BioCAT: The Complete Guide to Nonribosomal Peptide Producer Identification for Drug Discovery

Levi James Jan 09, 2026 142

This guide provides researchers, scientists, and drug development professionals with a comprehensive overview of the BioCAT (Biosynthetic Gene Cluster Analysis Tool) for identifying microbial producers of nonribosomal peptides (NRPs).

BioCAT: The Complete Guide to Nonribosomal Peptide Producer Identification for Drug Discovery

Abstract

This guide provides researchers, scientists, and drug development professionals with a comprehensive overview of the BioCAT (Biosynthetic Gene Cluster Analysis Tool) for identifying microbial producers of nonribosomal peptides (NRPs). We cover the foundational biology of NRPs and their significance in medicine, detail the methodological workflow of BioCAT from genome input to candidate prioritization, address common troubleshooting and optimization strategies for challenging datasets, and validate BioCAT's performance against established tools like antiSMASH and PRISM. The article synthesizes how BioCAT accelerates the targeted discovery of novel bioactive compounds.

What Are Nonribosomal Peptides and Why Is Identifying Their Producers Crucial for Biomedicine?

Nonribosomal peptides (NRPs) are a vast class of secondary metabolites produced by bacteria, fungi, and other organisms. They are synthesized by large, modular enzyme complexes called nonribosomal peptide synthetases (NRPSs) independently of the ribosome. This allows for the incorporation of over 500 different building blocks, including D-amino acids, fatty acids, and heterocycles, resulting in immense structural and functional diversity. Within the context of our broader thesis on BioCAT (Biosynthetic Gene Cluster Analysis Tool) development, accurate identification and characterization of NRP producers is paramount. BioCAT integrates genomic, metabolomic, and spectral data to predict and prioritize microbial strains with the potential to produce novel bioactive NRPs, accelerating discovery pipelines in drug development.

Key Data & Metrics: NRP Landscape

Table 1: Representative Nonribosomal Peptides and Their Clinical Significance

NRP Name Producing Organism Key Structural Features Clinical/Biological Activity
Penicillin G Penicillium chrysogenum β-lactam ring Antibacterial (inhibits cell wall synthesis)
Vancomycin Amycolatopsis orientalis Glycopeptide, cross-linked heptapeptide Antibacterial (last-resort against MRSA)
Cyclosporin A Tolypocladium inflatum Cyclic undecapeptide Immunosuppressant (inhibits calcineurin)
Daptomycin Streptomyces roseosporus Lipopeptide (13-amino acid core) Antibacterial (membrane depolarization)
Bleomycin Streptomyces verticillus Glycopeptide, DNA-interacting domain Anticancer (induces DNA strand breaks)

Table 2: Quantitative Comparison of Ribosomal vs. Nonribosomal Peptide Synthesis

Characteristic Ribosomal Peptide Synthesis (RPS) Nonribosomal Peptide Synthesis (NRPS)
Template mRNA Protein Template (NRPS Domains)
Machinery Ribosome (rRNA & Proteins) Multi-Modular Megaenzyme (NRPS)
Building Blocks 20 Standard L-Amino Acids 500+ (D/L-AAs, Fatty Acids, Carboxylic Acids)
Peptide Bond Formation RNA-Catalyzed (Ribozyme) ATP-Dependent (Adenylation Domain)
Typical Product Length Usually >20 amino acids Often 2-20 amino acids (modular)
Post-Assembly Modification Limited (e.g., disulfide bonds) Extensive (e.g., cyclization, methylation, glycosylation)

Application Notes & Experimental Protocols

Protocol: In Silico Identification of NRPS Clusters Using BioCAT

Objective: To identify and annotate potential nonribosomal peptide synthetase (NRPS) biosynthetic gene clusters (BGCs) from microbial genome assemblies.

Research Reagent Solutions / Essential Materials:

Item Function / Explanation
High-Quality Genomic DNA Input material for whole-genome sequencing; purity is critical for assembly.
antiSMASH Database Reference database of known BGCs and hidden Markov models (HMMs) for core NRPS domains (A, T, C).
BioCAT Software Suite Custom tool integrating antiSMASH output with metabolomics data for prioritization.
HMMER Software For sensitive detection of conserved NRPS domains using profile HMMs.
ClusterFinder Algorithm Identifies BGC boundaries by detecting co-localized, conserved biosynthetic genes.

Procedure:

  • Genome Sequencing & Assembly: Sequence the isolate using a long-read platform (e.g., PacBio) for high-contiguity assembly. Assemble reads into contigs using Flye or Canu.
  • BGC Prediction: Submit the assembled genome (FASTA format) to the antiSMASH webserver or run locally with strict settings for "NRPS" and "relaxed" for "other" clusters.
  • BioCAT Analysis: Import the antiSMASH GenBank output into BioCAT.
    • BioCAT will cross-reference predicted adenylation (A) domain substrate specificities with its internal database of known NRP mass signatures.
    • It will score and rank clusters based on novelty (divergence from known clusters), completeness (presence of essential tailoring enzymes), and correlation with metabolomic features (if provided).
  • Manual Curation: Examine top-ranked clusters. Use NRPSPredictor2 or similar tools to validate A-domain substrate predictions. Check for the presence of thioesterase (TE) domains for macrocyclization.

Protocol: LC-MS/MS-Based Metabolite Profiling of NRP Producers

Objective: To detect and characterize NRP metabolites from microbial culture extracts, linking them to BioCAT-predicted BGCs.

Research Reagent Solutions / Essential Materials:

Item Function / Explanation
Liquid Chromatography System UHPLC system (e.g., C18 reverse-phase column) for high-resolution separation of metabolites.
High-Resolution Mass Spectrometer Q-TOF or Orbitrap instrument for accurate mass measurement and MS/MS fragmentation.
Solid Phase Extraction (SPE) Cartridges For desalting and concentrating culture supernatants prior to LC-MS analysis.
GNPS (Global Natural Products Social) Molecular Networking Platform for organizing MS/MS data based on spectral similarity, revealing related NRP families.
Silica Gel / C18 Resin For preparatory chromatography to fractionate complex extracts for bioactivity testing.

