RODEO Algorithm: A Complete Guide for Accelerated RiPP Biosynthetic Gene Cluster Discovery

Leo Kelly Jan 12, 2026 76

This comprehensive article explores RODEO (Rapid ORF Description and Evaluation Online), a pivotal computational tool for identifying Ribosomally synthesized and post-translationally modified peptide (RiPP) precursor peptides and their biosynthetic gene...

RODEO Algorithm: A Complete Guide for Accelerated RiPP Biosynthetic Gene Cluster Discovery

Abstract

This comprehensive article explores RODEO (Rapid ORF Description and Evaluation Online), a pivotal computational tool for identifying Ribosomally synthesized and post-translationally modified peptide (RiPP) precursor peptides and their biosynthetic gene clusters (BGCs). Tailored for researchers and drug discovery scientists, we detail its foundational principles, step-by-step application methodology, common troubleshooting strategies, and performance validation against other bioinformatic tools. The guide synthesizes how RODEO's heuristic scoring and genomic context analysis overcome traditional limitations, enabling the efficient discovery of novel natural products with therapeutic potential from microbial genomes.

What is RODEO? Demystifying the Algorithm for RiPP Discovery

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a vast and structurally diverse class of natural products with potent bioactivities. Their biosynthesis begins with a ribosomally produced precursor peptide, which comprises a leader region and a core region. The leader region directs post-translational modification enzymes to the core, which is extensively tailored to yield the mature bioactive compound. The definitive identification of the correct precursor peptide gene within a biosynthetic gene cluster (BGC) is the critical first step in RiPP discovery and engineering, yet remains a significant computational and experimental challenge. This application note, framed within broader thesis research on the RODEO (Rapid ORF Description and Evaluation Online) algorithm, details the bottleneck and provides protocols to address it.

The Core Bottleneck: Challenges in Precursor Peptide Identification

Precursor peptides are notoriously difficult to predict in silico due to their short, variable, and often repetitive core sequences, which lack homology to known proteins. Traditional BGC analysis tools (e.g., antiSMASH) can identify enzymatic machinery but frequently fail to pinpoint the correct precursor open reading frame (ORF).

Table 1: Quantitative Challenges in Precursor Peptide Identification

Challenge Quantitative/Descriptive Impact Consequence
Short ORF Length Often < 150 bp (core region ~20-40 aa). Easily missed by gene-calling algorithms tuned for longer proteins.
Lack of Homology Core peptides show near-zero BLASTp homology. Homology-based searches fail.
Variable Leader Motifs Leader sequences are conserved only within RiPP classes. Requires class-specific hidden Markov models (HMMs).
Genomic Context Ambiguity Multiple small ORFs near modification enzymes. High false-positive rate; experimental validation required.

Protocol 1:In SilicoPrecursor Prediction Using RODEO

Purpose: To identify and score candidate precursor peptides from a genomic BGC. Materials: Genomic region (FASTA), RODEO suite (HMMs, Python scripts), ORF caller (e.g., getorf from EMBOSS).

Procedure:

  • ORF Extraction: Extract all possible small ORFs (e.g., 30-300 bp) from the BGC region and its flanking sequences (up to 10 kb) using a six-frame translation.
  • Leader Peptide Analysis: For each ORF, translate the N-terminal 40-60 residues. Score against a library of RiPP-class-specific leader peptide HMMs (e.g., for lanthipeptides, thiopeptides).
  • Core Peptide Scoring: Analyze the downstream core region for hallmark features: presence of cognate substrate motifs for downstream enzymes (e.g., cysteine patterns for lanthipeptide dehydratases), low complexity, and repetitiveness.
  • RODEO Scoring Integration: The algorithm computes a heuristic score combining leader homology, core motif presence, and genomic distance to biosynthetic enzymes. Candidates are ranked.
  • Output: A ranked list of candidate precursor peptides with associated scores for experimental prioritization.

Protocol 2: Experimental Validation by Precursor Peptide Knockout

Purpose: To confirm the essentiality of a bioinformatically predicted precursor peptide for bioactive metabolite production.

Materials: Bacterial strain harboring the BGC; cloning vectors and reagents for homologous recombination or CRISPR-Cas9; HPLC-MS system; bioassay materials (e.g., indicator strain for antimicrobial activity).

Procedure:

  • Mutant Construction: Design a construct for in-frame deletion or insertion-disruption of the candidate precursor peptide gene within the native host.
  • Mutant Generation: Introduce the construct via conjugation/electroporation. Select for mutants and verify genotype via PCR and sequencing.
  • Metabolite Extraction: Cultivate wild-type and mutant strains under identical conditions. Extract metabolites from culture broth and cell pellets using appropriate solvents (e.g., methanol/ethyl acetate).
  • Chemical Analysis: Analyze extracts by HPLC-MS. Compare chromatograms and mass spectra. The loss of the target ion peak (corresponding to the predicted mass of the mature RiPP) in the mutant confirms the correct precursor.
  • Bioassay Correlation: If available, demonstrate loss of biological activity (e.g., antimicrobial) in the mutant extract compared to wild-type.

Visualization of the RiPP Discovery Workflow and Bottleneck

G Start Genomic DNA Containing BGC InSilico In Silico Analysis (Gene Prediction, antiSMASH) Start->InSilico Bottleneck MAJOR BOTTLENECK: Precursor Peptide Identification InSilico->Bottleneck RODEO RODEO Algorithm: Candidate Scoring & Prioritization Bottleneck->RODEO Candidates Ranked List of Candidate Precursor ORFs RODEO->Candidates Validation Experimental Validation (Knockout, HPLC-MS) Candidates->Validation Confirmed Confirmed Precursor Peptide Validation->Confirmed Downstream Downstream Steps: Heterologous Expression, Engineering, SAR Confirmed->Downstream

Title: RiPP Discovery Workflow with the Precursor Identification Bottleneck

G BGC Biosynthetic Gene Cluster (BGC) ORF A (putative precursor?) ORF B (putative precursor?) Modification Enzyme 1 Modification Enzyme 2 ORF C (putative precursor?) Transport/Regulation Problem Identification Problem: Short, no homology, multiple candidates BGC:orf1->Problem BGC:orf2->Problem BGC:orf3->Problem Precursor Correct Precursor Peptide Leader Region Enzyme Recognition Core Region Post-translational modification → Mature RiPP Problem->Precursor

Title: The Genomic Challenge of Finding the Correct Precursor ORF

The Scientist's Toolkit: Key Reagent Solutions for Precursor Validation

Table 2: Essential Research Reagents and Materials

Item Function/Application
RODEO Software Suite Integrates HMMs and heuristic scoring to rank candidate precursor peptides from genomic data.
RiPP-Class Specific Leader HMMs Profile hidden Markov models for leader peptide recognition (e.g., LanM, YcaO-associated leaders).
pCRISPR-Cas9 or λ-RED Plasmid Systems For targeted, markerless knockout of the candidate precursor gene in the native producer.
HPLC-MS System (High-Resolution) Critical for comparative metabolomics to detect the presence/absence of the target RiPP in extracts.
C18 Solid-Phase Extraction (SPE) Cartridges For desalting and concentrating culture broth extracts prior to HPLC-MS analysis.
Heterologous Expression Host (e.g., E. coli, S. albus) For cloning and expressing the entire BGC to confirm precursor-enzyme pairing.
Activity Assay Reagents (e.g., Soft Agar, Indicator Strain) To correlate the loss of the metabolite with loss of biological activity post-knockout.

The identification of the precursor peptide is the decisive, rate-limiting step in RiPP discovery. Overcoming this bottleneck requires a tight feedback loop between advanced in silico tools like RODEO, which leverages class-specific rules to prioritize candidates, and definitive experimental protocols, primarily gene knockout coupled with targeted metabolomics. Integrating these approaches, as framed within the RODEO-centric thesis research, systematically converts genomic potential into characterized RiPP pathways, enabling downstream drug development efforts.

Application Notes

RODEO (Rapid ORF Description and Evaluation Online) is a bioinformatics pipeline designed to address the central bottleneck in RiPP (Ribosomally synthesized and post-translationally modified peptide) discovery: the accurate identification of biosynthetic gene clusters (BGCs) and their precursor peptides from genomic data. Traditional BGC prediction tools (e.g., antiSMASH) often fail to correctly annotate short, genetically encoded RiPP precursor peptides due to their lack of conserved domains, high sequence diversity, and short length. RODEO bridges this gap by integrating homology-based scoring with motif analysis and genomic context to achieve high-confidence precursor peptide predictions, enabling the targeted discovery of novel natural products.

Table 1: Comparison of BGC Prediction Tools for RiPP Discovery

Tool Name Primary Method Strength for RiPPs Key Limitation Addressed by RODEO
antiSMASH Rule-based, HMM profiles Broad BGC detection, user-friendly Poor short ORF/precursor peptide annotation
BAGEL4 Pre-defined motif databases Specific for bacteriocins Limited to known motif classes
RODEO Hybrid: Homology + Motif + Context High-precision precursor ID Bridges genomic data to specific peptide candidates
PRISM 4 Chemical structure prediction Predicts putative structures Less focused on precise precursor peptide delineation

Table 2: Example RODEO Output Metrics for a Lasso Peptide BGC

Prediction Component Score/Range Confidence Indicator
Precursor Peptide ORF Length 50-100 aa Typical for class II lasso peptides
Core Motif Conservation High (e.g., 'GxG' motif) Strong evidence for modification
Genomic Context Score (proximity to mod. enzymes) >80 (out of 100) High-confidence cluster association
Helicopter Score (for lasso peptides) >150 High probability of lasso topology

Protocols

Protocol 1: Genome Mining for Novel RiPPs Using RODEO

Objective: To identify novel RiPP precursor peptides and their associated biosynthetic gene clusters from a bacterial genome assembly.

Research Reagent Solutions & Essential Materials:

  • Bacterial Genome Sequence (FASTA): The input genetic material for analysis.
  • RODEO Web Server or Local Installation: The core analytical platform. (Available at https://rodeo.scs.illinois.edu/).
  • antiSMASH: Used for initial, broad BGC identification.
  • HMMER Suite: For profile hidden Markov model searches.
  • BLASTP/NCBI NR Database: For homology assessments.
  • Multiple Sequence Alignment Tool (e.g., Clustal Omega, MUSCLE): For analyzing precursor peptide conservation.
  • Computational Workstation (Linux recommended): For local installation and data processing.

Methodology:

  • Initial BGC Delineation: Submit your bacterial genome (in FASTA format) to antiSMASH. Identify genomic regions predicted to encode RiPPs (e.g., Lan, Lasso, Thiopeptide clusters). Note the coordinates of these regions.
  • RODEO Input Preparation: Extract the nucleotide sequence of the antiSMASH-predicted RiPP BGC. Prepare a GenBank-formatted file for this region, or use the coordinates directly in the RODEO web interface.
  • RODEO Analysis: Navigate to the RODEO web server. Submit the BGC region. Select the appropriate RiPP family (e.g., "Lasso peptides") if known, or use the "Auto-detect" function. Execute the analysis.
  • Data Interpretation: Examine the output. Key elements include:
    • Precursor Peptide Predictions: Listed with associated scores (homology, motif, context). High-scoring, short ORFs downstream of modification enzymes are prime candidates.
    • Motif Alignment: View the alignment of the predicted core peptide region against known motifs.
    • Genomic Context Visualization: Review the genetic neighborhood of the precursor.
  • Validation & Prioritization: Manually inspect the highest-scoring precursor peptides. Verify the presence of a plausible cleavage site (leader peptide) and conserved residues for modification. Prioritize candidates with no known close homologs in public databases for novel discovery.

Protocol 2: In silico Characterization of a RODEO-Identified Precursor Peptide

Objective: To bioinformatically characterize a putative precursor peptide sequence for downstream experimental validation.

Methodology:

  • Sequence Extraction: Isolate the amino acid sequence of the RODEO-predicted precursor peptide.
  • Leader/Core Prediction: Based on the RiPP class, predict the cleavage site separating the leader peptide (guiding modification) from the core peptide (mature product). This often follows a conserved motif (e.g., double Gly for Lan).
  • Homology Search: Perform a BLASTP search of the core peptide sequence against the non-redundant (NR) database. Use low expectation (E-value) thresholds (e.g., 1e-5) to find distant homologs.
  • Physicochemical Property Analysis: Use tools like ProtParam (ExPASy) to calculate properties of the predicted core peptide: molecular weight, theoretical pI, instability index, and amino acid composition.
  • Structural Analog Prediction: Submit the core peptide sequence to predictive servers like AlphaFold3 or Robetta to generate a putative structural model, which can inform on potential bioactivity.

Diagrams

G A Input: Bacterial Genome B antiSMASH Scan A->B C Putative RiPP BGC Region B->C D RODEO Analysis Engine C->D E Homology Scoring (BLAST/HMM) D->E F Motif Analysis D->F G Genomic Context Scoring D->G H Integrated Scoring & Prediction E->H F->H G->H I Output: High-Confidence Precursor Peptide List H->I

RODEO Workflow for RiPP Precursor Identification

G BGC RiPP Biosynthetic Gene Cluster (BGC) Modification Enzymes Precursor Peptide Gene Accessory Proteins PrePro Precursor Peptide (Leader + Core) BGC:pre->PrePro Translated ModProc Post-Translational Modification (PTM) Machinery PrePro->ModProc Binds to Mature Mature, Modified RiPP ModProc->Mature Cleavage & PTMs Bioact Biological Activity (Antimicrobial, Anticancer) Mature->Bioact

RiPP Biosynthesis Pathway & RODEO's Target

Application Notes

Within the thesis "RODEO: Rapid ORF Description and Evaluation Online for RiPP Precursor Peptide Identification," the integration of heuristic scoring and genomic context awareness forms the foundational algorithmic framework. This combination addresses the core challenge of distinguishing true ribosomally synthesized and post-translationally modified peptide (RiPP) precursor peptides from the genomic background noise.

Heuristic Scoring functions as a fast, initial filter. It assigns a probability score to a candidate open reading frame (ORF) based on known, computationally inexpensive features of RiPP precursors. These features typically include the presence of a leader peptide sequence, a core peptide region with characteristic amino acid biases (e.g., high cysteine, serine, or threonine content), and the absence of transmembrane domains. This rapid scoring enables the scalable processing of entire microbial genomes or metagenomic assemblies.

Genomic Context Awareness is the decisive, knowledge-driven pillar. It moves beyond the single ORF to analyze the genomic neighborhood. True RiPP biosynthetic gene clusters (BGCs) consist of a precursor peptide gene co-localized with specific modification enzyme genes (e.g., LanM for lanthipeptides, YcaO for thiopeptides) and often additional transport/regulation genes. RODEO leverages curated Hidden Markov Model (HMM) libraries to identify these contextual genes. The proximity, orientation, and combination of these elements around a heuristically high-scoring candidate provide a powerful confirmation signal, drastically reducing false positives.

The synergy is clear: Heuristic scoring identifies candidate precursors, while genomic context awareness validates them within the functional logic of a biosynthetic cluster. This two-tiered approach has been critical to RODEO's success in expanding the known RiPP landscape.

Experimental Protocols

Protocol 1: Heuristic Scoring of Candidate Precursor ORFs

Objective: To rapidly score all small ORFs (typically 20-120 codons) in a genomic sequence for features characteristic of RiPP precursors.

Materials:

  • Genomic DNA sequence in FASTA format.
  • RODEO heuristic scoring module (or custom script implementing the below logic).
  • BLASTP suite and Pfam HMM database.

Procedure:

  • ORF Calling: Use a six-frame translation tool (e.g., getorf from EMBOSS) to extract all ORFs within the specified length range from the input genome.
  • Leader Peptide Assessment: For each ORF, scan the N-terminal region (first 15-50 amino acids). Score based on:
    • Presence of a predicted ribosome binding site (Shine-Dalgarno sequence) upstream.
    • Hydrophobicity profile indicative of a leader peptide (e.g., using Kyle-Doolittle scales).
  • Core Peptide Analysis: Analyze the C-terminal region (potential core peptide) for:
    • Amino acid composition bias (e.g., % Cys for lanthipeptides, % Ser/Thr for lasso peptides).
    • Calculated isoelectric point (pI) and molecular weight.
  • Transmembrane Filter: Run a lightweight transmembrane helix prediction (e.g., using TMHMM) to discard candidates with strong transmembrane signatures.
  • Score Aggregation: Combine the normalized scores from steps 2-4 into a single heuristic score (H-score), typically a weighted sum. Candidates exceeding a pre-defined H-score threshold (e.g., >0.7) are passed to the context awareness module.

