate class-independent RiPP discovery using an RRE-centric strategy.

Main Methods:

• Development of custom bioinformatic models for RRE domain prediction and annotation.
• Utilizing fluorescence polarization binding assays to validate precursor peptide binding.
• Employing in vitro enzyme activity assays to assess RRE:substrate interactions.

Main Results:

• Established protocols for class-specific RRE domain prediction.
• Demonstrated methods for experimental validation of RRE-precursor peptide interactions.
• Provided a framework for leveraging RRE domains in RiPP discovery.

Conclusions:

• The developed methods enable accurate prediction and validation of RRE domains.
• These protocols are anticipated to enhance future analyses of RRE domains.
• The RRE domain can be utilized as a customizable tool for molecular biology and bioengineering applications.