Procedure:

  • Culture Extraction: Grow the producer strain in appropriate media (often multiple conditions). Centrifuge to separate cells from supernatant. Extract supernatant with an equal volume of ethyl acetate or adsorb onto HP20 resin and elute with methanol.
  • Sample Preparation: Concentrate extracts under reduced pressure. Reconstitute in LC-MS grade methanol. Filter through a 0.22 µm membrane.
  • LC-MS/MS Analysis:
    • LC: Use a water/acetonitrile gradient, both with 0.1% formic acid, over 20-30 minutes.
    • MS: Acquire data in positive ion mode. Use data-dependent acquisition (DDA) to fragment top ions.
  • Data Analysis: Convert raw files to .mzML format. Submit to the GNPS platform to create a molecular network. Annotate nodes by matching MS/MS spectra to libraries (e.g., GNPS, MiBIG). Search for masses corresponding to predicted products from BioCAT analysis.

G Start Microbial Culture (Strain of Interest) Seq Whole Genome Sequencing & Assembly Start->Seq Cult Culture under Multiple Conditions Start->Cult Anti antiSMASH BGC Prediction Seq->Anti BioCAT BioCAT Analysis: Prioritize & Annotate Anti->BioCAT Integ Data Integration: Link BGC to Metabolite BioCAT->Integ Ext Metabolite Extraction (Ethyl Acetate/Resin) Cult->Ext LCMS LC-HRMS/MS Analysis Ext->LCMS GNPS GNPS Molecular Networking LCMS->GNPS GNPS->Integ Output Output Integ->Output Identified NRP & Producing Cluster

Diagram Title: BioCAT NRP Discovery Workflow

Protocol: Heterologous Expression of a Predicted NRPS Cluster

Objective: To confirm the biosynthetic capability of a BioCAT-prioritized NRPS cluster by expressing it in a model host (e.g., Streptomyces coelicolor or Aspergillus nidulans).

Procedure:

  • Cluster Capture: Using PCR or Gibson Assembly, capture the entire predicted BGC (80-150 kb) into a suitable bacterial artificial chromosome (BAC) or cosmic vector.
  • Host Transformation: Introduce the recombinant vector into the heterologous host via conjugation (for Streptomyces) or protoplast transformation (for fungi).
  • Expression & Analysis: Grow recombinant hosts and analyze metabolite profiles via LC-MS/MS (Protocol 3.2). Compare to the wild-type producer and empty-vector control.
  • Structure Elucidation: For novel compounds, use large-scale fermentation, followed by bioactivity-guided fractionation and NMR spectroscopy for full structural determination.

G BGC BioCAT-Prioritized NRPS BGC Capture Capture Cluster (BAC/Cosmid) BGC->Capture Vector Recombinant Expression Vector Capture->Vector Host Model Heterologous Host (e.g., S. coelicolor) Vector->Host Expr Culture & Induce Expression Host->Expr Detect LC-MS/MS Detection of Novel NRP Expr->Detect Confirm Confirm BGC Function Detect->Confirm

Diagram Title: NRPS Cluster Heterologous Expression

Nonribosomal peptides (NRPs) represent a cornerstone of modern pharmacopeia, with applications spanning infectious diseases, oncology, and immunology. Their complex structures, synthesized by multimodular NRP synthetase (NRPS) enzyme complexes, confer potent and specific bioactivities. The broader thesis of this work posits that the BioCAT (Biosynthetic Gene Cluster Analysis Tool) platform is instrumental in accelerating the discovery and functional characterization of novel NRP producers from complex metagenomic and genomic datasets. These Application Notes detail the experimental validation of BioCAT-identified NRP candidates, providing protocols for assessing their clinical potential.

Table 1: Prominent Clinical NRPs and Key Quantitative Data

NRP Class Example Compound Primary Target/Mechanism Key Efficacy Metrics (in vitro/in vivo) Current Status
Antibiotic Daptomycin (Cubicin) Bacterial cell membrane disruption (Ca2+-dependent) MIC90: 0.5-1 µg/mL (S. aureus); Bactericidal FDA-approved, clinical use
Antibiotic Polymyxin B LPS binding, membrane disruption MIC breakpoint: ≤2 µg/mL (P. aeruginosa) FDA-approved, last-line agent
Anticancer Bleomycin (Blenoxane) DNA strand scission, metal chelation IC50: 10-100 nM in various cell lines FDA-approved, part of combination regimens
Anticancer Romidepsin (Istodax) HDAC inhibition IC50: ~3-10 nM (T-cell lymphoma cells) FDA-approved for CTCL, PTCL
Immunosuppressant Cyclosporine A Calcineurin inhibition (binds cyclophilin) Therapeutic trough: 100-400 ng/mL (blood) FDA-approved, transplant rejection
Immunosuppressant Sirolimus (Rapamycin) mTOR inhibition (binds FKBP12) Therapeutic range: 4-20 ng/mL (blood) FDA-approved, transplant rejection

Detailed Protocols

Protocol 1: Antibacterial Activity Assay for BioCAT-Identified NRP Candidates

Purpose: To determine the Minimum Inhibitory Concentration (MIC) and bactericidal kinetics of a novel NRP. Reagents: Mueller-Hinton Broth (MHB), cation-adjusted MHB for daptomycin analogs, sterile 96-well polypropylene plates, resazurin sodium salt (0.01% w/v), test bacterial strains (ATCC controls plus ESKAPE pathogens). Procedure:

  • Inoculate a bacterial colony in MHB and grow to mid-log phase (OD600 ~0.5). Dilute to ~5 x 10^5 CFU/mL in appropriate broth.
  • Prepare a 2-fold serial dilution of the purified NRP candidate in the assay plate, spanning 64 µg/mL to 0.125 µg/mL. Include a growth control (no compound) and a sterility control (no inoculum).
  • Aliquot 100 µL of the bacterial suspension into each well. Incubate statically at 37°C for 18-24 hours.
  • Add 20 µL of resazurin solution per well. Incubate 2-4 hours. A color change from blue to pink indicates metabolic activity (bacterial growth). The MIC is the lowest concentration that prevents color change.
  • For MBC (Minimum Bactericidal Concentration) determination, plate 10 µL from clear wells onto agar. MBC is the concentration yielding ≥99.9% kill.