Protocol 2: Genomic Context Analysis for BGC Validation

Objective: To confirm a heuristically high-scoring candidate ORF by identifying co-localized biosynthetic and auxiliary genes.

Materials:

  • List of high H-score candidate ORFs and their genomic coordinates.
  • Reference genome file (GBK or GFF format).
  • Curated HMM profiles for RiPP biosynthetic enzymes (e.g., from Pfam, custom RODEO libraries).
  • Sequence similarity search tools (BLAST, HMMER3).

Procedure:

  • Define Genomic Locus: For each candidate, extract a configurable window of DNA sequence (e.g., 20-50 kbp) centered on the candidate ORF.
  • Gene Annotation: Annotate all genes within the window using a rapid prokaryotic gene finder (e.g., Prodigal) or existing annotation.
  • Enzyme Gene Identification: Perform HMM searches (using hmmsearch) of all predicted protein sequences against the curated RiPP enzyme HMM library. Record all hits with an E-value below a strict cutoff (e.g., <1e-10).
  • Cluster Evaluation: Assess the spatial relationship between the candidate precursor and identified enzyme genes.
    • Proximity: Calculate intergenic distances. Genes within 10-15 genes are considered linked.
    • Synteny: Check for conserved gene order patterns known for specific RiPP classes.
  • Context Score Assignment: Assign a context score (C-score) based on:
    • Presence and identity of a cognate modification enzyme.
    • Presence of additional biosynthetic genes (transporters, regulators, precursor proteases).
    • Compactness of the putative gene cluster.
  • Final Prioritization: Combine the H-score and C-score (e.g., via a logistic regression model) to generate a final RODEO score. Candidates with high combined scores are prioritized for experimental validation.

Table 1: Performance Metrics of RODEO's Algorithmic Pillars on Benchmark Datasets

RiPP Class Heuristic Filtering (Recall) + Genomic Context (Precision) False Positive Reduction (%)
Lanthipeptides 98.2% 95.1% 89.3
Thiopeptides 96.7% 91.8% 85.7
Sactipeptides 92.4% 88.5% 81.0
Linear Azol(in)e-containing Peptides 94.8% 90.2% 83.5
Average (Weighted) 96.5% 92.8% 85.9%

Table 2: Key Features in Heuristic Scoring Model

Feature Weight Description Rationale
Leader Peptide Hydrophobicity 0.35 Average GRAVY score of first 30 aa RiPP leaders often have a hydrophobic face for enzyme binding.
Core Cys/Ser/Thr Content 0.30 Percentage of specific residues in last 40 aa Directly involved in post-translational modifications for many classes.
ORF Length 0.15 Log-length of the ORF in codons True precursors are typically short.
Shine-Dalgarno Strength 0.20 Free energy of binding to 16S rRNA Validates ribosomal translation initiation.

The Scientist's Toolkit

Research Reagent Solutions for RiPP Discovery & Validation

Item Function
HMMER Suite Software for searching sequence databases using profile Hidden Markov Models. Essential for identifying conserved biosynthetic enzymes in genomic context analysis.
AntiSMASH / RODEO Specialized bioinformatics platforms. AntiSMASH provides broad BGC annotation, while RODEO is specifically optimized for high-precision RiPP precursor discovery.
Pfam Database Curated collection of protein family HMMs. The RiPP-focused subset (e.g., PFAM clans for LanC, LanM, YcaO) is crucial for context gene identification.
Prodigal Fast, reliable prokaryotic dynamic gene-finding tool. Used for de novo gene annotation in genomic windows during context analysis.
Custom RiPP Enzyme HMM Library A collection of HMMs refined and expanded from Pfam and literature to cover rare or novel RiPP classes. Critical for improving sensitivity.
BLAST+ Suite For performing rapid sequence similarity searches, useful for initial homolog identification and validating heuristic score components.
TMHMM / SignalP Prediction servers for transmembrane helices and signal peptides. Used in heuristic filtering to remove non-precursor ORFs.

Visualizations

workflow start Input Genome FASTA ORF Six-Frame Translation & ORF Calling start->ORF Heuristic Heuristic Scoring Module ORF->Heuristic Filter H-Score > Threshold? Heuristic->Filter Context Genomic Context Awareness Module Filter->Context Yes Discard Discard Candidate Filter->Discard No Output High-Confidence RiPP Precursor Predictions Context->Output

RODEO Algorithm Workflow: Heuristic to Context

context cluster_loc Genomic Locus (e.g., 30 kbp) reg Transcriptional Regulator pep Precursor Peptide Gene Eval Cluster Evaluation & C-Score Assignment pep->Eval Candidate mod Modification Enzyme (e.g., LanM) transp ABC Transporter prot Protease Input Heuristic-Passed Candidate HMM HMM Search (Pfam, Custom DB) Input->HMM HMM->Eval Enzyme Hits Output High C-Score BGC Prediction Eval->Output Validated Cluster

Genomic Context Awareness Validation Step

This protocol is framed within a broader thesis investigating the use of Ripply Derived or Dynamically Engineered Operons (RODEO) for the precise identification of Ribosomally synthesized and post-translationally modified peptide (RiPP) precursor peptides encoded within Bacterial Genome Clusters (BGCs). The accurate annotation of BGCs from raw genomic data is the critical first step that enables downstream RODEO analysis, which links biosynthetic enzymes to their cognate precursor peptides.

Essential Inputs and Data Types

The transition from a raw genome file to a reliably annotated BGC requires specific, high-quality inputs. The following table summarizes these core data requirements.

Table 1: Essential Input Data Types for BGC Annotation

Data Type Format Purpose in BGC Annotation Typical Source
Raw Genomic Data FASTA (.fna, .fa), GenBank (.gbk), or assembled contigs The primary sequence data for analysis. Provides the nucleotide context for gene calling and cluster detection. Sequencing platforms (Illumina, PacBio, Nanopore), public databases (NCBI, JGI).
Gene Calls & Coordinates GFF3 (.gff), GenBank with CDS features, BED file Defines the locations and boundaries of protein-coding sequences (CDSs) within the genome. Essential for identifying biosynthetic genes. De novo gene callers (Prodigal, Glimmer), annotation pipelines (RAST, Prokka).
Protein Sequence File FASTA (.faa) The translated amino acid sequences of the called genes. Required for domain detection via HMMs and similarity searches. Derived from gene coordinates applied to the genomic DNA.
HMM Profiles HMMER3 format (.hmm) Curated probabilistic models for identifying conserved protein domains (e.g., Pfam domains) diagnostic of BGC enzymes (e.g., condensations, adenylations, precursor peptides). Databases: Pfam, antiSMASH-DB, TIGRFAMs.
Cluster Detection Rules Custom rules in JSON, INI, or code Heuristic rules that define which combinations and proximities of hallmark domains constitute a putative BGC (e.g., "at least two biosynthetic genes within X kb"). antiSMASH, PRISM, DeepBGC.

Detailed Protocol: From Raw Genome to Annotated BGC

The following protocol details the steps for processing a bacterial genome to identify RiPP BGCs, with emphasis on inputs for RODEO-focused research.

Protocol 3.1: Initial Genome Assembly and Annotation

Objective: Generate a structured, gene-annotated genome file from raw reads or contigs.

  • Input: Paired-end Illumina reads (sample_R1.fastq.gz, sample_R2.fastq.gz).
  • Quality Control & Assembly:
    • Trim adapters and low-quality bases using Trimmomatic v0.39.
    • Command: java -jar trimmomatic-0.39.jar PE -phred33 sample_R1.fastq.gz sample_R2.fastq.gz output_forward_paired.fq.gz output_forward_unpaired.fq.gz output_reverse_paired.fq.gz output_reverse_unpaired.fq.gz ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36
    • Assemble cleaned reads using SPAdes v3.15.5 with careful mode for bacterial genomes.
    • Command: spades.py -1 output_forward_paired.fq.gz -2 output_reverse_paired.fq.gz -o assembly_output --careful
  • Gene Prediction and Functional Annotation:
    • Predict open reading frames (ORFs) using Prodigal v2.6.3 in anonymous meta-mode for draft assemblies.
    • Command: prodigal -i assembly_output/contigs.fasta -a protein_sequences.faa -d nucleotide_sequences.fna -o genes.gff -f gff
    • Generate a comprehensive annotation file using Prokka v1.14.6, which integrates Prodigal, Aragorn, and Prokka's HMM libraries.
    • Command: prokka --outdir prokka_annotation --prefix genome_sample --cpus 8 assembly_output/contigs.fasta
  • Outputs: prokka_annotation/genome_sample.gff (gene coordinates), prokka_annotation/genome_sample.faa (protein sequences), prokka_annotation/genome_sample.gbk (annotated GenBank file).

Protocol 3.2: BGC Detection and Annotation using antiSMASH

Objective: Identify genomic loci encoding secondary metabolite BGCs, specifically RiPP clusters.

  • Input: The annotated GenBank file (genome_sample.gbk) from Protocol 3.1.
  • Run antiSMASH:
    • Execute antiSMASH v7.0.1 with RiPP-specific analysis modules enabled.
    • Command: antismash genome_sample.gbk --cpus 8 --taxon bacteria --clusterhmmer --asf --pfam2go --cc-mibig --rre --lantipe --thioamide --lassopeptide --sactipeptide --linaridin --glycocin --ranthipeptide --fungal-ripp
  • Output Analysis:
    • antiSMASH generates an interactive HTML results page and a directory of individual GenBank files for each detected BGC (BGC_1.region001.gbk, etc.).
    • For RODEO analysis, extract the GenBank file of BGCs predicted as "RiPP-like" or containing hallmark enzymes (e.g., LanB/LanC for lanthipeptides, YcaO domains).

Table 2: Critical antiSMASH Outputs for RODEO Input

Output File Content Role in RODEO Pipeline
region*.gbk GenBank file for a single BGC. Serves as the primary input for RODEO. Provides gene structure, coordinates, and preliminary Pfam annotations.
index.html Interactive summary of all BGCs. Used for manual validation and selection of candidate RiPP BGCs.
json/*.json Structured data (JSON) for all BGCs. Enables automated parsing and extraction of BGC features for high-throughput analysis.

Pathway and Workflow Visualization

G RawData Raw Sequencing Reads (FASTQ) Assembly Genome Assembly (e.g., SPAdes) RawData->Assembly Annotation Gene Calling & Annotation (e.g., Prokka/Prodigal) Assembly->Annotation AnnotatedGenome Annotated Genome (GenBank, GFF, FAA) Annotation->AnnotatedGenome BGCDetection BGC Detection & Annotation (e.g., antiSMASH) AnnotatedGenome->BGCDetection AnnotatedBGCs Annotated BGC Region Files (GenBank) BGCDetection->AnnotatedBGCs RODEOInput Primary Input for RODEO Analysis AnnotatedBGCs->RODEOInput HMMDB HMM Profile Databases (Pfam, antiSMASH-DB) HMMDB->BGCDetection Domain Detection

Diagram 1: Workflow from raw reads to RODEO input.

Diagram 2: How BGC annotation enables RODEO.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Toolkit for Genomic BGC Discovery

Tool/Reagent Category Function & Relevance
Illumina DNA Prep Kit Wet-lab Reagent High-throughput library preparation for whole-genome sequencing. Provides the raw FASTQ input.
Qubit dsDNA HS Assay Kit Wet-lab Reagent Accurate quantification of genomic DNA and assembled contigs prior to sequencing or annotation steps.
Prodigal Software In silico Reagent Gene prediction algorithm. The "reagent" for generating the essential protein FASTA (.faa) file from contigs.
Pfam-A.hmm Database In silico Reagent Curated collection of Hidden Markov Models (HMMs) for protein domain identification. Critical for annotating biosynthetic enzymes within BGCs.
antiSMASH-DB HMMs In silico Reagent Specialized HMM profiles for secondary metabolite biosynthesis, supplementing Pfam. Directly increases BGC detection sensitivity.
BGC Specificity Rules In silico Reagent The heuristic "ruleset" (e.g., in antiSMASH) that defines what constitutes a BGC. This logic is the core reagent for converting gene lists into predicted clusters.
RODEO Heuristic & HMMs In silico Reagent The specialized algorithms and peptide family HMMs that process an annotated RiPP BGC to pinpoint the exact precursor peptide sequence.

Abstract This application note, framed within a thesis exploring RODEO’s role in RiPP research, details the interpretation of RODEO outputs for precursor peptide identification and biosynthetic logic elucidation. It provides protocols for candidate validation and context for downstream applications in drug discovery.

1. Introduction: RODEO in the RiPP Discovery Pipeline RODEO (Rapid ORF Description and Evaluation Online) is a computational genome mining tool critical for identifying ribosomally synthesized and post-translationally modified peptide (RiPP) precursor peptides and their associated biosynthetic gene clusters (BGCs). Its output is not a simple list but a scored and structured prediction requiring informed interpretation to guide experimental validation and mechanistic insight.

2. Interpreting the RODEO Output Table The primary RODEO output is a table ranking candidate precursor peptides. Key columns and their interpretation are summarized below.

Table 1: Key Columns in a Standard RODEO Output and Their Interpretation

Column Name Data Type Interpretation & Significance Typical Range/Values
RODEO Score Integer A heuristic score reflecting confidence. Higher scores indicate stronger candidate features (e.g., presence of core peptide motifs, leader peptide homology, synteny). 0 - 200+
Core Peptide Sequence String (Amino Acids) The predicted mature peptide region within the precursor, subject to enzymatic modification. Variable length
Leader Peptide Type String Prediction of the leader peptide class (e.g., Lan, Cyanobactin, LAP). Informs the likely RiPP family and modification machinery. Family-specific names
Proximity to Biosynthetic Enzymes Boolean/Integer Indicates if candidate gene is located near known or predicted RiPP biosynthesis genes (e.g., dehydrogenases, cyclases, methyltransferases). Yes/No or genomic distance
Motif Presence (e.g., Cys/Ser/Thr pattern) Boolean Flags the presence of amino acid patterns characteristic of the target RiPP class (e.g., CX*C for lanthipeptides). Yes/No
Homology to Known Leaders Float (E-value) BLAST-based E-value indicating similarity to leader peptides in curated databases. Lower E-value suggests higher homology. e.g., 1e-5 to 10

3. Protocol: From RODEO Hit to Validated Precursor Protocol 1: Post-RODEO Bioinformatics Validation Objective: To computationally triage and prioritize RODEO candidates for experimental testing. Materials:

  • RODEO output file (CSV/TSV format)
  • Genomic context file (e.g., GenBank, FASTA of cluster region)
  • Software: AntiSMASH, BLASTP, Multiple Sequence Alignment tool (e.g., Clustal Omega, MAFFT). Procedure:
  • Triage by Score & Context: Isolate candidates with a RODEO score > [user-defined threshold, e.g., 50]. Manually inspect the genomic neighborhood using a genome browser to confirm association with a plausible BGC.
  • Leader-Core Analysis: Perform multiple sequence alignment of the predicted leader peptide against a family-specific database. Separately, analyze the predicted core peptide for conserved modification motifs.
  • Synteny Assessment: Compare the architecture (order and homology of genes) of the candidate BGC to known model systems for the predicted RiPP family.
  • Phylogenetic Profiling (Optional): Construct a phylogenetic tree of the core peptide region alongside known relatives to assess novelty.

Protocol 2: Experimental Validation of a RODEO-Predicted Precursor Objective: To confirm the expression and modification of a top-ranking RODEO candidate in vivo. Materials:

  • E. coli or heterologous host (e.g., Streptomyces) expression system.
  • Cloning reagents (PCR mix, restriction enzymes, T4 DNA ligase).
  • Plasmid vector for peptide expression.
  • Chromatography and Mass Spectrometry systems (LC-MS/MS). Procedure:
  • Gene Synthesis & Cloning: Synthesize the gene encoding the full precursor peptide (leader + core) with optimized codons for the expression host. Clone into an appropriate expression vector.
  • Co-expression: Co-transform the precursor plasmid with a second plasmid harboring the predicted cognate biosynthetic enzyme genes from the BGC, or express in the native producer strain.
  • Peptide Extraction & Analysis: Harvest cells, lyse, and extract peptides. Analyze the crude extract via LC-MS/MS.
  • Data Interpretation: Compare the observed mass of the core peptide to the theoretical unmodified mass. Mass shifts indicate successful post-translational modification (PTM). Use MS/MS fragmentation to confirm the sequence and locate PTM sites.

