Expansion of predicted RiPP biosynthetic sequence space using the RiPP recognition element

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a family of natural products for which discovery efforts have rapidly grown over the past decade. More than half of the prokaryotic RiPP classes include a protein domain called the RiPP Recognition Element (RRE) for successful installation of post-translational modifications on a RiPP precursor peptide. In most cases, the RRE domain binds to the N-terminal ‘leader’ region of the precursor peptide, facilitating enzymatic modification of the C-terminal ‘core’ region. The prevalence of the RRE domain renders it a theoretically useful bioinformatic handle for class independent RiPP discovery. Moreover, with most known RRE domains engaging their cognate precursor peptide(s) with high specificity and nanomolar affinity, evaluation of the residue-specific interactions that govern RRE:substrate complexation is a necessary first step to leveraging the RRE domain for various bioengineering applications. Chapter 1 details protocols for developing custom bioinformatic models to predict and annotate RRE domains in a class-specific manner. Methods are outlined for experimental validation of precursor peptide binding using fluorescence polarization binding assays and in vitro enzyme activity assays. Chapter 2 presents a novel genome mining tool, RRE-Finder, created to identify and annotate RRE domains from genomic data. Given its prevalence across various types of RiPP biosynthetic gene clusters (BGCs), the RRE can be used as a bioinformatic handle to identify novel classes of RiPPs. However, sequence divergence of RREs across RiPP classes has precluded automated identification based solely on sequence similarity. RRE-Finder is a tool for identifying RRE domains with high sensitivity. RRE-Finder can be used in “precision” mode to confidently identify RREs in a class-specific manner or in “exploratory” mode, to assist in the discovery of novel RiPP classes. RRE-Finder operating in precision mode on the UniProtKB protein database retrieved ~25,000 high-confidence RREs spanning all characterized RRE-dependent RiPP classes, as well as several yet-uncharacterized RiPP classes that require future experimental confirmation. Finally, RRE-Finder was used in precision mode to explore a possible evolutionary origin of the RRE domain. The results suggest RREs originated from a co-opted DNA-binding transcriptional regulator domain. Altogether, RRE-Finder provides a powerful new method to probe RiPP biosynthetic diversity and delivers a rich dataset of RRE sequences that will provide a foundation for deeper biochemical studies into this intriguing and versatile protein domain. Chapter 3 leverages RRE-Finder precision mode datasets to delve into the relationship between RRE domains and their cognate precursor peptides. The identification of recognition sequences in RiPP precursor peptides is important not only from the perspective of accurately predicting viable precursors in genomic contigs but also for leveraging possible biotechnological applications of the RRE in protein purification and detection. In this chapter, we bioinformatically survey the landscape of RS motifs found across all RRE-dependent RiPP classes, highlighting 26 widespread RS motifs that are prevalent in one or more RiPP classes. Next, we employ in vitro binding assays to quantify binding interactions for several as-of-yet uncharacterized RRE:RS interactions, using this binding data to validate or disprove hypotheses generated by AlphaFold models for critical RS residues in these complexes.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Chemistry

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Shelton, Kyle Engstrom

Contributors dc:contributor

• Mitchell, Douglas A
• Van der Donk, Wildred A
• Nair, Satish K
• Hergenrother, Paul J

• Natural Product
• Ripp
• Biosynthesis

Statement dc:rights

• Copyright 2023 Kyle Shelton

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/121216

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F121216