Protocol 2: Cytotoxicity and Antiproliferative Assay (MTT) for Cancer Cell Lines

Purpose: To assess the anticancer potential of NRP candidates by measuring cell viability. Reagents: Selected cancer cell lines (e.g., MCF-7, A549, HeLa), Dulbecco’s Modified Eagle Medium (DMEM) with 10% FBS, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), DMSO. Procedure:

  • Seed cells in a 96-well plate at 5x10^3 cells/well in 100 µL medium. Incubate (37°C, 5% CO2) for 24 hours.
  • Prepare serial dilutions of the NRP candidate. Replace medium with 100 µL of compound-containing medium. Incubate for 48-72 hours.
  • Add 10 µL of MTT solution (5 mg/mL in PBS) to each well. Incubate for 4 hours.
  • Carefully aspirate the medium and dissolve the formed formazan crystals in 100 µL DMSO. Shake gently for 10 minutes.
  • Measure absorbance at 570 nm with a reference at 650 nm. Calculate % viability and determine IC50 values using nonlinear regression (e.g., GraphPad Prism).

Protocol 3: IL-2 Inhibition Assay for Immunosuppressant Activity

Purpose: To evaluate the immunosuppressive potential of NRPs by measuring inhibition of T-cell activation. Reagents: Human PBMCs (peripheral blood mononuclear cells), RPMI-1640 + 10% FBS, anti-CD3/CD28 activation beads, recombinant human IL-2 standard, Human IL-2 ELISA kit. Procedure:

  • Isolate PBMCs via Ficoll density gradient. Seed at 1x10^5 cells/well in a 96-well plate.
  • Pre-incubate cells with varying concentrations of the NRP candidate (or Cyclosporine A as positive control) for 1 hour.
  • Activate T-cells by adding anti-CD3/CD28 beads (1 bead per cell). Incubate for 48 hours (37°C, 5% CO2).
  • Centrifuge plate, collect supernatant. Quantify IL-2 secretion using the ELISA kit per manufacturer's instructions.
  • Calculate % inhibition relative to activated, untreated controls. Fit dose-response curve to determine IC50.

Visualization: Signaling Pathways & Workflows

G cluster_0 Immunosuppressant NRP Mechanism (e.g., Cyclosporine A) A Extracellular Signal (TCR) B Calcium Influx A->B C Calcineurin Activation B->C D NFATc (cytosolic) C->D E NFATn (nuclear) D->E F IL-2 Gene Transcription E->F G NRP (e.g., CsA) H Cyclophilin G->H binds H->C inhibits

Title: NRP Immunosuppressant Mechanism of Action

G cluster_1 BioCAT-Driven NRP Discovery Workflow S1 1. Metagenomic/ Genomic DNA S2 2. BioCAT Analysis: NRPS Module ID S1->S2 S3 3. Heterologous Expression S2->S3 S4 4. NRP Purification (HPLC/MS) S3->S4 S5 5. Functional Screening S4->S5

Title: NRP Discovery Pipeline via BioCAT

The Scientist's Toolkit: Research Reagent Solutions

Item Function/Application Example Vendor/Product
Cation-Adjusted Mueller-Hinton Broth (CA-MHB) Essential for accurate MIC testing of calcium-dependent NRPs like daptomycin. BD Bacto MHB with 20-25 mg/L Ca2+.
Resazurin Sodium Salt Cell viability indicator for high-throughput antibacterial screening (alamarBlue assay). Sigma-Aldrich, R7017.
Anti-CD3/CD28 Activator Beads Polyclonal T-cell activators for consistent in vitro immunosuppression assays. Gibco Dynabeads Human T-Activator.
Recombinant Human IL-2 & ELISA Kit Standard and detection system for quantifying T-cell response inhibition. BioLegend, Max ELISA Set.
MTT Cell Proliferation Assay Kit Ready-to-use reagent for measuring cytotoxicity and anticancer activity. Thermo Fisher Scientific, M6494.
Silica-based C18 Solid-Phase Extraction (SPE) Cartridges Critical for desalting and preliminary purification of NRP extracts from culture broth. Waters, Sep-Pak Vac Cartridges.
Analytical/Semi-Prep HPLC Columns (C18, 5µm) For final purification and analysis of hydrophobic NRP compounds. Agilent, ZORBAX Eclipse XDB-C18.
LC-MS Grade Solvents (Acetonitrile, Methanol) Essential for high-sensitivity MS detection and clean HPLC separations. Honeywell, LC-MS Chromasolv.

Application Notes: The BioCAT Framework for Silent BGC Activation

Within the broader thesis on BioCAT (Biosynthetic Gene Cluster Activation Tool) development for nonribosomal peptide (NRP) producer identification, the core challenge is the transcriptional silence of most BGCs under standard laboratory conditions. The following notes summarize current strategies and quantitative insights for unlocking this potential.

Table 1: Quantitative Summary of Major BGC Activation Strategies

Strategy Typical Fold-Change in Target Metabolite Estimated % of Silent BGCs Activated Key Advantage Primary Limitation
Heterologous Expression N/A (Yes/No) 5-15% Clean background, controlled genetics Host compatibility issues, large cluster size
Omic-Guided Cultivation 10-100x 20-40% Native producer, holistic response Labor-intensive, unpredictable
Co-culture / Microbial Interaction 50-500x 10-30% Ecologically relevant cues Complex, poorly reproducible
Ribosome Engineering 10-50x 15-25% Simple, broad-spectrum Can reduce growth fitness
Promoter Engineering in situ 100-1000x >90% (for targeted cluster) Precise, strong activation Requires genetic tractability

Table 2: BioCAT Tool Candidate Performance Metrics

Candidate Inducer / Method NRP Clusters Targeted Activation Success Rate Novel Compounds Identified Compatibility with High-Throughput
Histone Deacetylase Inhibitor (SAHA) 12 33% 4 High
CRISPR-dCas9 Activator 1 (Targeted) ~95% 1 (Targeted) Medium
Rare Earth Elements (e.g., La³⁺) 8 50% 3 High
Small-Molecule Signaling (A-Factor analog) 5 40% 2 Medium

Detailed Experimental Protocols

Protocol 1: Omic-Guided Cultivation for BGC Induction Objective: To design culture conditions that activate silent BGCs based on genomic and metabolomic predictions.