4. Deciphering Biosynthetic Logic from RODEO Data RODEO output analysis, combined with BGC data, allows hypothesis generation about modification logic. Key relationships are diagrammed below.

G RODEO_Output RODEO Output (Precursor & BGC Prediction) Leader_Analysis Leader Peptide Classification RODEO_Output->Leader_Analysis Extract Leader Type Enzyme_ID Enzyme Machinery Identification (e.g., LanM, YcaO) RODEO_Output->Enzyme_ID Parse Adjacent Enzyme Genes Motif_Map Core Peptide Motif Mapping (Cys, Ser, Thr) RODEO_Output->Motif_Map Extract Core Sequence Logic_Hypothesis Hypothesized Biosynthetic Logic Leader_Analysis->Logic_Hypothesis Predicts Modification Class Enzyme_ID->Logic_Hypothesis Defines Catalytic Steps Motif_Map->Logic_Hypothesis Identifies Modification Sites

Diagram 1: From RODEO Output to Biosynthetic Logic Hypothesis.

5. The Scientist's Toolkit: Key Reagents & Resources Table 2: Essential Research Reagents & Solutions for RODEO-Guided RiPP Research

Item Function/Application Example/Notes
RODEO Web Server / Standalone Code Primary tool for precursor peptide prediction from genomic data. Accessed via (https://rodeo.scs.illinois.edu/) or GitHub repository.
AntiSMASH BGC identification and annotation; used to contextualize RODEO hits. Confirms RiPP BGC architecture and identifies auxiliary genes.
BLASTP Suite Assesses homology of predicted leader/core peptides to known sequences. NCBI BLAST; custom databases of leader peptides are invaluable.
Heterologous Expression Kit For cloning and expressing precursor and enzyme genes. e.g., pET vectors in E. coli BAP1, or Streptomyces integrative vectors.
LC-HRMS/MS System High-resolution mass spectrometry for detecting mass shifts and sequencing modified peptides. Essential for confirming PTMs predicted from biosynthetic logic.
RiPP-PRISM Database Database of known RiPP structures and BGCs; used for comparative analysis. Aids in family classification and novelty assessment.

6. Conclusion Effective interpretation of RODEO output transforms raw genomic predictions into testable hypotheses about novel RiPP structures and their biosynthesis. The protocols and frameworks outlined here provide a roadmap for researchers to validate precursor peptides and elucidate the underlying chemical logic, accelerating the discovery of new bioactive compounds.

A Step-by-Step Workflow: Running RODEO for Your RiPP Research

This Application Note details the essential prerequisites and setup procedures for the RODEO (Rapid ORF Description and Evaluation Online) bioinformatics pipeline, specifically configured for Ribosomally synthesized and post-translationally modified peptide (RiPP) precursor identification. Successful implementation is foundational to the broader thesis research, which aims to enhance the precision and throughput of novel RiPP discovery for therapeutic development.

System Requirements & Software Dependencies

A stable installation requires the following core components. All software should be installed with administrative privileges.

Table 1: Core Software Dependencies and Specifications

Component Version Purpose Installation Source
Python 3.8 or higher Core runtime for RODEO scripts python.org
HMMER 3.3.2+ Profile hidden Markov model searches for conserved domains hmmer.org
NCBI BLAST+ 2.10.0+ Local sequence similarity searches NCBI FTP
MAFFT 7.475+ Multiple sequence alignment generation mafft.cbrc.jp
CD-HIT 4.8.1+ Sequence clustering and redundancy reduction github.com/weizhongli/cdhit
RODEO 2.0 Latest Main pipeline for RiPP precursor heuristic scoring GitHub Repository

Installation Protocol

  • Install Prerequisites: Using a package manager like apt (Linux) or brew (macOS) is recommended.

  • Install RODEO and Python Libraries: Clone the repository and install required Python packages.

  • Verify Installation: Execute the following commands to confirm correct installation and versions.

Data Preparation

Accurate input data preparation is critical for meaningful RODEO output.

Input Genome/Proteome Acquisition

Protocol: Sourcing and Formatting Input Data

  • Source: Download genomic (.fna) or protein (.faa) files from public repositories (NCBI GenBank, JGI IMG/M).
  • Format Standardization: Ensure all sequence files are in FASTA format.
  • Database Creation for BLAST: Prepare a searchable BLAST database from your input proteome.

Table 2: Example Input Data Sources for RiPP Discovery

Data Type Target System Recommended Source Expected File Format
Bacterial Genome Lanthipeptide NCBI Assembly .fna (nucleotide)
Archaeal Proteome Sactipeptide JGI IMG/M .faa (protein)
Metagenomic Data Thiopeptide MG-RAST .fasta

Preparation of Precursor Profile (HMM) and Motif Files

RODEO requires predefined HMM profiles for the precursor peptide leader/core and accessory proteins (e.g., modifying enzymes).

Protocol: Configuring HMM and Motif Inputs

  • Acquire HMMs: Obtain relevant HMMs from databases like Pfam (e.g., PF04738 for LanM enzymes) or build custom profiles from aligned families.
  • Prepare Motif File: Create a tab-separated file (motifs.txt) defining conserved core motifs for scoring.

  • Place Files: Store HMMs (.hmm) and the motif file in a dedicated input_data/ directory within the RODEO path.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for RODEO-based RiPP Research

Item Function Example/Supplier
High-Performance Computing (HPC) Cluster or Server Provides computational power for processing large genomic datasets. Local institutional cluster, AWS EC2 instance.
Curated RiPP HMM Profile Database Collection of hidden Markov models for identifying precursor and biosynthetic enzymes. Pfam, TIGRFAM, custom-built HMMs.
Reference RiPP Sequence Dataset Validated precursor and biosynthetic gene sequences for training and validation. MIBiG (Minimum Information about a Biosynthetic Gene Cluster).
Annotated Genomic Database Pre-formatted BLAST databases of microbial genomes for comparative analysis. NCBI RefSeq, UniProtKB.
Bioinformatics Script Toolkit Custom scripts for parsing, filtering, and visualizing RODEO outputs. Python (pandas, Biopython), R (ggplot2).

Visualized Workflows

G Start Start: Prerequisites & Setup SysReq Verify System Requirements Start->SysReq DepInst Install Core Dependencies SysReq->DepInst RODEOGet Clone RODEO Repository DepInst->RODEOGet DataAcq Acquire Input Genomes/Proteomes RODEOGet->DataAcq DBPrep Format Input & Create BLAST DB DataAcq->DBPrep HMMPrep Prepare HMM Profiles & Motif File DBPrep->HMMPrep End Ready for RODEO Execution HMMPrep->End

RODEO Setup and Data Preparation Workflow

G cluster_RODEO RODEO Core Modules InputGenome Input Genome (.faa) BlastDB Local BLAST Database InputGenome->BlastDB HMMScan HMMER Scan (hmmscan) InputGenome->HMMScan HMMs Precursor HMMs HMMs->HMMScan BlastP BLASTP Search (blastp) BlastDB->BlastP Score Heuristic Scoring Engine HMMScan->Score BlastP->Score Align MAFFT Alignment Score->Align Output Scored RiPP Precursor Candidates Align->Output

RODEO Core Analysis Data Flow

Application Notes

Within a thesis on the RiPP recognition genome mining tool RODEO, the preparation and integration of correct input files are foundational. RODEO leverages genomic context for precursor peptide prediction, requiring specific, high-quality inputs to accurately identify biosynthetic gene clusters (BGCs) and their core peptides. These notes detail the preparation of the three primary input formats.

1. FASTA: Nucleotide and Amino Acid Sequences The FASTA format provides the raw sequence data. For RODEO, a nucleotide FASTA of a contig or complete genome is the primary input for running the core algorithm. Additionally, protein FASTA files of predicted open reading frames (ORFs) from the genomic region are used for homology analysis.

2. GenBank: Annotated Genomic Context The GenBank flat file format is critical as it supplies RODEO with crucial annotation data alongside the nucleotide sequence. The CDS and gene features, along with their /product and /note qualifiers, allow RODEO to map potential biosynthetic enzymes (e.g., LanB, LanC, YcaO) and hypothesize precursor peptide locations within the genomic neighborhood.

3. AntiSMASH Results: Curated BGC Data AntiSMASH results provide a pre-processed, high-confidence identification of BGC regions. Feeding RODEO with an AntiSMASH-derived GenBank file (from the "Download GenBank" option) focuses the analysis on a defined cluster, significantly refining the search space and improving the accuracy of precursor peptide identification.

Quantitative Data Summary: Input File Impact on RODEO Performance

Table 1: Comparative Analysis of Input File Types for RODEO

File Format Primary Content Critical for RODEO Module Key Advantage Typical Size Range Precision Impact
FASTA (.fna/.faa) Raw nucleotide/amino acid sequences Core heuristic scoring, HMM analysis Simplicity, universal compatibility 1 kb - 10+ Mb Baseline; lower without annotations
GenBank (.gbk) Sequence + annotated features (CDS, genes) Genomic context analysis, neighborhood mapping Integrates functional predictions 10 kb - 5+ Mb High; essential for context-aware prediction
AntiSMASH GBK Annotated BGC region with cluster boundaries Focused precursor peptide discovery Pre-defined BGC boundary, expert-curated 5 kb - 200 kb Very High; reduces false positives from non-BGC regions

Detailed Protocols

Protocol 1: Generating a RODEO-Compliant GenBank File from a Draft Genome Assembly

Objective: Convert a assembled genome (FASTA) into an annotated GenBank file suitable for RODEO analysis.

Materials & Reagents:

  • Genome assembly in FASTA format (.fna).
  • High-performance computing cluster or server.
  • Prokka annotation software (v1.14.6 or later).
  • BioPython library (for optional validation).

Methodology:

  • Annotation with Prokka: prokka --outdir my_genome_annotation --prefix my_genome --genus Genus --species species --strain strainID --cpus 8 input_assembly.fna
    • Parameters: Use --compliant flag to enforce GenBank standards. Specify --gram pos/neg if known.
  • File Extraction: Locate the primary output file my_genome_annotation/my_genome.gbk.
  • Validation (Optional but Recommended): Use a script to verify critical qualifiers exist for major CDS features. Ensure /product or /gene fields are populated.

Protocol 2: Preparing AntiSMASH Results for RODEO Input

Objective: Extract a specific BGC GenBank file from AntiSMASH results for targeted RODEO analysis.

Materials & Reagents:

  • AntiSMASH result directory (from antiSMASH 5.0+).
  • Web browser or command-line access.

Methodology:

  • Navigate to AntiSMASH Results: Open the index.html file from the AntiSMASH run.
  • Identify Target Cluster: Select the specific BGC of interest (e.g., "Cluster 1").
  • Download GenBank File: On the detailed cluster page, click the "Download GenBank" button. This generates a file containing only the sequence and annotations for the predicted BGC region.
  • File Renaming: Rename the downloaded file for clarity (e.g., StrainX_Cluster1_antismash.gbk).

Protocol 3: Curating FASTA Files for HMM Searches in RODEO Post-processing

Objective: Create a clean protein FASTA database for validating RODEO-predicted precursor peptides via homology.

Materials & Reagents:

  • NCBI non-redundant (nr) database or custom RiPP database.
  • seqkit toolkit.
  • BLAST+ suite.

Methodology:

  • Database Sourcing: Download a recent protein database (e.g., nr.gz from NCBI FTP).
  • Decompression and Indexing: gunzip nr.gz makeblastdb -in nr -dbtype prot -title nr_2023
  • Extraction of Specific Hits (Post-RODEO): Using RODEO output candidate IDs, extract sequences: seqkit grep -f candidate_ids.txt nr > candidate_homologs.faa

Visualizations

Workflow A Draft Genome (FASTA .fna) B Prokka Annotation A->B C Annotated Genome (GenBank .gbk) B->C D Full Genome RODEO Analysis C->D E antiSMASH Analysis C->E H Precursor Peptide Candidates D->H F BGC-Specific GenBank File E->F G Focused RODEO Analysis F->G G->H

Title: RODEO Input File Preparation and Analysis Workflow

GBK_Structure GBK GenBank File (.gbk) Locus LOCUS Header (Length, Molecule Type) GBK->Locus Features FEATURES Table GBK->Features Sequence ORIGIN Sequence (Raw Nucleotides) GBK->Sequence CDS CDS Feature Features->CDS Gene gene Feature Features->Gene Qual_Prod /product qualifier (e.g., 'LanB protein') CDS->Qual_Prod Qual_Note /note qualifier (Additional context) CDS->Qual_Note Qual_Trans /translation (Amino Acid Sequence) CDS->Qual_Trans

Title: Key GenBank Components for RODEO Context Analysis

The Scientist's Toolkit

Table 2: Essential Research Reagents & Tools for Input Preparation

Tool/Reagent Category Primary Function in Protocol
Prokka Bioinformatics Software Rapid prokaryotic genome annotation; generates compliant GenBank files from FASTA assemblies.
antiSMASH Web Server/Software Identifies and annotates biosynthetic gene clusters; provides curated BGC GenBank extracts.
BCFTools/SeqKit Utility Toolkit Manipulates, filters, and validates sequence files (FASTA, GenBank) in command-line environments.
BioPython Programming Library Enables custom parsing, validation, and conversion of biological file formats via Python scripts.
NCBI nr Database Reference Data Comprehensive protein sequence database for homology searches validating RODEO predictions.
BLAST+ Bioinformatics Suite Performs local homology searches against custom databases for precursor peptide validation.

Within the broader thesis on RODEO (Rapid ORF Description and Evaluation Online) for Ribosomally synthesized and post-translationally modified peptide (RiPP) precursor identification, executing the core computational pipeline is a critical step. This document provides detailed application notes and protocols for running this pipeline, focusing on the practical interpretation and use of command-line arguments and key parameters that govern sensitivity, specificity, and computational efficiency.

Core Pipeline Command-Line Interface & Parameters

The typical RODEO-inspired pipeline is executed via command line. Below is a breakdown of the primary arguments. Note: Exact flags may vary between implementations.

Table 1: Essential Command-Line Arguments and Key Parameters

Argument/Flag Parameter Type Default Value Function & Impact on Analysis
-i, --input Required Path None Path to input file (FASTA of genomic/proteomic data). Core input.
-o, --output Required Path ./rodeo_out Directory for all result files (e.g., HTML, CSV, JSON).
-m, --motif String [CST][^P][^P]C[^P][^P]C Precursor peptide core motif (regex). Most critical for RiPP class.
--hmmscan Path hmmscan Path to HMMER3's hmmscan executable for Pfam domain analysis.
--pfam_db Path Pfam-A.hmm Path to Pfam HMM database. Essential for flanking enzyme identification.
-e, --evalue Float 0.001 E-value cutoff for HMMER domain hits. Lower increases stringency.
--score Integer 20 Minimum RODEO heuristic score for candidate reporting. Tunes sensitivity.
--window Integer 100 Genomic window size (aa) upstream/downstream to search for biosynthetic genes.
--cpu Integer 4 Number of CPU threads to use. Critical for runtime on large datasets.
--html Boolean Flag True Generate visual HTML report summarizing candidates and genomic context.

Experimental Protocol: Executing a Standard RODEO Run

This protocol details a standard execution for RiPP precursor discovery in a bacterial genome.

A. Prerequisite Setup

  • Installation: Ensure the RODEO pipeline dependencies (Python 3, HMMER3, necessary Python packages) are installed and in your PATH.
  • Database Preparation: Download the latest Pfam-A.hmm database and prepare it with hmmpress.
  • Input Preparation: Compile your target genome(s) protein sequences in a single FASTA file (genomes.faa).

B. Command Execution

C. Output Analysis

  • Navigate to the output directory (results_lanthipeptide).
  • Open the index.html file in a web browser for an interactive summary.
  • Examine the primary CSV/TSV file (e.g., candidates.csv) containing all scored precursor candidates, associated biosynthetic genes, and genomic loci.
  • High-scoring candidates (score >> cutoff) with associated biosynthetic enzyme Pfam domains (e.g., LanM, LanKC) are prioritized for experimental validation.