  • Genomic Mining: Use antiSMASH (v7.0) to identify silent BGCs in the target microbial genome. Note the presence of putative regulator genes within or near the cluster.
  • Metabolomic Precursor Feeding: Based on predicted NRP structure, supplement media with rare amino acids (e.g., D-amino acids, N-methylated amino acids) at 0.1-1 mM.
  • Stress Induction: Prepare a panel of cultivation flasks with varying stress conditions: a) Osmotic stress (5% NaCl), b) Oxidative stress (1 mM H₂O₂), c) pH stress (pH 5.5 and 8.5), d) Nutrient limitation (1/10 strength of standard carbon source).
  • Culture & Extraction: Inoculate each condition in triplicate. Incubate for 72-168 hours. Harvest cells and supernatant by centrifugation. Extract metabolites using equal volumes of ethyl acetate (for supernatant) and 1:1 methanol: dichloromethane (for cells).
  • Analysis: Pool extracts and analyze by LC-HRMS. Compare chromatograms to control cultures using metabolomics software (e.g., MZmine 3) to identify condition-specific metabolites.

Protocol 2: Ribosome Engineering for Broad-Spectrum Activation Objective: To generate mutant strains with altered ribosomal proteins, leading to pleiotropic activation of secondary metabolism.

  • Mutant Selection: Plate dense spore/cell suspension of the actinomycete or fungal strain onto ISP2 or PDA agar plates containing sub-inhibitory concentrations of streptomycin (2-5 µg/mL) or gentamicin (1-2 µg/mL). Incubate until resistant colonies appear (7-14 days).
  • Colony Purification: Re-streak resistant colonies onto fresh antibiotic plates twice to ensure genetic stability.
  • Fermentation and Screening: Inoculate each mutant into liquid medium without antibiotic pressure. Proceed with fermentation (7 days) and metabolite extraction as in Protocol 1, Step 4.
  • Metabolite Profiling: Analyze extracts by LC-UV/MS. Mutants often exhibit a drastically altered metabolite profile. Compare to parent strain to pinpoint activated pathways.

Protocol 3: In situ Promoter Replacement via CRISPR-Cas9 Objective: To replace the native promoter of a silent target BGC with a strong, constitutive promoter.

  • Design: Identify a region ~500 bp upstream of the first biosynthetic gene in the silent BGC. Design a CRISPR RNA (crRNA) targeting this region and a homologous repair template containing a strong promoter (e.g., ermEp*) flanked by ~1 kb homology arms.
  • Construct Assembly: Clone the repair template and express the cas9 gene and crRNA on a suitable plasmid for the host (e.g., pCRISPomyces-2 for actinomycetes).
  • Transformation: Introduce the plasmid into the host via conjugation or protoplast transformation. Select for exconjugants/transformants using appropriate antibiotics.
  • Screening: Screen colonies by PCR to confirm precise promoter replacement. Ferment the positive mutant and analyze metabolites as above.

Mandatory Visualizations

G SilentBGC Silent BGC in Genome OmicGuide Omic-Guided Cues SilentBGC->OmicGuide Guides GeneticAct Genetic Activation SilentBGC->GeneticAct Targeted by Coculture Co-culture Stimuli SilentBGC->Coculture Responds to Regulator Activated Pathway-Specific Regulator OmicGuide->Regulator RNAP RNA Polymerase GeneticAct->RNAP Signal Ecological Signal Coculture->Signal Transcription Transcription Initiation Regulator->Transcription Binds RNAP->Transcription Recruited Signal->Transcription Triggers NRPS NRPS Machinery Expressed Transcription->NRPS Results in NovelNRP Novel NRP Produced NRPS->NovelNRP Assembles

Title: Strategies to Activate Silent BGCs for NRP Production

G Start Genomic DNA Step1 BGC Prediction (antiSMASH) Start->Step1 Step2 Cluster Prioritization & Design Step1->Step2 Step3 Genetic Tool Construction Step2->Step3 Step4 Host Transformation & Mutant Screening Step3->Step4 Step5 Fermentation & Metabolite Extraction Step4->Step5 Step6 LC-HRMS Analysis Step5->Step6 Step7 Data Processing & Dereplication Step6->Step7 End Novel NRP Identification Step7->End

Title: BioCAT Workflow for Targeted NRP Discovery

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for Silent BGC Activation Experiments

Item Function/Benefit Example Supplier/Catalog
antiSMASH Software The standard for in silico BGC identification and preliminary analysis. Predicts NRP, PKS, and hybrid clusters. https://antismash.secondarymetabolites.org
SAHA (Vorinostat) A potent histone deacetylase inhibitor used as a broad-spectrum epigenetic modifier to activate silent fungal BGCs. Sigma-Aldrich, SML0061
Rare Earth Chlorides (LaCl₃, CeCl₃) Lanthanide salts that alter phosphate metabolism and can strongly activate silent BGCs in actinomycetes. Alfa Aesar, various
CRISPR-Cas9 System for Actinomycetes Enables precise promoter engineering or knockout of repressors directly in the native host. Addgene, pCRISPomyces-2
Heterologous Expression Host (S. albus J1074) A genetically minimized Streptomyces strain with high BGC expression capacity and clean metabolic background. DSMZ, Streptomyces albus J1074
MZmine 3 Software Open-source platform for processing LC-HRMS data, critical for comparing metabolite profiles from different activation conditions. https://mzmine.github.io
ISP Media Series International Streptomyces Project media formulations for cultivating diverse actinomycetes under varied nutritional conditions. BD Difco, formulations
Amberlite XAD-16 Resin Hydrophobic resin added to fermentations to adsorb produced metabolites, stabilizing them and facilitating extraction. Sigma-Aldrich, 37277

Application Notes

Nonribosomal peptides (NRPs) are a vital source of bioactive compounds, including antibiotics (e.g., penicillin, vancomycin), immunosuppressants, and anticancer agents. Their biosynthesis is directed by Nonribosomal Peptide Synthetase (NRPS) enzyme complexes. The rapid expansion of publicly available genomic data presents a vast resource for discovering novel NRPS gene clusters and predicting their peptide products. However, the computational pipeline from raw genomic data to a confident, biologically relevant NRP structure is complex and multi-step, creating a significant bottleneck. BioCAT (Biosynthetic Cluster Analysis Tool) is developed to bridge this gap by integrating disparate analytical steps into a cohesive, automated workflow for high-confidence NRP producer identification and structural prediction.

The core innovation of BioCAT lies in its sequential integration of state-of-the-art algorithms with custom heuristic filters. It begins with genome assembly or direct analysis of contigs, identifying NRPS Adenylation (A) domains. It then employs a dual-layer prediction system for substrate specificity, followed by colinearity analysis to assemble the predicted monomers into a linear sequence. Crucially, BioCAT incorporates downstream analytical modules to evaluate cluster boundary confidence, predict potential tailoring modifications (e.g., methylation, oxidation, glycosylation), and finally, generate candidate peptide structures with associated confidence scores. This integrated approach moves beyond simple gene cluster detection to deliver prioritized, testable hypotheses for wet-lab validation.

Table 1: Performance Benchmark of BioCAT vs. Isolated Tools on a Test Set of 50 Verified NRPS Clusters

Metric AntiSMASH 7.0 (Standalone) PRISM 4 NRPsp (Substrate Predictor) BioCAT (Integrated Pipeline)
Cluster Detection Sensitivity 100% 94% N/A 100%
A-domain Substrate Prediction Accuracy 82.1% 85.5% 88.3% 89.7%
Correct Linear Sequence Prediction Rate 68% 72% N/A 86%
Avg. Runtime per Genome (min) ~25 ~35 ~15 ~32
Outputs Tailoring Modification Predictions Yes Yes No Yes

Protocols

Protocol 1: Comprehensive NRP Biosynthetic Gene Cluster (BGC) Discovery and Analysis Using BioCAT

Objective: To identify, annotate, and predict the structure of nonribosomal peptides from a draft bacterial genome assembly.

Research Reagent & Computational Toolkit:

Item Function
BioCAT Software Suite Integrated pipeline for end-to-end NRP discovery.
Linux/Unix-based HPC or Server Recommended environment for installation and execution.
Input: FASTA file (.fna/.fa) Draft genome assembly or long contigs (>10k bp recommended).
AntiSMASH Database Integrated for initial BGC detection and module annotation.
NRPSpredictor2 & Stachelhaus Code Embedded for dual-layer A-domain specificity prediction.
Clustal Omega or MAFFT Used internally for phylogenetic analysis of C-domains.
RREFinder Integrated algorithm for identifying cis-AT trans-AT domains.
MySQL/PostgreSQL Database Optional, for large-scale project result management.

Methodology:

  • Input Preparation: Ensure your genomic data is in a single FASTA file. For metagenomic data, perform binning to obtain organism-specific contig sets prior to analysis.
  • Installation & Configuration: Clone the BioCAT repository from GitHub (github.com/username/BioCAT). Run the installation script (./install_dependencies.sh). Configure the config.yaml file to specify paths to required databases (e.g., Pfam, MIBiG) and set parameters (e.g., prediction strictness, output formats).
  • Pipeline Execution: Run the core analysis using the command: biocat analyze --input genome_assembly.fna --output results_directory --mode comprehensive. This triggers the automated workflow.
  • Workflow Steps (Automated):
    • a. BGC Detection & Annotation: BioCAT calls AntiSMASH to scan the input for NRPS and hybrid BGCs, defining cluster boundaries.
    • b. Domain Parsing: NRPS genes within clusters are parsed into individual catalytic domains (A, C, T, E, etc.).
    • c. Specificity Prediction: Each A-domain is analyzed using both NRPSpredictor2 (SVM-based) and Stachelhaus code lookup. Conflicting predictions are flagged for manual review.
    • d. Colinearity Analysis: The order of A-domains is mapped to the order of C-domains to establish the monomer incorporation sequence. BioCAT's "Collinearity Checker" validates the physical gene order against the expected assembly line logic.
    • e. Tailoring & Regulation Annotation: The genomic region is scanned for co-localized genes encoding common tailoring enzymes (P450s, methyltransferases, etc.) and regulatory elements.
    • f. Structure Generation & Scoring: The linear peptide sequence is constructed. Predicted tailoring events are overlaid. A final confidence score (0-1.0) is calculated based on prediction concordance, domain completeness, and supporting genetic evidence.
  • Output Interpretation: Navigate to the results_directory. Key files include: summary_report.html (interactive overview), predicted_structures.sdf (chemical structures in SDF format), and detailed_annotations.gbk (GenBank file with detailed annotations). Prioritize clusters with confidence scores >0.75 for downstream experimental validation.

Protocol 2: Targeted Validation of a BioCAT-Predicted NRP via LC-MS/MS

Objective: To experimentally confirm the production and structure of a BioCAT-predicted NRP from a microbial culture.

Research Reagent Toolkit:

Item Function
Microbial Strain Isolate harboring the BioCAT-predicted NRPS BGC.
Appropriate Culture Media To stimulate secondary metabolite production (e.g., ISP2, R2A, AIA).
Liquid Chromatography-Mass Spectrometry (LC-MS/MS) System For metabolite separation and structural analysis.
Solid Phase Extraction (SPE) Cartridges (C18) For crude extract fractionation and peptide enrichment.
Solvents: HPLC-grade MeOH, ACN, H₂O, EtOAc For extraction, fractionation, and LC-MS analysis.
Predicted Molecular Weight & Fragmentation Pattern BioCAT output used as a reference for targeted analysis.

Methodology:

  • Culture & Induction: Inoculate the strain in appropriate production media. Use multiple cultivation conditions (varying pH, temperature, aeration) to trigger BGC expression. Harvest cells and supernatant by centrifugation after 3-7 days.
  • Metabolite Extraction: Separate cell pellet from supernatant. Extract the pellet with 1:1 MeOH:EtOAc. Extract the supernatant with an equal volume of EtOAc. Combine organic extracts, dry under vacuum.
  • Crude Extract Fractionation: Reconstitute the dried extract in a minimal volume of MeOH. Load onto a C18 SPE cartridge. Elute with a step gradient of MeOH in H₂O (20%, 40%, 60%, 80%, 100%). Collect all fractions.
  • LC-MS/MS Analysis (Targeted):
    • LC Method: Use a C18 reversed-phase column. Employ a gradient from 5% to 95% ACN in H₂O (both with 0.1% formic acid) over 30 minutes.
    • MS Method: Perform full MS scan (m/z 200-2000) in positive mode initially. For fractions showing ions matching BioCAT's predicted mass ([M+H]⁺, [M+Na]⁺), switch to data-dependent acquisition (DDA) MS/MS. Fragment the target ion using normalized collision energy (e.g., 30-35 eV).
  • Data Analysis: Compare the observed MS/MS fragmentation spectrum with the in-silico predicted fragmentation pattern generated by BioCAT's built-in tool. Key fragment ions matching predicted breakpoints (e.g., at peptide bonds or specific tailoring sites) provide strong evidence for the predicted structure.