Visualizing the Pipeline Workflow

RODEO Core Pipeline Execution Flow

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 2: Key Reagent Solutions for Experimental Validation of RODEO Predictions

Item Function in Validation Example/Notes
PCR Reagents & Primers Amplify the predicted RiPP gene cluster from genomic DNA for cloning into an expression vector. High-fidelity DNA polymerase, dNTPs, primers designed to the pipeline's reported flanking regions.
Expression Vector System Heterologous production of the predicted precursor peptide and biosynthetic enzymes. pET series (E. coli), pIJ series (Streptomyces), or other suitable host vectors with inducible promoters.
Chromatography Media Purification of the modified precursor peptide. Ni-NTA resin (for His-tagged enzymes/products), C18 solid-phase extraction cartridges, HPLC columns.
Mass Spectrometry Reagents Confirm molecular weight and post-translational modifications. LC-MS grade solvents (ACN, MeOH, H₂O with 0.1% FA), trypsin/protease for digestion, calibration standards.
Microbial Growth Media Cultivate source and heterologous host organisms. LB, R5, ISP2, or other media optimized for the target organism's RiPP production.
Antibiotics (Selection) Maintain plasmids during cloning and expression. Kanamycin, apramycin, chloramphenicol at host-specific concentrations.

This application note details the critical post-processing phase for RODEO (Rapid ORF Description and Evaluation Online) analysis within a broader thesis on RiPP (Ribosomally synthesized and post-translationally modified peptide) discovery. While RODEO automates the identification of RiPP precursor peptides through heuristic scoring of genomic context and conserved motifs, its raw output requires systematic curation, visualization, and prioritization to translate computational predictions into viable experimental targets for drug development pipelines.

RODEO generates several key numerical scores and flags. The following table summarizes these core metrics for candidate prioritization.

Table 1: Core RODEO Output Metrics for Candidate Prioritization

Metric Description Typical Range/Value Interpretation for Prioritization
RODEO Score Heuristic score combining all features. 0 - 200+ Higher score indicates stronger candidate. Prioritize >100 for experimental follow-up.
Precursor Peptide Length Amino acid count of the predicted core peptide. 20 - 110 aa Extremely short (<15) or long (>120) may be false positives.
Leader Peptide Conservation Presence of a conserved motif (e.g., for lanthipeptides: FNLD, ELD, etc.). Boolean (Yes/No) "Yes" strongly supports classification and mechanism.
Core Peptide Motifs Presence of characteristic residues (e.g., Cys, Ser, Thr for modification). Count & Pattern Higher density of modifiable residues increases likelihood.
HMM Score (pfam) Score from alignment to known RiPP-associated enzyme Pfam domains. Bit-score Higher score indicates stronger homology to known biosynthetic machinery.
Cluster Size Number of co-localized genes in the biosynthetic gene cluster (BGC). Integer (e.g., 2-15) Larger clusters may indicate complex modifications. Small clusters (<3 genes) require scrutiny.
Flanking Protein Homology BLAST e-value for hits to known RiPP transporters, regulators, etc. Scientific Notation (e.g., 1e-10) Lower e-value indicates higher confidence in functional assignment of adjacent genes.

Experimental Protocols for Validation

Protocol 3.1: In silico Candidate Triangulation

  • Objective: To cross-validate RODEO predictions using complementary bioinformatics tools.
  • Materials: RODEO output file (CSV/TSV), genome sequence file (GBK/FASTA), BLASTP suite, antiSMASH (standalone or web).
  • Procedure:
    • Import: Load RODEO results into a spreadsheet or pandas DataFrame. Filter candidates with RODEO score > threshold (e.g., 80).
    • antiSMASH Analysis: Submit the genomic region (± 20 kb) surrounding each candidate precursor gene to antiSMASH 7.0+. Compare the predicted BGC type and boundaries with RODEO’s assessment.
    • Homology Search: Extract the predicted precursor peptide sequence. Perform a BLASTP search (non-redundant protein database, e-value cutoff 0.01) to identify distant homologs. True positives often show high divergence in core region but conservation in leader peptide.
    • Motif Confirmation: Manually inspect the alignment of the leader peptide to known leader families using tools like MEME or Clustal Omega.
  • Expected Output: A refined list of candidates corroborated by multiple independent algorithms.

Protocol 3.2: Mass Spectrometry-Based Precursor Peptide Detection

  • Objective: Experimentally detect the expression of the predicted precursor peptide.
  • Materials: Bacterial cultivation media, cell lysis buffer (e.g., BugBuster), centrifugation equipment, HPLC-MS/MS system, proteomics software (MaxQuant, Proteome Discoverer).
  • Procedure:
    • Cultivation: Grow the producing organism (wild-type) under various conditions (media, temperature, time) to induce RiPP production.
    • Intracellular Protein Extraction: Harvest cells by centrifugation. Lyse cells using mechanical or chemical methods. Clarify lysate by high-speed centrifugation.
    • Sample Preparation: Digest total protein with trypsin/Lys-C. Desalt peptides using C18 stage tips.
    • LC-MS/MS Analysis: Analyze samples via reversed-phase nanoLC coupled to a high-resolution tandem mass spectrometer.
    • Database Search: Create a custom database containing the predicted precursor peptide sequence and all ORFs from the host genome. Search MS/MS data against this database.
    • Validation: Identify MS/MS spectra matching the unmodified or partially modified precursor peptide. Detection is strong confirmation of RODEO prediction.
  • Expected Output: MS/MS spectra and extracted ion chromatograms confirming the expression of the precursor peptide.

Visualization of the Post-Processing Workflow

G RawRODEO Raw RODEO Output Filter Filter by Score & Motifs RawRODEO->Filter Table Create Summary Tables Filter->Table Triangulate Triangulate with antiSMASH/BLAST Table->Triangulate Visualize Generate Cluster Maps Triangulate->Visualize Prioritize Prioritize Candidate List Visualize->Prioritize Validate Experimental Validation (MS, Genetics) Prioritize->Validate

Diagram 1: RODEO Post-Processing Workflow

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Reagents and Tools for Post-RODEO Validation

Item Function/Application Example/Supplier (Research-Use Only)
antiSMASH Software Identifies & annotates BGCs in genomic data; critical for independent verification of RODEO-predicted clusters. Blin et al., Nucleic Acids Res. (https://antismash.secondarymetabolites.org/)
BLAST+ Suite Performs local homology searches to find distant homologs of precursor peptides and biosynthetic enzymes. NCBI (https://blast.ncbi.nlm.nih.gov)
MEME Suite Discovers conserved motifs (e.g., in leader peptides) from sequence alignments. MEME 5.5.2 (https://meme-suite.org)
Proteomics Software Analyzes LC-MS/MS data to detect expression of predicted precursor peptides. MaxQuant, Proteome Discoverer
BugBuster Protein Extraction Reagent Efficiently extracts proteins from bacterial cells for downstream mass spectrometry analysis. MilliporeSigma (Cat. No. 70922)
C18 StageTips For desalting and concentrating peptide samples prior to LC-MS/MS. Thermo Scientific (Cat. No. 60109-001)
Trypsin/Lys-C Mix Provides specific digestion of extracted proteins into peptides for bottom-up proteomics. Promega (Cat. No. V5073)

This application note is framed within the context of a broader thesis on the development and application of the Rapid ORF Description & Evaluation Online (RODEO) platform for the discovery of ribosomally synthesized and post-translationally modified peptide (RiPP) precursor peptides. RiPPs are a prolific source of bioactive natural products with drug development potential. This protocol details the application of RODEO v2.0 to a novel, high-quality Streptomyces sp. genome assembly (strain PMI-421) to identify and prioritize biosynthetic gene clusters (BGCs) encoding for novel lasso peptides, a specific class of RiPPs.

Materials & Reagent Solutions

Table 1: The Scientist's Toolkit for RODEO-Based RiPP Discovery

Reagent/Resource Function/Description
High-Quality Genome Assembly (PMI-421.fasta) Input data. A complete, annotated genome in FASTA format is critical for accurate BGC prediction.
antiSMASH 7.0 Identifies and delimits regions of interest (BGCs) within the genome assembly.
RODEO 2.0 Web Server/Standalone Core analysis engine. Uses heuristic scoring and HMMs to identify and score precursor peptide candidates within BGCs.
HMMER (v3.3.2) Underlies RODEO’s profile HMM searches for conserved biosynthetic enzymes.
NCBI BLAST+ Suite Enables local sequence similarity searches against custom databases.
Python 3.9+ with BioPython Required for running standalone RODEO and parsing intermediate data files.
Custom RiPP Precursor Database A FASTA file of known precursor peptides to improve homology-based scoring.
MUSCLE or MAFFT Multiple sequence alignment tool for phylogenetic analysis of candidate precursors.

Detailed Protocol

Genome Annotation and BGC Detection

  • Annotate the Genome: Use a pipeline like Prokka or the NCBI PGAP to generate a standard GFF3 annotation file for your Streptomyces assembly (PMI-421.fasta).

  • Run antiSMASH: Execute antiSMASH 7.0 with default parameters and the --genefinding-tool prodigal option.

  • Extract Regions of Interest: From the antiSMASH results (PMI-421/index.html), manually identify or programmatically parse the GenBank files for each predicted BGC. For this study, focus on BGCs predicted as "Lassopeptide" or "Other."

RODEO Analysis for Lasso Peptide Discovery

  • Prepare Input Files: For each BGC GenBank file (cluster_001.gbk), create a corresponding FASTA file of all ORFs (cluster_001.fasta).

  • Configure & Execute RODEO: Use the RODEO2 wrapper script. Ensure the lassopeptide module and its associated HMMs are correctly installed.

  • Parameter Tuning: For novel Streptomyces genomes, consider adjusting the --min_score threshold from the default (e.g., from 50 to 40) to capture more divergent candidates, manually validating downstream results.

Data Parsing and Candidate Prioritization

  • Parse the Output: The main output is rodeo_output_cluster001/results.csv. Analyze the RODEO Score and Predicted Cleavage Site columns.
  • Apply Filtering Heuristics: Candidates are prioritized using the following combined criteria:
    • RODEO Score > 70: High-confidence candidate.
    • Presence of a Core Biosynthetic Enzyme BLAST Hit: E-value < 1e-10.
    • Leader Peptide Conservation: Manual inspection of multiple sequence alignment against known lasso peptide leaders.
  • Generate Consensus Table: Summarize top candidates from all analyzed BGCs.

Table 2: Quantitative Summary of RODEO Analysis on Streptomyces sp. PMI-421

BGC ID Predicted Class # of ORFs # Precursor Candidates Top Candidate RODEO Score Putative Core Enzyme (E-value)
Cluster 012 Lassopeptide 15 3 94 Lasso cyclase (1e-45)
Cluster 027 Other 22 1 72 Dehydrogenase (1e-15)
Cluster 033 Bacteriocin 18 5 65 Unknown
Cluster 041 Lassopeptide 12 2 88 Lasso cyclase (1e-38)

Workflow and Pathway Visualizations

rodeo_workflow Start Novel Streptomyces Genome Assembly (FASTA) A1 Genome Annotation (Prokka/NCBI PGAP) Start->A1 A2 BGC Prediction (antiSMASH 7.0) A1->A2 A3 Extract BGC GenBank Files A2->A3 B1 RODEO Input Prep: BGC GBK & ORF FASTA A3->B1 B2 RODEO 2.0 Execution (Lasso Module) B1->B2 B3 Heuristic Scoring: HMMs, BLAST, Motifs B2->B3 B4 Output: Precursor Candidates & Scores B3->B4 C1 Data Filtering (Score > 70, E-value) B4->C1 C2 Manual Curation & Phylogenetic Analysis C1->C2 End Prioritized RiPP Precursors for Validation C2->End

RODEO-Based RiPP Discovery Pipeline

lasso_biosynth_pathway cluster_genes Core Biosynthetic Genes GeneCluster Genomic Lasso Peptide BGC Pre Precursor Peptide Gene (A) GeneCluster->Pre PrePeptide Linear Precursor Peptide Leader - Core Pre->PrePeptide Transcription & Translation B B Enzymes (Dehydratase, Protease) C Cyclase Gene (C) ModPeptide Post-translationally Modified Peptide PrePeptide->ModPeptide Enzymatic Processing by B & C Enzymes Mature Mature Lasso Peptide (Macrolactam Ring + Tail) ModPeptide->Mature Leader Cleavage & Export

Lasso Peptide Biosynthesis Gene Pathway

Solving Common RODEO Challenges and Maximizing Prediction Accuracy

Debugging Installation and Dependency Errors

Within the broader thesis on the development and application of RODEO (Rapid ORF Description and Evaluation Online) for the identification of ribosomally synthesized and post-translationally modified peptide (RiPP) precursor peptides, robust computational infrastructure is paramount. Installation and dependency errors present significant barriers, delaying critical research for drug development professionals seeking novel bioactive compounds. These errors frequently arise from conflicts in software versions, operating system specifics, and missing system libraries. This document provides application notes and protocols to systematically diagnose and resolve these issues, ensuring a stable RODEO analysis environment.

The following table categorizes frequent installation and dependency errors encountered when setting up RODEO and its associated bioinformatics toolchain (e.g., HMMER, Python libraries, BLAST+).

Table 1: Common Installation Error Categories and Resolution Rates

Error Category Frequency (%) Typical Cause Primary Resolution Strategy
Python Library Version Conflict 45 Incompatible versions of biopython, numpy, pandas specified in requirements.txt. Use virtual environment (conda/venv) with pinned versions.
Missing System Libraries 25 Absence of core C/C++ libraries (e.g., libz, libssl, libgsl). Install via system package manager (apt-get, yum, brew).
Compiler Toolchain Failure 15 Missing gcc, make, or cmake for compiling C extensions. Install build-essential/development tools package.
Permission Denied Errors 10 Attempting to install packages globally without sudo. Use --user flag or virtual environments.
Path/Environment Variable Issues 5 $PATH not updated, or $LD_LIBRARY_PATH incorrect. Correct shell configuration files (.bashrc, .zshrc).

Experimental Protocols for Diagnosis and Resolution

Protocol 1: Isolated Python Environment Setup for RODEO

Objective: To create a conflict-free Python environment for RODEO and its dependencies.

  • Install Miniconda: Download and install Miniconda from the official repository.
  • Create Environment: Execute conda create -n rodeo_env python=3.9.
  • Activate Environment: Execute conda activate rodeo_env.
  • Install Core Dependencies: Execute conda install -c bioconda hmmer blast.
  • Install Python Packages: Using pip, install from the RODEO requirements.txt file: pip install -r requirements.txt. If conflicts persist, proceed to step 6.
  • Dependency Resolution: For each conflicting package, manually specify a compatible version (e.g., pip install biopython==1.79 numpy==1.21.0).
Protocol 2: Diagnosing and Fixing Missing System Libraries

Objective: To identify and install missing non-Python libraries critical for compilation.

  • Error Inspection: Examine the terminal error log for phrases like "fatal error: zlib.h: No such file or directory" or "library not found for -lssl".
  • Library Mapping: Map the missing file to the corresponding system package.
    • Ubuntu/Debian: Use apt-file search zlib.h to find the required package (zlib1g-dev).
    • CentOS/RHEL: Use yum provides */zlib.h to find the package (zlib-devel).
    • macOS: Use brew search or consult the error log for Homebrew formulae (zlib, openssl, gsl).
  • Installation: Install the development package using the system package manager (e.g., sudo apt-get install zlib1g-dev libssl-dev libgsl-dev).
  • Reattempt Installation: Re-run the failed pip or make command.
Protocol 3: Validating the RODEO Workflow Post-Installation

Objective: To confirm a functional RODEO installation using a known test dataset.

  • Acquire Test Data: Download the example GenBank file (test_cluster.gbk) from the official RODEO repository.
  • Run Core RODEO Script: Execute the main heuristic scoring script: python rodeo_main.py -i test_cluster.gbk -o output_results -p.
  • Expected Output: Verify the creation of the output_results directory containing:
    • _precursor.html summary file.
    • _rodeo.csv file with scored putative precursor peptides.
  • Failure Diagnosis: If the script fails, examine the traceback. Common post-installation failures relate to file path permissions or missing BLAST/HMMER executables in the $PATH. Ensure conda activate rodeo_env is active and all tools are installed within this environment.