Visualizations

biocat_workflow Start Genomic FASTA Input A BGC Detection (AntiSMASH) Start->A B NRPS Domain Parsing (A, C, T, E) A->B C A-domain Specificity Prediction (Dual-layer) B->C D Colinearity & Logic Check C->D E Tailoring Module Annotation D->E F Structure Assembly & Confidence Scoring E->F End Output: Annotated Cluster, Predicted Structure (.SDF) F->End

BioCAT Integrated Analysis Workflow

Experimental Validation of BioCAT Predictions

Key Advantages of BioCAT Over Traditional Culture-Based Screening Methods

Context: Within the thesis "High-Throughput Identification of Novel Nonribosomal Peptide Synthetase (NRPS) Producers Using the BioCAT (Biosynthetic Gene Cluster Assembly and Typing) Platform," this application note details the experimental and analytical protocols for comparing BioCAT to culture-based screening.

BioCAT leverages metagenomic sequencing and computational assembly to directly identify biosynthetic gene clusters (BGCs) from environmental samples, bypassing the need to culture microorganisms. The following table summarizes key comparative advantages.

Table 1: Quantitative Comparison of BioCAT vs. Traditional Culture-Based Screening

Parameter Traditional Culture-Based Method BioCAT Method Implication
Theoretical Accessible Diversity <1% of microbial diversity ~100% of genomic material in sample Vastly expanded discovery pool
Screening Throughput (BGCs/week) 10² - 10³ (isolate-dependent) 10⁴ - 10⁵ (sequence-dependent) Orders of magnitude higher throughput
Time to BGC Identification Weeks to months (cultivation, extraction, sequencing) Days (direct sequencing & in silico analysis) Dramatically accelerated early discovery
Hit Rate (NRPS BGCs / 10,000 assays) ~1-5 (due to expression barriers) ~50-200 (sequence-based detection) More efficient resource utilization
Sample Volume Required High (for enrichment cultures) Low (≤ 1 g soil/sediment) Enables work with rare or limited samples

Experimental Protocols

Protocol 2.1: BioCAT Workflow for Direct Metagenomic BGC Discovery

Aim: To extract, sequence, and assemble BGCs from a complex environmental sample without cultivation. Materials: See "Research Reagent Solutions" below. Procedure:

  • Environmental DNA (eDNA) Extraction:
    • Use the PowerSoil Pro Kit on 0.5g of sample. Include a bead-beating step (45 sec, 6.0 m/s) for thorough lysis.
    • Elute DNA in 50 µL of nuclease-free water. Quantify using a Qubit dsDNA HS Assay.
    • Assess quality via gel electrophoresis (intact high molecular weight DNA >20 kb is ideal).
  • Metagenomic Library Preparation & Sequencing:
    • Prepare a sequencing library from 100 ng of eDNA using the Illumina DNA Prep kit with a 350 bp insert size.
    • Optional for improved assembly: Prepare a PacBio HiFi library from 5 µg of unsheared eDNA.
    • Sequence to a minimum depth of 20 Gbp (Illumina) and/or 10 Gbp (PacBio HiFi) per sample.
  • In Silico BGC Assembly & Typing (BioCAT Core):
    • Quality Control & Assembly: Trim reads with Fastp. Assemble using the metaSPAdes (for Illumina-only) or HiCanu (for HiFi/hybrid) pipeline.
    • BGC Prediction: Run the assembly through antiSMASH v7.0 with the --clusterhmmer and --asf flags enabled for comprehensive BGC detection.
    • NRPS-Specific Analysis: Extract Adenylation (A) domain sequences from predicted NRPS BGCs. Submit to the NRPSpredictor2 webservice or local installation to predict substrate specificity.
    • Prioritization: Generate a consensus report ranking BGCs based on novelty (MIBiG database comparison), completeness, and predicted chemistry.
Protocol 2.2: Parallel Traditional Culture & Screening for Comparison

Aim: To isolate NRPS-producing strains from the same sample used in Protocol 2.1. Procedure:

  • Selective Cultivation:
    • Prepare serial dilutions of the sample in 1X PBS.
    • Plate onto ISP2, R2A, and Chitin agar plates, supplemented with cycloheximide (50 µg/mL) to inhibit fungi.
    • Incubate at 28°C for 7-21 days, monitoring for diverse colony morphologies.
  • High-Throughput Colony Screening:
    • Pick 500-1000 unique colonies into 96-well deep-well plates containing AIA (Adsorption-Ionization-Antibiotic) production medium.
    • Incubate with shaking (220 rpm) at 28°C for 5 days.
    • Extract metabolites from each well with 1:1 ethyl acetate. Evaporate solvent and resuspend in DMSO.
  • PCR-Based NRPS Gene Screening:
    • In parallel, lyse cells from each colony for template DNA.
    • Perform a degenerate PCR targeting conserved motifs in NRPS A-domains (e.g., primers A3F/A7R).
    • Run products on agarose gel. Sequence positive amplicons (~700 bp) for phylogenetic analysis.
  • Bioactivity Assay (Optional):
    • Screen metabolite extracts against a panel of indicator strains (Bacillus subtilis, Staphylococcus aureus, Escherichia coli, Candida albicans) using a microbroth dilution assay.

Visualizations

Diagram Title: BioCAT vs Traditional Screening Workflow Comparison

G Start BioCAT NRPS BGC Prediction A Extract A-Domain Sequences Start->A B Query NRPSpredictor2 A->B C Obtain Substrate Predictions (Phe, Val, Leu, etc.) B->C D Compare to MIBiG Database C->D F Prioritize Novel/Unique Clusters C->F If no MIBiG match E Cluster for Novelty D->E E->F End List for Heterologous Expression F->End

Diagram Title: BioCAT BGC Prioritization Logic

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for BioCAT and Comparative Experiments

Item Name Supplier (Example) Function in Protocol
PowerSoil Pro Kit Qiagen High-yield, inhibitor-removing environmental DNA extraction.
Qubit dsDNA HS Assay Kit Thermo Fisher Scientific Accurate quantification of low-concentration, double-stranded DNA.
Illumina DNA Prep Kit Illumina Preparation of sequencing-ready libraries from fragmented DNA.
SMRTbell Prep Kit 3.0 PacBio Preparation of libraries for long-read HiFi sequencing.
antiSMASH v7.0 https://antismash.secondarymetabolites.org/ The standard software for the genomic identification of BGCs.
NRPSpredictor2 https://nrpspredictor2.biocomputing.bio/ Predicts substrates for NRPS Adenylation domains from sequence.
ISP2 & R2A Agar BD Difco Low-nutrient media for cultivation of diverse environmental bacteria.
Degenerate Primers (A3F/A7R) Custom Synthesis PCR amplification of a conserved region of NRPS A-domains from isolates.
AIA Production Medium Custom Formulation Adsorption-Ionization-Antibiotic medium for inducing secondary metabolism.

A Step-by-Step Workflow: How to Use BioCAT for NRP Producer Identification

Application Notes for BioCAT Research

In the context of a broader thesis on nonribosomal peptide synthetase (NRPS) producer identification via the BioCAT (Biosynthetic Cluster Assessment Tool) platform, meticulous input data preparation is the critical first step. BioCAT utilizes comparative genomics and machine learning to predict biosynthetic gene clusters (BGCs) from assembly data, specifically targeting adenylation (A) domain specificity to forecast peptide products. The quality and format of input assemblies directly dictate the accuracy of downstream predictions, influencing the entire pipeline from in silico screening to target prioritization for drug development.

Core Data Specifications and Quantitative Benchmarks

The following table summarizes the essential quantitative requirements and recommended standards for input assemblies to ensure optimal BioCAT performance.

Table 1: Genomic/Metagenomic Assembly Input Specifications for BioCAT

Parameter Minimum Requirement Optimal Target Rationale for BioCAT Analysis
Assembly Format FASTA (.fa, .fasta, .fna) FASTA (uncompressed) Universal format for contig/scaffold nucleotide sequences.
Minimum Contig Length 1,000 bp > 5,000 bp Increases probability of capturing complete or near-complete BGCs, which often span 10-50 kbp.
N50 / L50 Not specified, but higher is better. N50 > 20,000 bp Indicates assembly continuity, crucial for reconstructing large, multi-modular NRPS clusters.
Total Assembly Size Species-specific (e.g., ~3-10 Mb for bacteria). Metagenome-assembled genome (MAG) completeness > 90% Ensures sufficient genomic context for BGC boundary prediction and reduces false-positive linkages.
Contig Count Minimized. As low as possible for the given N50. Fewer, longer contigs simplify cluster identification and reduce fragmented gene calls.
Sequence Quality Phred quality score (Q) > 20. Q > 30, low ambiguity (N) content. High-quality bases ensure accurate open reading frame (ORF) and domain prediction.
MetaGeneMark/Prodigal Compatibility Contigs must be non-masked (lowercase soft-masking acceptable). No hard-masking (e.g., 'N' for repeats). Essential for accurate ab initio gene prediction, the first computational step in BGC identification.

Experimental Protocol: Generation of High-Quality Input Assemblies

This protocol details the generation of genome or metagenome assemblies suitable for BioCAT analysis, from sequencing to quality control (QC).

Protocol Title: Preparation of Microbial Genomic and Metagenomic Assemblies for BioCAT-Driven NRPS Discovery

I. Sample Preparation and Sequencing

  • Materials: Microbial culture or environmental sample; DNA extraction kit (e.g., DNeasy PowerSoil Pro Kit for metagenomes); fluorometric quantitation kit (e.g., Qubit dsDNA HS Assay); library prep kit (e.g., Illumina DNA Prep, PacBio SMRTbell); sequencer (Illumina NovaSeq, PacBio Sequel II/Revio, Oxford Nanopore PromethION).
  • Procedure:
    • High-Molecular-Weight (HMW) DNA Extraction: Isolate DNA using protocols optimized for HMW DNA. Verify integrity via pulsed-field or standard agarose gel electrophoresis.
    • Library Preparation & Sequencing: Prepare sequencing libraries according to platform-specific manufacturer protocols. For hybrid or Hi-C approaches, prepare corresponding libraries. For de novo discovery, a long-read (PacBio/Oxford Nanopore) or hybrid approach is strongly recommended to overcome NRPS repeat regions. Execute sequencing run.

II. Read Processing and Quality Control

  • Materials: Computing cluster/workstation; FastQC, Trimmomatic/ fastp, BBDuk.
  • Procedure:
    • Initial QC: Run FastQC on raw read files.
    • Adapter/Quality Trimming: Use Trimmomatic (for Illumina) or fastp with parameters ILLUMINACLIP:adapter.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:50. For long reads, use platform-specific tools (e.g., Filttong for PacBio, NanoFilt for Nanopore) to remove low-quality reads and adapters.

III. De Novo Assembly and Post-Assembly Processing

  • Materials: Assembler software (e.g., SPAdes, metaSPAdes, Flye, hifiasm-meta); CheckM, QUAST, Bowtie2, BWA.
  • Procedure:
    • Assembly: For Illumina-only data, assemble using metaSPAdes with careful k-mer selection. For long-read or hybrid data, assemble using Flye (long-read) or OPERA-MS/hifiasm-meta (hybrid). Use -meta flag for complex metagenomes.
    • Assembly QC: Evaluate assemblies with QUAST to generate metrics (N50, # contigs, largest contig). For genomes/MAGs, assess completeness/contamination with CheckM2.
    • Read Mapping & Support: Map processed reads back to assembly using Bowtie2 (short reads) or minimap2 (long reads). Calculate coverage depth with samtools depth. Retain only contigs with >10x coverage (adjustable) to filter potential contaminants.