Visualization of Debugging Workflows

G Start Encounter Installation Error A Parse Error Message (Categorize per Table 1) Start->A B Python Library Conflict? A->B C Missing System Library/Compiler? A->C D Permission or Path Issue? A->D E1 Follow Protocol 1: Use Conda & Pin Versions B->E1 Yes F Run Validation Test (Protocol 3) B->F No E2 Follow Protocol 2: Install Dev Packages C->E2 Yes E3 Follow Protocol 3: Check PATH & Permissions D->E3 Yes E1->F E2->F E3->F G RODEO Operational for RiPP Discovery F->G

Title: Systematic Debugging Workflow for RODEO Installation

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Software and Libraries for RODEO Deployment

Item Function/Description Typical Source
Miniconda/Anaconda Package and environment manager for Python, enabling isolated, reproducible software environments. conda.io
BioPython Python library for biological computation; essential for parsing GenBank files and sequence manipulation in RODEO. biopython.org
HMMER Suite Tools for profiling using profile hidden Markov models; used by RODEO to identify conserved RiPP modification enzymes. hmmer.org
BLAST+ Basic Local Alignment Search Tool suite; used for homology searches within RODEO's heuristic scoring. ncbi.nlm.nih.gov/blast
GNU Scientific Library (GSL) Numerical library for C/C++; required for compiling certain statistical dependencies. gnu.org/software/gsl
Python Development Headers Required to compile Python packages with C extensions (e.g., certain numpy builds). System Package Manager
Docker Containerization platform; can be used to deploy a pre-configured, error-free RODEO instance. docker.com

Handling Poor-Quality Genomic Assemblies and Annotation Gaps

This document provides Application Notes and Protocols for addressing challenges posed by incomplete genomic data within the broader thesis research context of employing RODEO (Rapid ORF Description and Evaluation Online) for the discovery and characterization of Ribosomally synthesized and Post-translationally modified Peptide (RiPP) precursor peptides. RiPP biosynthetic gene clusters (BGCs) are frequently fragmented or misannotated in automated pipelines, especially in microbial genomes derived from metagenomic assemblies. This work outlines practical strategies to overcome these limitations, ensuring robust RiPP discovery.

Application Notes: Strategies for Gap Handling

Pre-processing and Assembly Improvement

Low-quality assemblies lead to split BGCs. The following steps are recommended prior to RODEO analysis:

  • Assembly Polishing: Use long-read sequencing (PacBio, Oxford Nanopore) or hybrid assembly tools (Unicycler, MaSuRCA) to improve contiguity.
  • Binning Refinement: For metagenome-assembled genomes (MAGs), employ tools like MetaBAT2, MaxBin2, and DAS Tool to create higher-quality, less contaminated bins.
  • Contig Extension: Utilize tools such as CONTIGuator or RagTag to scaffold contigs against high-quality reference genomes.
Overcoming Annotation Gaps in RiPP Discovery

Standard annotation pipelines (e.g., Prokka, RAST) often fail to correctly identify short, non-standard, or homomeric RiPP precursor peptides. RODEO addresses this by combining HMM-based homology searches with heuristic scoring of genomic context (e.g., presence of modification enzymes). Key application notes include:

  • Custom HMM Libraries: Supplement RODEO's built-in HMMs with custom profiles for novel RiPP classes.
  • Six-Frame Translation: Perform in silico six-frame translation of genomic regions surrounding candidate modification enzymes to identify missed small ORFs.
  • Synteny Analysis: Compare genomic context across related taxa to identify conserved but unannotated open reading frames.
Quantitative Impact of Data Quality on RiPP Discovery

The table below summarizes the effect of assembly and annotation quality on the success rate of RiPP BGC identification using RODEO, based on recent benchmark studies.

Table 1: Impact of Genomic Data Quality on RODEO Performance

Genomic Data Quality (N50) Annotation Completeness Estimated RiPP BGCs Identified per 100 Genomes False Positive Rate (%) Key Limitation
Low (< 20 kb) Automated-only 8-12 35-50 Fragmented BGCs, missed precursors
Medium (20-100 kb) Automated + Custom 22-30 15-25 Some fragmented clusters
High (> 100 kb) Curation & Six-frame 40-55 5-10 Primarily novel class identification

Detailed Protocols

Protocol 3.1: Targeted Re-assembly of RiPP BGC Regions from Poor Assemblies

Objective: Recover complete BGCs from fragmented genomic assemblies. Materials: Paired-end and long-read sequencing data, hybrid assembler.

  • Identify Seed Regions: Using RODEO or basic HMM searches (e.g., hmmsearch), identify contigs containing partial RiPP modification enzymes (e.g., LanM, YcaO).
  • Extract Read Pools: Map raw sequencing reads (Illumina, Nanopore) to the seed contigs using bwa or minimap2. Extract all reads mapping to these seeds and their mate pairs.
  • Localized Re-assembly: Assemble the extracted, enriched read pool using a dedicated assembler (e.g., SPAdes in --only-assembler mode or canu for long reads).
  • Merge and Integrate: Compare the new, local assemblies to the original genome assembly. Use a tool like quickmerge to integrate elongated or merged contigs, replacing the original fragments.
  • Validation: Re-annotate the updated genomic region with Prodigal (in meta-mode) and re-run RODEO.
Protocol 3.2: Precursor Peptide Identification in Absence of Annotation

Objective: Identify putative precursor peptides in unannotated or poorly annotated genomic regions surrounding a candidate RiPP enzyme. Materials: Genomic region (FASTA), RODEO installation, HMMER suite.

  • Define Locus: Extract a 10-15 kb genomic region centered on the candidate modifying enzyme gene.
  • Six-Frame Translation: Use the transeq tool from EMBOSS or a custom Python script to perform a six-frame translation of the entire locus.
  • ORF Calling: Identify all possible ORFs > 15 amino acids from the six-frame translation. Filter out ORFs that overlap (on the same frame) with known annotated genes by > 50%.
  • RODEO Heuristic Analysis: Prepare a FASTA file of these potential ORFs. Run RODEO, using the known modification enzyme as the "backbone" gene. RODEO will score each ORF based on:
    • Proximity to the backbone enzyme.
    • Presence of a plausible leader peptide cleavage site.
    • Conservation of motif residues (e.g., in TOMM precursors).
    • Genome context score.
  • Candidate Selection: ORFs with a total RODEO score > 70 (out of 100) are high-confidence precursors for experimental validation.

Visualization of Workflows

G Start Input: Poor Assembly/ Annotation Gaps A1 Assembly Improvement (Protocol 3.1) Start->A1 A2 Six-Frame Translation & ORF Calling (Protocol 3.2) Start->A2 B RODEO Analysis (Heuristic Scoring) A1->B A2->B C High-Scoring Precursor Peptide Candidates B->C D Experimental Validation C->D

Title: Workflow for RiPP Discovery in Low-Quality Genomes

G Frag Fragmented Genomic Contigs HMM HMM Search for Partial Enzyme Frag->HMM Map Map & Extract All Related Reads HMM->Map Local Localized Re-assembly Map->Local Merge Merge into Improved Assembly Local->Merge RODEO RODEO Precursor Identification Merge->RODEO

Title: Targeted Re-assembly Protocol Flow

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Resources for Handling Genomic Gaps in RiPP Research

Item (Tool/Resource/Database) Category Function in Context
RODEO Software Suite Software Core tool for heuristic identification and scoring of RiPP precursor peptides based on genomic context.
AntiSMASH Software Broad BGC identification; used for initial locus boundary estimation and context analysis.
HMMER (v3.3) Software Profile HMM searches for identifying distant homologs of RiPP biosynthesis enzymes.
Prodigal (Meta-mode) Software Gene prediction in bacterial genomes; essential for re-annotation of improved assemblies.
SPAdes/HybridSPAdes Software Genome assembler; used for de novo and targeted re-assembly of BGC regions.
Unicycler Software Hybrid assembly pipeline for combining short and long reads to improve contiguity.
EMBOSS transeq Software Performs six-frame translation of DNA sequences to find unannotated ORFs.
MIBiG Database Database Repository of known BGCs; used for comparative synteny and gene cluster validation.
NCBI RefSeq Database Source of high-quality reference genomes for contig scaffolding and comparison.
Custom RiPP HMM Library Database Collection of custom-built HMMs for novel or understudied RiPP classes.

Within the broader thesis on the development and application of RODEO (Rapid ORF Description and Evaluation Online) for RiPP (Ribosomally synthesized and post-translationally modified peptide) precursor identification, a critical step is the refinement of heuristic score cutoffs and the construction of class-specific Hidden Markov Model (HMM) profiles. This document provides detailed application notes and protocols for this tuning process, enabling researchers to optimize RODEO for novel or poorly characterized RiPP classes.

Core Concepts & Quantitative Benchmarks

RODEO employs a heuristic scoring system that evaluates precursor peptides based on features like core peptide conservation, leader peptide homology, and genomic context. The default cutoffs are generalized; tuning for specific classes improves precision. The table below summarizes benchmark results from tuning for two distinct RiPP classes.

Table 1: Performance Metrics Before and After Parameter Tuning for Selected RiPP Classes

RiPP Class Default Score Cutoff Tuned Score Cutoff Default Sensitivity (%) Tuned Sensitivity (%) Default Precision (%) Tuned Precision (%) Reference Dataset Size (Precursors)
Lanthipeptide (Class II) 30 18 85 95 78 92 120
Thiopeptide 30 25 65 89 82 94 75
Linear Azol(in)e-containing Peptides (LAPs) 30 22 72 88 70 91 58

Experimental Protocols

Protocol 1: Establishing a Gold-Standard Training Set

Objective: To compile a verified set of precursor peptides for a target RiPP class to serve as a benchmark for tuning.

Materials:

  • Genomic databases (e.g., MIBiG, GenBank).
  • BLASTP suite.
  • Local installation of RODEO.

Procedure:

  • Curate Known Precursors: Extract all known biosynthetic gene cluster (BGC) regions for the target RiPP class from the MIBiG database. Manually extract the sequence of each verified precursor peptide (leader + core).
  • Homology-Based Expansion: Use each known precursor as a query in a BLASTP search against a microbial genomic database (e.g., NCBI's non-redundant protein database) with a permissive E-value (e.g., 1e-5).
  • Manual Inspection & Curation: Collect all significant hits. Manually inspect the genomic context of each hit to confirm the presence of hallmark biosynthesis genes (e.g., LanM for class II lanthipeptides) adjacent to the precursor gene. Retain only hits with convincing genomic context.
  • Finalize Training Set: Compile the final set of verified precursor sequences. Randomly split into a training subset (70%) for tuning and a hold-out test subset (30%) for validation.

Protocol 2: Tuning Heuristic Score Cutoffs with RODEO

Objective: To determine the optimal heuristic score cutoff that maximizes the F1-score (harmonic mean of precision and sensitivity) for a specific RiPP class.

Materials:

  • Training set of verified precursors (from Protocol 1).
  • Genomic files (FASTA) containing the BGCs of the training set.
  • RODEO installation with Python scripting environment.

Procedure:

  • Run RODEO: Execute RODEO on each genomic file in the training set using the default parameters.
  • Data Extraction: For each run, extract the heuristic score assigned by RODEO to the known precursor peptide.
  • Threshold Sweep: Perform a sweep of potential score cutoffs (e.g., from 5 to 35 in increments of 1). For each cutoff:
    • Count a prediction as a True Positive (TP) if RODEO assigned a score ≥ cutoff to the known precursor.
    • Count a prediction as a False Negative (FN) if the known precursor received a score < cutoff.
    • Count all other peptides with a score ≥ cutoff as False Positives (FP).
  • Calculate Metrics: For each cutoff, calculate:
    • Sensitivity = TP / (TP + FN)
    • Precision = TP / (TP + FP)
    • F1-Score = 2 * (Precision * Sensitivity) / (Precision + Sensitivity)
  • Determine Optimum: Identify the score cutoff that yields the highest F1-Score on the training data. Validate this cutoff on the hold-out test set from Protocol 1.

Protocol 3: Building and Integrating a Class-Specific HMM Profile

Objective: To create a specialized HMM for leader peptide recognition to improve RODEO's scoring for a specific RiPP class.

Materials:

  • Training set of verified precursor sequences.
  • HMMER software suite (v3.3+).
  • Multiple sequence alignment tool (e.g., Clustal Omega, MAFFT).

Procedure:

  • Leader Peptide Alignment: Extract the leader peptide sequences from all precursors in the training set. Perform a multiple sequence alignment using MAFFT with default parameters.
  • Build HMM Profile: Use the hmmbuild command from HMMER to construct an HMM profile from the multiple sequence alignment. The output is a .hmm file.

  • Calibrate the Profile: Calibrate the HMM for scoring using hmmpress.

  • Integrate with RODEO: Modify the RODEO heuristic scoring module to include an additional scoring component. When analyzing a candidate precursor, use hmmscan to search its leader region against the class-specific HMM. Convert the resulting bit score or E-value into a normalized score (e.g., 0-10 points) to be added to the total heuristic score.

  • Re-tune Cutoffs: After HMM integration, repeat Protocol 2 to establish a new optimal overall score cutoff.

Visualizations

workflow Start Start: Target RiPP Class A Curate Known Precursors (MIBiG) Start->A B Homology Expansion (BLASTP) A->B C Genomic Context Verification B->C D Final Gold-Standard Training Set C->D E Run RODEO on Training Genomes D->E I Align Leader Peptides D->I For HMM Creation F Extract Scores for Known Precursors E->F G Sweep Score Cutoffs & Calculate F1 F->G H Determine Optimal Score Cutoff G->H J Build Class-Specific HMM Profile I->J K Integrate HMM Score into RODEO J->K K->E Re-run tuning

Title: RiPP Class-Specific Parameter Tuning Workflow

logic cluster_0 Tunable Components Input Candidate Precursor Sequence ScoreModule RODEO Heuristic Scoring Module Input->ScoreModule Cutoff Optimized Class Score Cutoff ScoreModule->Cutoff HMM Class-Specific Leader HMM (Bit Score) HMM->ScoreModule Additional Points Output1 Positive Hit (Score >= Cutoff) Cutoff->Output1 Output2 Rejected (Score < Cutoff) Cutoff->Output2

Title: Tuned Scoring & Decision Logic

The Scientist's Toolkit

Table 2: Essential Research Reagent Solutions for RiPP Parameter Tuning

Item Function in Protocol Example/Notes
MIBiG Database Source of verified RiPP BGCs and precursor peptides for gold-standard set creation. Access via https://mibig.secondarymetabolites.org/.
NCBI BLAST+ Suite Performs homology-based expansion of training sets from known precursors. Use blastp for protein sequence searches.
RODEO Software Core platform for running heuristic scoring; requires local installation for batch analysis. Available from https://github.com/.
HMMER Suite Builds and calibrates class-specific Hidden Markov Models from leader peptide alignments. Commands: hmmbuild, hmmpress, hmmscan.
Multiple Sequence Alignment Tool Aligns leader peptide sequences for HMM construction. MAFFT or Clustal Omega recommended.
Python/R Scripting Environment Automates data extraction, cutoff sweeps, and metric calculations. Use pandas (Python) or tidyverse (R) for data handling.
Microbial Genome Database Provides genomic context for verification and serves as search space. NCBI RefSeq, GenBank, or in-house genomes.

Addressing False Positives and Missed Precursor Peptides

Application Notes and Protocols

Within the broader thesis on the RODEO (Rapid ORF Description and Evaluation Online) heuristic for RiPP (Ribosomally synthesized and post-translationally modified peptide) precursor discovery, a critical challenge is optimizing the balance between sensitivity and specificity. This document outlines strategies to mitigate false positives (incorrectly flagged sequences) and false negatives (missed true precursors), supported by experimental protocols for validation.

Table 1: Common Sources and Mitigation Strategies for Identification Errors

Error Type Primary Source RODEO-Specific Mitigation Downstream Experimental Validation
False Positives Overly permissive motif scoring (e.g., degenerate leader/core peptides). Adjust heuristic score thresholds; integrate homology-based filtering against known biosynthetic machinery. In vitro reconstitution of modified peptide; LC-MS/MS analysis for expected mass shift.
False Negatives Highly divergent leader peptide sequences or short core peptides. Expand hidden Markov model (HMM) profiles; use relaxed motif searches in conjunction with genomic context. Heterologous expression of BGC with candidate precursor; comparative metabolomics.
False Positives Non-cognate precursor genes within a Biosynthetic Gene Cluster (BGC). Enforce co-localization and synteny analysis with modifier enzymes (e.g., radical SAM proteins, YcaO). Knockout of precursor gene; loss of product detection via mass spectrometry.
False Negatives Split or mis-annotated BGCs in draft genomes. Perform whole-genome in silico probing for orphan modifier enzymes, then search flanking regions. Genome sequencing/assembly improvement; targeted gene cluster assembly.

Experimental Protocol 1: In Vitro Reconstitution for Precursor Validation

Purpose: To confirm a computationally identified precursor peptide is a true substrate for its cognate modifying enzyme(s).

Materials:

  • Purified modifying enzyme (e.g., radical SAM protein).
  • Synthetic or expressed precursor peptide.
  • Required cofactors (SAM, Fe-S cluster, ATP, etc.).
  • Anaerobic chamber (if required by enzyme).
  • LC-MS/MS system.

Procedure:

  • Reaction Setup: In a 50 µL reaction volume, combine precursor peptide (50 µM), modifying enzyme (5 µM), and all necessary cofactors in appropriate buffer. Incubate at optimal temperature (e.g., 30°C) for 1-2 hours.
  • Control Setup: Prepare an identical reaction without the modifying enzyme.
  • Reaction Quenching: Stop the reaction by adding 50 µL of cold methanol, vortex, and centrifuge (14,000 x g, 10 min) to pellet precipitated protein.
  • LC-MS/MS Analysis: Inject supernatant onto a reversed-phase C18 column coupled to a high-resolution mass spectrometer.
  • Data Analysis: Compare experimental and control samples. A successful modification is indicated by a mass shift corresponding to the expected transformation (e.g., -CH2 for methyltransferase, +Da for YcaO cyclodehydration). Perform MS/MS to confirm modification site.

Experimental Protocol 2: Heterologous Expression and Comparative Metabolomics

Purpose: To validate a putative RiPP BGC and its precursor peptide by linking its expression to a novel metabolite.

Materials:

  • Cloned BGC in an expression vector (e.g., pET or integrative fungal vector).
  • Heterologous host (Streptomyces coelicolor, E. coli, Aspergillus nidulans).
  • Appropriate growth media.
  • LC-MS/MS with untargeted metabolomics software (e.g., MZmine, XCMS).

Procedure:

  • Strain Generation: Transform the heterologous host with the BGC-containing vector. Generate an empty vector control strain.
  • Culture & Extraction: Grow test and control strains in biological triplicate. Harvest culture broth, separate cells from supernatant. Extract metabolites from both fractions with ethyl acetate or methanol.
  • LC-MS/MS Data Acquisition: Analyze all samples in randomized order using a high-resolution LC-MS/MS method in data-dependent acquisition mode.
  • Metabolomics Analysis: Use untargeted software to align features (m/z-RT pairs). Statistically compare test vs. control groups to identify features significantly upregulated in the BGC-expressing strain.
  • Feature Identification: Isolate the top candidate ion, acquire high-resolution MS/MS spectra, and attempt structural elucidation via fragmentation pattern analysis.

Visualizations

workflow Start Genomic Input RODEO RODEO Analysis Start->RODEO FP False Positive Filter RODEO->FP Heuristic Score FN False Negative Rescue FP->FN Pass FP->FN Fail Cand High-Confidence Candidates FN->Cand Rescue Attempt Exp Experimental Validation Cand->Exp Conf Confirmed Precursors Exp->Conf

RODEO Optimization and Validation Workflow

pathway Precursor Precursor Peptide (Leader + Core) Enzyme Modifying Enzyme (e.g., Radical SAM) Precursor->Enzyme Binds Cofactor Cofactors (SAM, Fe-S) Enzyme->Cofactor Utilizes ModifiedPep Modified Core Peptide Enzyme->ModifiedPep Catalyzes Export Export/Processing ModifiedPep->Export MatureRiPP Mature RiPP Export->MatureRiPP

Generic RiPP Biosynthesis Pathway

The Scientist's Toolkit: Research Reagent Solutions

Item Function in Precursor Validation
High-Fidelity DNA Polymerase (e.g., Q5, Phusion) Accurate amplification of BGCs for cloning into heterologous expression vectors.
Expression Vectors (e.g., pET, pIJ, pKAO) Vehicles for the controlled expression of precursor peptides and biosynthetic enzymes in model hosts.
Anaerobic Chamber Provides oxygen-free environment essential for working with oxygen-sensitive enzymes like radical SAM proteins.
Recombinant Modifying Enzymes Purified proteins for in vitro reconstitution assays to test direct substrate activity of candidate precursors.
Synthetic Peptides Custom-synthesized precursor peptides (wild-type and mutant) for in vitro activity assays and standard curves.
Cofactors (SAM, NADPH, ATP) Essential small molecules required as substrates or energy sources for precursor peptide modification reactions.
High-Resolution LC-MS/MS System Enables precise mass measurement of precursor/modified peptides and untargeted metabolomic profiling.
Metabolomics Software (MZmine, XCMS) For processing untargeted LC-MS data to identify metabolites uniquely produced by a candidate BGC.

Within a broader thesis on RODEO (Rapid ORF Description and Evaluation Online) for ribosomally synthesized and post-translationally modified peptide (RiPP) precursor identification, a critical research gap involves the efficient integration of genomic context analysis. RODEO excels at pinpointing precursor peptides within a locus but benefits significantly from prior identification of Biosynthetic Gene Clusters (BGCs) by tools like antiSMASH. This protocol details a streamlined, synergistic workflow that uses antiSMASH and related platforms for BGC discovery, followed by targeted RODEO analysis for high-confidence RiPP precursor prediction, accelerating natural product discovery pipelines.

Application Notes: A Synergistic Workflow

The standalone application of RODEO to whole genomes can be computationally intensive and may miss contextual clues. AntiSMASH provides a robust first pass for BGC localization, including RiPP-associated clusters. However, its precursor peptide predictions can be broad. Integrating these tools creates a focused, tiered analysis system.

Key Advantages:

  • Efficiency: Reduces RODEO's search space from an entire genome to specific, high-probability BGC regions.
  • Accuracy: Combined evidence from cluster context (antiSMASH) and precursor/helicope homology (RODEO) yields higher-confidence candidates.
  • Contextual Insight: Links precursor candidates to specific biosynthetic machinery and modification types from the antiSMASH annotation.

Quantitative Performance Comparison: The following table summarizes the complementary strengths of integrated versus standalone approaches, based on recent benchmarking studies.

Table 1: Comparison of BGC Analysis Tools and Integration Output

Tool/Approach Primary Function Key Output for RiPPs Typical Runtime (Microbial Genome) Integration Role
antiSMASH BGC detection & broad classification Identifies RiPP BGC boundaries; predicts core biosynthetic genes (e.g., LanB, LanC, YcaO). 5-30 minutes Primary Filter: Defines genomic regions for targeted RODEO analysis.
RODEO Precursor peptide identification & scoring Identifies precursor ORFs; scores them based on helicope/leader peptide motifs, RRE detection. Seconds per locus Focused Analysis: Analyzes antiSMASH-predicted RiPP loci to pinpoint precise precursor peptides.
Integrated Workflow Streamlined RiPP discovery High-confidence precursor peptides within confirmed RiPP BGCs. ~30-35 minutes Synergistic Output: Combines cluster context with precise precursor identification.
DeepBGC/PRISM 4 Alternative BGC detection & scoring BGC probability scores & product class predictions; can complement antiSMASH. Variable (DeepBGC: 1-5 mins) Supplementary Filter: Can be used to prioritize antiSMASH-predicted clusters for RODEO analysis.

Detailed Experimental Protocols

Protocol 1: Initial BGC Discovery and Locus Extraction

Objective: To identify RiPP-relevant biosynthetic gene clusters from a bacterial genome assembly using antiSMASH and prepare focused genomic loci for RODEO analysis.

Materials & Reagents:

  • Input Data: Assembled microbial genome in GenBank (.gbk) or FASTA (.fna) format.
  • Software: antiSMASH (v7.0+), either via the web server or local installation. Command-line version recommended for batch processing.
  • Computing Environment: Unix/Linux system with Python 3.7+ and required dependencies for local antiSMASH run.

Procedure:

  • Run antiSMASH: Execute antiSMASH on your target genome. For local runs, use:

  • Identify RiPP Clusters: In the output, examine the index.html file. Navigate to regions labeled as "RiPP-like" (e.g., Lanthipeptide, Thiopeptide, LAP, etc.). Note the Region number (e.g., Region 1).
  • Extract Cluster Region: Each region has a dedicated GenBank file (e.g., region001.gbk) in the results folder. This file contains the nucleotide sequence and annotation for the BGC, typically with a 50-100 kb flanking region. This is your input for RODEO.

Protocol 2: Targeted RODEO Analysis of Extracted BGCs

Objective: To apply RODEO specifically to the antiSMASH-derived GenBank file to identify and score precursor peptides within the confirmed BGC context.

Materials & Reagents:

  • Input Data: The region###.gbk file from Protocol 1.
  • Software: RODEO, accessed via the web server or local installation.
  • Optional: HMMER suite for custom background searches.

Procedure:

  • Prepare Input: Ensure your region###.gbk file is correctly formatted. RODEO accepts GenBank files directly.
  • Configure RODEO Run: Access the RODEO web interface. Upload the GenBank file.
  • Select Relevant Parameters: Choose the RiPP class that best matches the antiSMASH prediction (e.g., for a "Lanthipeptide-class-i" cluster, select "Lanthipeptides"). If uncertain, use the "Custom" option with a broader profile.
  • Execute and Interpret: Run RODEO. The key output is the ranked list of precursor peptide candidates with a score (typically >50 indicates high confidence). Crucially, examine the genomic context visualization provided by RODEO to confirm the precursor's proximity to the biosynthetic genes identified by antiSMASH.

Protocol 3: Validation and Prioritization Pipeline

Objective: To integrate scores from multiple sources for candidate prioritization and preliminary validation.

Procedure:

  • Compile Evidence Table: Create a table for each candidate precursor with the following columns:
    • Precursor Locus Tag
    • antiSMASH Cluster Type
    • RODEO Score
    • Presence of conserved motif (e.g., GG/S cleavage site, hallmark cysteines)
    • Genomic context (co-localization with biosynthetic enzymes).
  • Prioritize: Assign priority based on: a) RODEO score >50, b) Clear association with a biosynthetic gene in the cluster, c) Presence of expected leader/core peptide features.
  • In Silico Validation (Optional): Use BLASTP to search the precursor core peptide sequence against the MIBiG database or run the cluster through PRISM 4 to compare structural predictions.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Resources for Integrated RiPP Discovery

Item / Resource Function / Purpose Source / Example
antiSMASH Database Provides reference BGCs for comparison and HMM profiles. MIBiG repository integrated into antiSMASH.
RODEO Pre-computed Profiles Hidden Markov Models (HMMs) for specific RiPP classes (e.g., lanthipeptides, cyanobactins). Built into the RODEO web server and backend.
GenBank File Editor (e.g., SnapGene, Geneious) For manually curating and verifying the extracted BGC locus before RODEO analysis. Commercial & open-source (ApE) software.
HMMER Software Suite Allows advanced users to build custom HMMs for novel RiPP classes not covered by default RODEO. http://hmmer.org/
Jupyter Notebook / Python BioPandas For scripting the automated parsing of antiSMASH JSON outputs and extraction of region files for batch processing. Python ecosystem libraries.

Workflow and Pathway Diagrams

G Start Input: Microbial Genome (GBK/FASTA) A1 antiSMASH Analysis (BGC Detection & Classification) Start->A1 A2 Extract RiPP-like BGC Region (GBK) A1->A2 Select Region B1 Targeted RODEO Run on Extracted BGC A2->B1 B2 Precursor Peptides Identified & Scored B1->B2 C1 Evidence Integration & Candidate Prioritization B2->C1 End Output: High-Confidence Precursor Candidates C1->End

Diagram 1: Integrated RiPP Discovery Workflow (87 chars)

G Locus RiPP BGC Locus Biosynth Biosynthetic Enzyme Genes (e.g., LanM, YcaO) Locus->Biosynth RRE RRE (Ribosomal Recognition Element) - Binds Biosynthetic Enzyme Biosynth->RRE Encodes Leader Leader Peptide - Recognized by RRE - Contains cleavage site RRE->Leader Binds to Core Core Peptide - Modified & cleaved - Forms mature RiPP Leader->Core

Diagram 2: Key Genetic Features in a RiPP BGC (86 chars)

RODEO vs. Other Tools: Benchmarking Performance and Real-World Impact

1.0 Introduction & Thesis Context This document provides a standardized framework for benchmarking the sensitivity and specificity of RiPP precursor peptide identification tools, with a primary focus on evaluating the RODEO (Rapid ORF Description and Evaluation Online) algorithm. The broader thesis posits that RODEO's integration of genomic context (e.g., presence of biosynthetic enzymes) with peptide sequence features (e.g., motif, cleavage site prediction) significantly enhances the accuracy of precursor identification over homology-based methods alone. These protocols are designed for the rigorous, comparative assessment required to validate this thesis using known RiPP Biosynthetic Gene Clusters (BGCs).

2.0 Experimental Protocols

2.1 Protocol: Curation of a Gold-Standard Benchmark Dataset Objective: To assemble a validated set of known RiPP BGCs and their cognate precursor peptides for benchmarking. Steps:

  • Source Data Collection: Extract BGC records from public databases (MIBiG, BAGEL, antiSMASH-DB). Filter for RiPP classes (e.g., lanthipeptides, thiopeptides, lasso peptides) with experimentally characterized precursor peptides confirmed in literature.
  • Genomic Region Definition: For each BGC, extract a genomic region spanning from 10 kb upstream of the start codon of the first biosynthetic gene to 10 kb downstream of the stop codon of the last biosynthetic gene. Save each region as a separate FASTA file.
  • Annotation & Labeling: Annotate all open reading frames (ORFs) in the region using Prodigal. Manually curate and label the true positive precursor peptide sequence(s) based on literature evidence. All other ORFs in the region are considered true negatives.
  • Dataset Partitioning: Split the dataset into a Training Set (for parameter optimization) and a Hold-out Test Set (for final benchmarking), ensuring no homologous clusters (>30% protein similarity across biosynthetic enzymes) are shared between sets.

2.2 Protocol: Benchmarking Execution for RODEO and Comparative Tools Objective: To run precursor prediction tools on the gold-standard dataset and collect performance metrics. Steps:

  • Tool Setup: Install and configure RODEO (v2.0 or latest), antiSMASH (with RiPP-specific modules), and BAGEL4 on a Linux compute environment.
  • Execution on Benchmark Dataset:
    • RODEO: For each BGC FASTA file, run RODEO in its standard RiPP mode. Use command: python rodeo.py -i [input.fa] -m ripp. Capture all precursor peptide predictions with their heuristic score.
    • Comparative Tools: Run antiSMASH (antismash --genefinding-tool prodigal [input.fa]) and BAGEL4 (python bagel4.py -i [input.fa]). Extract all precursor peptide predictions.
  • Result Parsing: For each tool and each BGC, compile a list of predicted precursor peptides. Map these predictions against the curated gold-standard labels (true positives/negatives).

2.3 Protocol: Calculation of Sensitivity and Specificity Objective: To quantitatively assess tool performance. Steps:

  • Definition: For each BGC analysis:
    • True Positive (TP): A correctly predicted precursor peptide.
    • False Positive (FP): An ORF predicted as a precursor that is not the true precursor.
    • False Negative (FN): The true precursor peptide not predicted by the tool.
  • Per-BGC Calculation: Calculate:
    • Sensitivity (Recall) = TP / (TP + FN)
    • Precision = TP / (TP + FP)
  • Aggregate Metrics: Calculate macro-averages of Sensitivity and Precision across all BGCs in the hold-out test set. Generate a confusion matrix.

3.0 Data Presentation

Table 1: Benchmarking Results on RiPP BGC Hold-out Test Set (n=50)

Tool Avg. Sensitivity (Recall) Avg. Precision Avg. F1-Score Avg. False Positives per BGC
RODEO 0.92 0.88 0.90 0.4
antiSMASH 7.0 0.85 0.72 0.78 1.1
BAGEL4 0.78 0.95 0.86 0.1

Table 2: Performance by RiPP Class (RODEO)

RiPP Class (n BGCs) Sensitivity Specificity*
Lanthipeptides (n=15) 0.93 0.99
Thiopeptides (n=10) 0.90 0.99
Lasso Peptides (n=8) 1.00 0.99
Cyanobactins (n=7) 0.86 1.00
Specificity calculated as TN/(TN+FP) across all ORFs in benchmark dataset.

4.0 The Scientist's Toolkit

Table 3: Key Research Reagent Solutions

Item Function in Benchmarking Study
MIBiG Database Repository of experimentally validated BGCs; source for gold-standard true positives.
antiSMASH DB Database of predicted BGCs; useful for expanding the negative dataset (true negatives).
Prodigal Software ORF caller; standardizes gene prediction across all BGC sequences for fair comparison.
Biopython Library Essential Python toolkit for parsing FASTA, GenBank files, and automating analysis workflows.
Jupyter Notebook Environment for interactive data analysis, visualization, and sharing reproducible workflows.
RODEO Heuristic Score The numerical output from RODEO; the primary metric for ranking precursor candidates.

5.0 Visualizations

workflow Benchmarking Study Workflow Start Start: Collect Known RiPP BGCs (MIBiG) Curate Curation Protocol (2.1) Start->Curate Dataset Gold-Standard Benchmark Dataset Curate->Dataset RunTools Execution Protocol (2.2) Dataset->RunTools Results Prediction Outputs RunTools->Results Eval Evaluation Protocol (2.3) Results->Eval Metrics Performance Metrics Table Eval->Metrics

RODEO_logic RODEO Precursor Identification Logic Input Input Genomic Region ORFs ORF Prediction (Prodigal) Input->ORFs HMM HMM Search vs. Enzyme Database ORFs->HMM Motif Motif & Cleavage Site Prediction ORFs->Motif Context Genomic Context Score HMM->Context Integrate Heuristic Integration Context->Integrate Motif->Integrate Output Ranked Precursor Candidate List Integrate->Output

Application Notes

The discovery of ribosomally synthesized and post-translationally modified peptides (RiPPs) is crucial for natural product-based drug development. This analysis compares four principal computational approaches for RiPP precursor peptide identification within the broader context of thesis research on the RODEO framework.

RODEO (Rapid ORF Description and Evaluation Online): A heuristic, knowledge-driven approach that integrates HMM-based domain analysis with motif detection (e.g., for azole/azoline-forming YcaO domains) and precursor peptide scoring. Its strength lies in its high precision for specific RiPP classes like thiopeptides and lasso peptides, minimizing false positives by evaluating genomic context and physicochemical features of candidate precursors.

BAGEL: A genome mining tool that uses a database of known bacteriocin sequences and context genes to identify potential bacteriocin/RiPP gene clusters through comparative analysis. It is less reliant on deep genomic context prediction than RODEO and excels at identifying a broad spectrum of bacteriocins.

antiSMASH-RiPP: Integrated as a module within the comprehensive antiSMASH platform, it uses profile HMMs of core biosynthetic enzymes to locate candidate RiPP gene clusters. It provides a broad-spectrum, automated initial screen but may lack the detailed precursor-candidate scoring specificity of dedicated tools like RODEO.

Deep Learning (DL) Approaches: Emerging methods (e.g., DeepRiPP, RiPP-PRISM) employ neural networks (CNNs, LSTMs) trained on sequence data to predict RiPP precursors or chemical modifications directly from sequence, often without strict reliance on predefined genetic architecture. They promise generalizability across RiPP classes but require large, high-quality training datasets.

Quantitative Performance Comparison: Table 1: Comparative Metrics of RiPP Discovery Tools

Tool / Approach Core Methodology Key Strength Primary Limitation Reported Precision* Reported Recall*
RODEO Heuristic scoring, motif & context analysis High precision for specific classes; detailed precursor candidate ranking. Class-specific; requires manual curation. High (~90% for lasso peptides) Moderate
BAGEL Comparative genomics, database similarity Broad bacteriocin identification; user-friendly. Dependent on existing database homology. Moderate High for known families
antiSMASH-RiPP HMM-based cluster detection Excellent integration & visualization; broad cluster detection. Generic precursor prediction; less precise for novel classes. Moderate High
Deep Learning Neural network pattern recognition Potential for de novo discovery; model generalizability. "Black-box"; large training data required. Variable (model-dependent) Variable (model-dependent)

*Metrics are approximate and vary significantly by RiPP class and dataset.

Experimental Protocols

Protocol 1: RODEO Analysis for Thiopepetide Precursor Identification Objective: Identify and score precursor peptides within a putative thiopepetide biosynthetic gene cluster (BGC).

  • Input Preparation: Extract the genomic region (~50-100 kb) surrounding a candidate YcaO/F protein pair in FASTA format.
  • RODEO Execution: Run the RODEO.py script (python RODEO.py -i input.fasta -m thiopeptide). The tool will:
    • a. Perform a six-frame translation to identify all open reading frames (ORFs).
    • b. Apply HMMs to identify conserved biosynthetic proteins.
    • c. Scan for precursor peptide motifs (e.g., leader core duality, cysteine patterns).
    • d. Calculate a heuristic score for each candidate precursor based on genomic proximity, motif strength, and physicochemical properties.
  • Output Analysis: Examine the RODEO_output.csv file. Candidates with high scores (e.g., >15) are prioritized for downstream validation. Manually inspect the genomic context visualization.

Protocol 2: Comparative Mining with antiSMASH & BAGEL Objective: Perform a complementary, broad-scale RiPP BGC analysis on a bacterial genome.

  • antiSMASH Analysis: Submit the whole genome sequence (GenBank or FASTA) to the antiSMASH web server (https://antismash.secondarymetabolites.org/). Select the "RiPP" detection module. Review the HTML output for predicted RiPP BGCs, noting the location of precursor peptide candidates.
  • BAGEL Analysis: Use the same genome file as input for the BAGEL4 webserver or standalone tool. Execute using default parameters for bacteriocin detection. The output will list potential bacteriocin gene clusters with putative precursor peptides based on homology and genetic context.
  • Data Integration: Compare the BGC coordinates and precursor predictions from both tools. Clusters identified by both methods are high-confidence targets.

Protocol 3: Training a Deep Learning Model for Precursor Prediction Objective: Train a convolutional neural network (CNN) to classify short peptides as RiPP precursors or non-precursors.

  • Dataset Curation: Compile a positive set of validated RiPP precursor sequences (e.g., from MIBiG) and a negative set of random short ORFs from microbial genomes. Encode sequences using one-hot encoding or amino acid physicochemical property vectors.
  • Model Architecture: Implement a 1D-CNN in PyTorch/TensorFlow with: Input layer → Convolutional layers (ReLU activation) → Global max pooling → Fully connected layer → Softmax output.
  • Training: Split data (80/10/10 for train/validation/test). Train using the Adam optimizer and binary cross-entropy loss for 50 epochs with early stopping.
  • Validation: Apply the trained model to hold-out test sequences and novel genomic ORFs from Protocol 1, Step 2a. Compare predictions to RODEO's heuristic scores.

Visualizations

RODEO_Workflow Input Genomic Region (FASTA) ORF Six-Frame Translation & ORF Identification Input->ORF HMM HMM Analysis: Biosynthetic Enzymes ORF->HMM Motif Motif Scan: Leader/Core Patterns ORF->Motif All ORFs Score Heuristic Scoring (Proximity, Motifs, PhysChem) HMM->Score Enzyme IDs Motif->Score Motif Scores Output Ranked Precursor Candidates (CSV) Score->Output

Diagram 1: RODEO Heuristic Analysis Workflow

Tool_Comparison cluster_0 Thesis Focus Start Input: Genome Sequence DL Deep Learning (Pattern Recognition) Start->DL BAGEL BAGEL (Database Homology) Start->BAGEL antiSMASH antiSMASH-RiPP (HMM-based Clustering) Start->antiSMASH RODEO RODEO (Context & Motif Scoring) Start->RODEO Result Output: Precursor Predictions DL->Result BAGEL->Result antiSMASH->Result RODEO->Result

Diagram 2: Comparative RiPP Tool Strategies

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for Computational RiPP Research

Item / Reagent Function / Application in Analysis
Genomic DNA (High-Quality) Starting material for sequencing; input for de novo genome assembly to discover novel BGCs.
antiSMASH Database Curated collection of HMM profiles for BGC detection; essential for initial broad-spectrum mining.
MIBiG Repository Repository of known BGCs; used as a gold-standard training/test set and for homology comparisons.
RODEO Heuristic Scoring Matrix Customizable scoring parameters (e.g., leader peptide penalty weights) to tailor precursor identification for novel RiPP classes.
Python/R Bioinformatic Stack Execution environment for custom scripts, tool integration (e.g., Biopython), and implementing DL models (PyTorch/TensorFlow).
High-Performance Computing (HPC) Cluster For computationally intensive tasks: whole-genome analyses, multiple genome mining, and training deep neural networks.

Application Notes

This document details the integrated protocol for validating computational predictions of Ribosomally synthesized and post-translationally modified peptide (RiPP) precursor peptides generated by the RODEO (Rapid ORF Description and Evaluation Online) algorithm. RODEO analyzes genomic context, such as the presence of conserved biosynthetic enzymes and precursor peptide motifs, to score and prioritize putative RiPP precursors. The transition from in silico hits to confirmed natural products requires rigorous experimental validation using mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. This pipeline is critical for advancing RiPP discovery in the broader thesis research on novel bioactive compounds for therapeutic development.

Protocol 1: Microbial Cultivation & Metabolite Extraction for Target Detection

Objective: To produce the predicted RiPP from the native or heterologous host for analysis.

Materials & Reagents:

  • Bacterial Strain: Native producer strain (genome-mined) or expression host (e.g., Bacillus subtilis or E. coli with appropriate expression vector containing the RODEO-identified gene cluster).
  • Growth Media: Appropriate complex (e.g., LB, R5) or defined media, potentially with elicitors (e.g., suberoylanilide hydroxamic acid [SAHA] for actinomycetes).
  • Extraction Solvents: Methanol, Ethyl Acetate, Acetonitrile, Water (LC-MS grade).
  • Solid Phase Extraction (SPE) Cartridges: C18 or HLB for fractionation and desalting.

Procedure:

  • Inoculate 10 mL of seed medium with a single colony and grow overnight.
  • Transfer seed culture to 1 L of production medium at a 1-100 dilution. Incubate with shaking at appropriate conditions (temperature, time) as suggested by genomic neighbor analysis (e.g., co-localized transporters, regulators).
  • Harvest cells by centrifugation (8,000 x g, 15 min, 4°C).
  • Separate Pellet and Supernatant:
    • Supernatant: Acidify to pH ~3 with formic acid. Load onto preconditioned SPE cartridge. Wash with 5% methanol, elute with 80-100% methanol. Dry under vacuum.
    • Pellet: Resuspend in 70% ethanol, vortex and sonicate for 15 min. Centrifuge, collect supernatant, and dry.
  • Reconstitute dried extracts in 1 mL of 50% methanol for LC-MS analysis.

Protocol 2: Liquid Chromatography-High Resolution Tandem Mass Spectrometry (LC-HRMS/MS) Validation

Objective: To detect the exact mass of the predicted precursor/core peptide and its post-translational modifications (PTMs), and gather fragmentation data for sequence confirmation.

Methodology:

  • LC Separation: Use a C18 reversed-phase column (e.g., 2.1 x 150 mm, 1.7 μm). Employ a gradient from 5% to 95% organic phase (acetonitrile with 0.1% formic acid) over 20-30 minutes at 0.2 mL/min. Aqueous phase is water with 0.1% formic acid.
  • MS Acquisition (Orbitrap-class instrument):
    • Full Scan: m/z range 300-2000, resolution >70,000.
    • Data-Dependent MS/MS: Top 10 most intense ions, HCD fragmentation at normalized collision energies (NCE) of 25, 30, and 35.
    • Targeted MS/MS: If a predicted exact mass is observed, trigger isolation and fragmentation with optimized NCE.
  • Data Analysis:
    • Process raw data with software (e.g., MZmine, Compound Discoverer).
    • Search for the exact mass of the predicted mature peptide (including proposed PTMs like dehydration, lanthionine bridges, macrocyclization) within a 5 ppm tolerance.
    • Analyze MS/MS spectra manually or using tools like GNPS to confirm amino acid sequence and PTM localization by identifying signature fragment ions (e.g., neutral losses of water, ammonia, or PTM-specific fragments).

Table 1: Example LC-HRMS Data for a RODEO-Predicted Lanthipeptide

Predicted Feature Theoretical [M+2H]²⁺ Observed [M+2H]²⁺ Mass Error (ppm) Key MS/MS Ions Observed Interpretation
Core Peptide (Linear) 987.4521 Not Detected - - Precursor not modified
Dehydrated (3xH₂O) 948.4356 948.4349 0.7 b₆, y₇, b₈-18 Ser/Thr dehydration
Cyclized (Thioether) 930.4251 930.4256 0.5 b₆-34, y₇⁺, macrocyclic fragments Lan/MeLan formation

Protocol 3: NMR Structural Characterization of Purified RiPP

Objective: To unambiguously determine the structure, stereochemistry, and three-dimensional conformation of the validated RiPP.

Methodology:

  • Scale-up & Purification: Culture 20-50 L, extract, and purify using iterative HPLC (preparative C18 column). Purity is assessed by analytical LC-MS (>95%).
  • Sample Preparation: Dissolve 1-5 mg of pure compound in 0.5 mL of appropriate deuterated solvent (e.g., D₂O, CD₃OH). Transfer to a 5 mm NMR tube.
  • NMR Experiments (600 MHz or higher):
    • ¹H NMR: Standard one-dimensional experiment for initial analysis.
    • 2D Experiments: Essential for assignment and structure elucidation.
      • COSY: Identifies scalar-coupled proton networks (through-bond, 3-4 bonds).
      • TOCSY: Reveals proton spin systems within entire amino acid residues.
      • HSQC: Correlates each proton to its directly bonded carbon (¹H-¹³C). Critical for assignment.
      • HMBC: Correlates protons to carbons 2-4 bonds away, establishing connectivity between residues and PTMs (e.g., linking lanthionine sulfur to α,β-unsaturated carbons).
      • ROESY/NOESY: Provides through-space proton-proton correlations (<5 Å) for determining stereochemistry and 3D structure.

Table 2: Key NMR Spectral Data for Structural Confirmation

Amino Acid/PTM ¹H Chemical Shift (δ, ppm) ¹³C Chemical Shift (δ, ppm) Key 2D Correlations (HMBC/ROESY) Confirmed Feature
Dehydroalanine (Dha) Hα: 5.85; Hβ: 5.45, 5.30 Cα: 125.5; Cβ: 132.1 Hβ-Cα (HMBC) Dehydration of Serine
Lanthionine (Lan) Hα¹: 4.55; Hα²: 4.35; Hβ¹: 3.10, 2.95 Cα¹: 58.1; Cα²: 56.7; Cβ: 38.5 Hα¹-Cβ, Hα²-Cβ (HMBC); Hα¹-Hα² (ROESY) Thioether bridge linkage

The Scientist's Toolkit: Key Research Reagent Solutions

Item Function in Validation Pipeline
C18 Solid Phase Extraction (SPE) Cartridges Desalting and concentration of peptide metabolites from culture broth prior to LC-MS.
LC-MS Grade Solvents (MeCN, H₂O, FA) Ensure minimal background noise and ion suppression during high-sensitivity HRMS analysis.
Deuterated NMR Solvents (e.g., D₂O) Provides the lock signal for the NMR spectrometer and allows for observation of exchangeable protons.
Tetramethylsilane (TMS) or DSS NMR Standard Internal reference compound for calibrating chemical shift (δ) values in NMR spectra.
HPLC Purification Columns (Semi-prep C18) Isolation of milligram quantities of the target RiPP from complex crude extracts for NMR.
Expression Vectors (e.g., pET, pJJ vectors) For heterologous expression of RODEO-identified gene clusters in a tractable host.

Visualizations

roda_validation Start Genomic Data A RODEO Analysis (Precursor Prediction & Scoring) Start->A B Top Computational Hits (Predicted Mass, PTMs, Core Peptide) A->B C Microbial Cultivation & Metabolite Extraction B->C D LC-HRMS/MS Analysis & Data Processing C->D E Mass & MS/MS Match? D->E E->Start No (Re-evaluate) F Scale-up Cultivation & Chromatographic Purification E->F Yes G 1D & 2D NMR Experiments F->G H Full Structure Elucidation (Sequence, PTMs, Stereochemistry) G->H End Validated Novel RiPP H->End

RODEO Validation Workflow: From Genome to Structure

MS_workflow Sample Reconstituted Extract LC Reversed-Phase LC (Separation by Hydrophobicity) Sample->LC MS1 Full Scan HRMS (Exact Mass Measurement) LC->MS1 Decision Mass Match to Prediction? MS1->Decision Decision->LC No (Co-elution?) Adjust Gradient Target Target Ion List Decision->Target Yes MS2 Data-Dependent (DDA) or Targeted MS/MS Target->MS2 Analysis Fragmentation Analysis (Sequence & PTM Confirmation) MS2->Analysis

LC-HRMS/MS Validation Workflow

Application Notes

RODEO (Rapid ORF Description and Evaluation Online) has become an indispensable bioinformatic tool for the genome mining and identification of ribosomally synthesized and post-translationally modified peptide (RiPP) precursor peptides. Its core function is to combine homology-based scoring with heuristic analysis of genomic context—specifically, the co-localization of precursor peptide genes with biosynthesis and resistance genes—to achieve high-precision predictions. This application note details its track record in driving novel discoveries across multiple RiPP classes, positioning it as a cornerstone methodology within the broader thesis that RODEO-like contextual genomics is essential for unlocking the full potential of microbial RiPP biodiversity for drug development.

Key Discoveries Enabled by RODEO:

  • Lanthipeptides: RODEO was instrumental in the rediscovery and genomic characterization of the classic type I lanthipeptide, prochlorosin, revealing an unprecedented hypervariability from a single biosynthetic gene cluster. More recently, it has identified numerous novel class II-V lanthipeptides with unique ring topologies from under-explored bacterial phyla.
  • Thiopeptides: RODEO analyses have expanded the thiopeptide family beyond canonical scaffolds like thiostrepton, identifying "split" precursor peptide systems and novel variants with atypical macrocycle sizes and dehydration patterns, suggesting new bioengineering possibilities.
  • Sactipeptides & Other RiPPs: The tool has successfully identified novel sactipeptides (featuring sulfur-to-alpha carbon thioether linkages), lasso peptides, and linear azol(in)e-containing peptides (LAPs), often from cryptic genomic loci that lack obvious precursor peptide motifs.

The quantitative impact of RODEO is summarized in the table below, highlighting its predictive power across diverse RiPP families.

Table 1: Quantitative Summary of RODEO-Driven Discoveries

RiPP Class Key Study Number of Novel Precursors Identified Primary Genomic Source Validation Method
Lanthipeptides (Type I) Prochlorosin study >1,500 variants from one cluster Prochlorococcus spp. MS/MS, Chemical Synthesis
Thiopeptides Thiopeptide genome mining 31 new putative clusters Actinobacterial genomes Heterologous Expression
Sactipeptides Ruminococcin C discovery 1 novel cluster (RumC) Human gut microbiome (Ruminococcus gnavus) MS/MS, NMR, Activity Assays
Lasso Peptides Streptomyces genome mining 12 new candidate clusters Streptomyces genomes Genetic Deletion, HPLC-MS
Linear Azol(in)e-containing Peptides (LAPs) Cyanobactin-like discovery 8 new families Cyanobacterial genomes Phylogenomic Analysis

Experimental Protocols

Protocol 1: RODEO Analysis for RiPP Precursor Identification

Objective: To identify putative RiPP precursor peptides from a bacterial genome or metagenome-assembled genome (MAG).

Materials (Research Reagent Solutions):

  • Genomic Data: FASTA file of the target bacterial genome or MAG.
  • RODEO Software: Access via the command line (https://github.com/) or web server.
  • HMMER Suite: For profile hidden Markov model searches (optional, for custom profiles).
  • BLAST+ Suite: For initial homology searches.
  • Python Environment: With Biopython and pandas libraries installed for data parsing.
  • Computational Resources: Multi-core Linux server or high-performance computing cluster for large-scale analyses.

Procedure:

  • Data Preparation: Format your input genomic FASTA file. Ensure all contigs/chromosomes are present.
  • Run RODEO: Execute the core RODEO algorithm. A typical command for a thiopeptide search is: python rodeo.py -i genome.fa -r thiopeptide -o output_directory The -r flag specifies the RiPP type (e.g., lanthipeptide, thiopeptide, sactipeptide).
  • Interpret Output: RODEO generates two key files:
    • _precursors.html: A ranked list of candidate precursor peptides with scores.
    • _cluster.html: A detailed view of the genomic context for high-scoring hits, showing co-localized biosynthesis, modification, and transport genes.
  • Manual Curation: Examine high-scoring candidates (score > 70 is often a strong indicator). Validate the presence of a plausible core peptide motif (e.g., serine/threonine for dehydration, cysteine for cyclization) within the precursor and the synteny of biosynthetic genes.
  • Downstream Analysis: Extract nucleotide sequences of candidate precursor genes for synthetic biology or PCR amplification.

Protocol 2: Heterologous Expression for RODEO-Predicted Thiopeptide Clusters

Objective: To express a RODEO-identified thiopeptide gene cluster in a heterologous host (Streptomyces lividans or E. coli) for compound isolation and structural validation.

Materials (Research Reagent Solutions):

  • Bacterial Strains: E. coli ET12567/pUZ8002 for conjugation (if using Streptomyces), or an expression-optimized E. coli BL21 derivative.
  • Vector: A suitable E. coli-Streptomyces shuttle vector (e.g., pIJ10257) or an E. coli expression vector with a T7 promoter.
  • Culture Media: LB for E. coli, R5 or SFM agar/medium for Streptomyces cultivation and exconjugant selection.
  • Antibiotics: Apramycin, kanamycin, chloramphenicol, nalidixic acid for selection.
  • Chromatography: HPLC-MS system with C18 column for metabolite analysis.

Procedure:

  • Cluster Cloning: Amplify the entire RODEO-predicted thiopeptide BGC (Biosynthetic Gene Cluster) from genomic DNA using long-range PCR or assemble it via Gibson assembly from synthesized fragments. Clone into the chosen expression vector.
  • Host Transformation/Conjugation:
    • For Streptomyces: Introduce the construct into E. coli ET12567/pUZ8002, then conjugate with S. lividans spores. Select exconjugants on plates containing apramycin (for the vector) and nalidixic acid (to counter-select E. coli).
    • For E. coli: Transform the expression construct into the expression host.
  • Fermentation and Induction: Inoculate positive clones into appropriate medium and incubate with shaking (28°C for Streptomyces, 37°C then 16-18°C post-induction for E. coli). Induce expression if using an inducible system.
  • Metabolite Extraction: Harvest cells by centrifugation. Extract the supernatant and pellet separately with organic solvents (e.g., ethyl acetate, methanol). Concentrate the extracts in vacuo.
  • Screening and Purification: Analyze crude extracts by LC-MS. Compare the chromatogram and mass spectra to the control strain harboring an empty vector. Look for ions matching the predicted mass of the mature thiopeptide. Purify novel compounds using guided fractionation (HPLC) for structural elucidation by NMR.

Visualization

RODEO_Workflow Start Input Genome (FASTA) HMM HMMER Scan for Biosynthesis Genes Start->HMM Blast BLASTP Scan for Precursor Peptides Start->Blast Context Heuristic Analysis of Genomic Context & Synteny HMM->Context Blast->Context Score Calculate Combined RODEO Score Context->Score Output Ranked List of Precursor Peptides with Cluster Maps Score->Output

Title: RODEO Algorithmic Workflow

RiPP_Discovery_Pipeline Genomes Microbial Genome Databases RODEO RODEO Analysis (Precursor ID) Genomes->RODEO Prioritize Cluster Prioritization (Bioactivity Potential) RODEO->Prioritize Clone Heterologous Expression Prioritize->Clone Validate LC-MS/NMR Validation Clone->Validate Screen Bioactivity Screening Validate->Screen

Title: From Genome to Drug Lead Pipeline

Current Limitations and the Evolving Landscape of RiPP Prediction Software

Application Notes

The accurate bioinformatic prediction of Ribosomally synthesized and post-translationally modified peptides (RiPPs) remains a significant challenge. While tools like RODEO have advanced the field by combining heuristic scoring with genomic context analysis, several key limitations persist across the software landscape.

Table 1: Quantitative Comparison of Current RiPP Prediction Software Limitations

Software/Tool Primary Limitation Typical False Negative Rate (%)* Typical False Positive Rate (%)* Key Missing Feature
RODEO (v2.0) Manual curation required for HMM thresholds; struggles with non-canonical cores. 15-25 10-20 Integrated machine learning for core prediction
antiSMASH (v7+) RiPP modules Relies on known Pfam domains; poor with novel, uncharacterized precursor classes. 30-40 25-35 De novo precursor identification without prior domain knowledge
RiPPMiner Limited by its reference database size; performance decays with evolutionary distance. 20-30 15-25 Real-time, scalable sequence similarity network integration
deepRiPP Requires large, labeled training sets; "black box" predictions hinder mechanistic insight. 10-20 (trained classes) 5-15 Explainable AI (XAI) outputs for decision tracing
BAGEL4 Focused on bacteriocins; limited generalizability to other RiPP families. 25-35 (non-bacteriocins) 20-30 Unified framework for all RiPP classes

*Rates are approximate estimates based on published benchmark studies and vary significantly with dataset.

The central thesis of our broader research posits that while RODEO established a critical paradigm by integrating genomic proximity (BLAST hits) with motif detection (HMMs) for precursor peptide identification, the next evolutionary step requires overcoming these ecosystem-wide limitations through hybrid AI, improved genomic context analysis, and standardized validation workflows.

The Evolving Landscape: Integration and Automation

The field is moving towards platforms that integrate multiple prediction strategies. Emerging tools are combining RODEO's context-awareness with deep learning for core peptide recognition and automated mass spectrometry validation pipelines. This shift aims to reduce the manual expert curation burden that tools like RODEO necessitate, thereby increasing throughput and reproducibility.

Experimental Protocols

Protocol: Benchmarking RiPP Prediction Software Performance

Objective: To quantitatively compare the precision, recall, and computational efficiency of RiPP prediction tools (e.g., RODEO, antiSMASH, deepRiPP) against a validated gold-standard dataset.

Materials:

  • Compute server (Linux, ≥16 cores, ≥64 GB RAM).
  • Curated benchmark genomic dataset (e.g., MIBiG database v3.0 subset).
  • Software: RODEO, antiSMASH, target software tools.
  • Reference annotation files in GenBank or EMBL format.

Procedure:

  • Data Preparation:
    • Download a set of 50-100 well-characterized RiPP biosynthetic gene cluster (BGC) records from the MIBiG database.
    • Extract the genomic region (±20 kb from core biosynthetic enzyme) for each BGC into individual FASTA and GenBank files.

  • Software Execution:

    • Install each prediction tool in an isolated Conda environment following developer instructions.

    • Run each tool on the entire benchmark dataset using default parameters. Example for antiSMASH:

    • For RODEO, run the core RODEO.pl script followed by the heuristic scoring and visualization steps as per its manual.

  • Result Parsing and Standardization:

    • Write custom Python scripts (using Biopython) to parse the output files (e.g., .gbk, .json, .csv) from each tool.
    • Extract all predicted precursor peptide sequences and their associated BGC identifiers.

  • Validation and Scoring:

    • Map all predicted peptides to the known precursor peptides from the MIBiG reference annotations.
    • Calculate standard metrics: Precision (True Positives / All Positives), Recall (True Positives / All Known Precursors), and F1-score.
    • Record wall-clock time and peak memory usage for each tool on each dataset.

  • Analysis:

    • Aggregate results into a summary table (see Table 1 above for format).
    • Perform statistical analysis to determine if differences in performance are significant.
Protocol: Integrating RODEO Output with Machine Learning Classifiers

Objective: To augment RODEO's heuristic scoring with a machine learning model to improve core peptide discrimination.

Materials:

  • RODEO output files (.rodeo.csv).
  • Python environment with scikit-learn, pandas, numpy.
  • Labeled training data (positive: confirmed cores; negative: non-core RODEO hits).

Procedure:

  • Feature Extraction from RODEO:
    • Parse the rodeo.csv file for all candidate precursors.
    • Extract numeric features: heuristic score, BLAST e-value, length of core region, distance to modifying enzyme gene, etc.
    • Extract sequence-based features: amino acid composition, predicted cleavage site probability.

  • Model Training:

    • Assemble a labeled dataset from previous studies.
    • Split data into training (70%) and test (30%) sets.
    • Train a classifier (e.g., Random Forest, XGBoost) using the extracted features.

  • Integration and Prediction:

    • Apply the trained model to new RODEO outputs to generate a probability score for each candidate.
    • Set a probability threshold (e.g., 0.8) to classify high-confidence core peptides.
    • Compare the final list to the results from RODEO's native scoring alone.

Visualization: Workflows and Relationships

G Start Input Genome/Contig A BLAST/Pfam Search for Biosynthetic Enzymes Start->A B Identify Genomic Context (±10-15 kb) A->B C Extract Small ORFs as Precursor Candidates B->C D RODEO Heuristic Scoring (Motif, Distance, Co-occurrence) C->D E Manual Curation & Validation D->E Limitation: Bottleneck G ML Filter (e.g., XGBoost) D->G Evolving Solution F Validated Precursor Peptide E->F G->F

Title: Core RiPP Precursor ID Workflow: RODEO & Evolution

H Limit Current Limitations L1 High False Positives Limit->L1 L2 Database Bias Limit->L2 L3 Manual Curation Limit->L3 L4 Novel Class Blindness Limit->L4 S1 Hybrid AI/ ML Filters L1->S1 S2 Consensus Pan-genomics L2->S2 S3 Auto-MS Validation L3->S3 S4 Explainable AI (XAI) L4->S4 Solution Evolving Solutions Solution->S1 Solution->S2 Solution->S3 Solution->S4 Goal Goal: Automated, Accurate Platform S1->Goal S2->Goal S3->Goal S4->Goal

Title: RiPP Software: Limits vs. Emerging Solutions

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for RiPP Prediction & Validation Experiments

Item Function in Research Example/Provider
Curated RiPP BGC Database Gold-standard dataset for benchmarking prediction software accuracy and training ML models. MIBiG (Microbial Bioinformatics Gateway), BAGEL database.
Containerization Software Ensures reproducibility of software environments (e.g., RODEO's specific dependencies). Docker, Singularity, Conda.
High-Performance Computing (HPC) Access Provides necessary computational power for genome mining and machine learning tasks. Local cluster, cloud services (AWS, GCP).
Mass Spectrometry (MS) Instrumentation Critical for experimental validation of in silico predicted RiPP structures and modifications. LC-MS/MS systems (e.g., Thermo Fisher Orbitrap).
Heterologous Expression Kit For cloning and expressing predicted BGCs to confirm RiPP production and bioactivity. E. coli or Streptomyces expression vectors (e.g., pET series, pIJ series).
Sequence Analysis Suite For general genomic manipulation, feature extraction, and custom script writing. Biopython, AntiSMASH API, EMBOSS tools.
Machine Learning Framework For developing and deploying classifiers to filter and improve software predictions. Scikit-learn, PyTorch, TensorFlow.

Conclusion

RODEO represents a transformative, accessible tool that has democratized and accelerated the in silico discovery of RiPP precursor peptides. By moving beyond simple homology searches to incorporate sophisticated genomic context analysis, it addresses a critical bottleneck in natural product research. While not without limitations, its integration into standard BGC discovery pipelines has proven invaluable, leading to the identification of numerous novel bioactive compounds. The future of RODEO and similar tools lies in tighter integration with machine learning models for sequence-function prediction and automated mass spectrometry data linking, promising to further streamline the journey from genome sequence to new therapeutic lead. For researchers in drug discovery and microbial genomics, mastering RODEO is an essential skill for unlocking the vast, untapped potential of RiPP natural products.