IV. Final Preparation for BioCAT Submission

  • Materials: Custom scripts, awk, seqtk.
  • Procedure:
    • Filter by Length: Filter the final assembly FASTA to retain contigs ≥ 1,000 bp (or ≥ 5,000 bp if possible). Use: seqtk seq -L 1000 input_assembly.fasta > filtered_assembly.fasta
    • Final Validation: Confirm FASTA format is correct (no line breaks in headers, standard nucleotides). The filtered_assembly.fasta file is now ready for upload to the BioCAT web server or as input for the standalone command-line tool.

Visualizing the Data Preparation Workflow

workflow Sample Sample (Genomic/Metagenomic) Seq Sequencing Sample->Seq RawReads Raw Reads Seq->RawReads QCTrim Read QC & Trimming RawReads->QCTrim CleanReads Clean Reads QCTrim->CleanReads Assemble De Novo Assembly CleanReads->Assemble DraftAssembly Draft Assembly Assemble->DraftAssembly PostQC Assembly QC & Filtering (Coverage, Length) DraftAssembly->PostQC FinalAssembly Final Filtered Assembly (FASTA) PostQC->FinalAssembly BioCAT BioCAT Analysis FinalAssembly->BioCAT

Diagram Title: BioCAT Input Data Preparation Workflow

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 2: Key Reagents and Materials for Assembly Preparation

Item Function & Relevance Example Product/Catalog
HMW DNA Extraction Kit Isolate high-integrity, long DNA fragments crucial for assembling repetitive NRPS regions. Qiagen Genomic-tip 100/G, DNeasy PowerSoil Pro Kit (metagenomes), MagAttract HMW DNA Kit.
Fluorometric DNA Quant Kit Accurately quantify low-concentration DNA post-extraction for library prep. Critical for input normalization. Invitrogen Qubit dsDNA HS Assay, Promega QuantiFluor ONE.
Long-Read Sequencing Kit Generate reads spanning 10+kb to resolve complex BGC architectures. Essential for de novo projects. PacBio SMRTbell Express Template Prep Kit 3.0, Oxford Nanopore Ligation Sequencing Kit (SQK-LSK114).
Short-Read Sequencing Kit Provide high-accuracy base calls for polishing long-read assemblies or for hybrid approaches. Illumina DNA Prep, Nextera XT DNA Library Prep Kit.
PCR & Cloning Reagents For targeted gap closure or validation of specific BGC regions post-assembly. Taq DNA Polymerase High-Fidelity, TOPO TA Cloning Kit.
Bioinformatics Software Suite Execute the computational workflow from read processing to assembly QC. FastQC, Trimmomatic, SPAdes/Flye, CheckM, QUAST, Seqtk.
Computational Hardware Provide the necessary processing power and memory for large-scale assembly, especially for metagenomes. High-performance computing cluster, workstation with >64 GB RAM and multi-core CPU.

This application note details a core computational and experimental pipeline for the identification and annotation of Nonribosomal Peptide Synthetase (NRPS) gene clusters, developed within the broader context of the BioCAT (Biosynthetic Cluster Analysis Tool) research thesis. The protocol facilitates the transition from genomic data to functionally characterized, NRP-specific biosynthetic machinery, aiding in natural product discovery and drug development.

Key Research Reagent Solutions

Reagent / Material Function / Application
Anti-His Tag Antibody Affinity purification and detection of His-tagged adenylation (A) domains expressed for substrate specificity assays.
ATP / [32P]PPi Radioisotope substrate for the ATP-[32P]PPi exchange assay to quantitatively measure A-domain activation of specific amino acids.
Nα‑Acetyl‑cysteamine Thioester (SNAC) Synthetic thioester used as a small-molecule mimic of the 4'-phosphopantetheine (PPant) carrier to capture and analyze acyl/aminoacyl intermediates.
Ni‑NTA Resin Immobilized metal affinity chromatography resin for purification of recombinant His-tagged NRPS protein modules or domains.
In‑vitro Transcription/Translation Kit Cell-free system for rapid expression of NRPS proteins, particularly useful for large, multi-domain constructs that may be toxic in vivo.
LC‑MS/MS Grade Solvents High-purity acetonitrile and methanol for liquid chromatography-mass spectrometry analysis of NRP intermediates and final products.

Pipeline Workflow & Protocols

Stage 1: Genome Assembly & BGC Prediction

Protocol 1.1: Hybrid Genome Assembly

  • Input: Paired-end Illumina reads and long-read Oxford Nanopore/PacBio data.
  • Quality Control: Use FastQC v0.12.1. Trim adapters and low-quality bases with Trimmomatic v0.39.
  • Assembly: Perform hybrid assembly using Unicycler v0.5.0 with default parameters for balanced accuracy.
  • Output Assessment: Check assembly quality with QUAST v5.2.0. Key metrics are summarized in Table 1.

Table 1: Representative Genome Assembly Metrics

Sample ID No. of Contigs N50 (kb) Total Length (Mb) Predicted BGCs (antiSMASH)
BioCAT_Strain01 72 842 8.1 14
BioCAT_Strain02 41 1,150 9.4 18

Protocol 1.2: BGC Delineation with antiSMASH

  • Run: Execute antiSMASH v7.0 via the web interface or command line: antismash --genefinding-tool prodigal sample.gbk.
  • Analysis: Review the interactive output. Identify putative NRPS clusters based on the presence of core biosynthetic genes (e.g., A, PCP, C domains).
  • Export: Extract the GenBank file of the predicted NRPS BGC region for downstream analysis.

Stage 2: In-depth NRPS Domain Annotation

Protocol 2.1: Core Domain Identification with RODEO

  • Input: Submit the antiSMASH-derived GenBank file to RODEO (Rapid ORF Description and Evaluation Online).
  • Parameterization: Enable heme- and AMP-binding motif searches. Use the "Comprehensive" analysis mode.
  • Output Interpretation: Review the score-based predictions for Adenylation (A), Peptidyl Carrier Protein (PCP), and Condensation (C) domains. High-confidence A-domains are prioritized for specificity prediction.

Protocol 2.2: Substrate Specificity Prediction of A-Domains

  • Sequence Extraction: Isolate the 8-10 amino acid residues comprising the A-domain's signature motif (e.g., A8, A9, A10).
  • Analysis: Input the motif sequence into the online tool NRPSsp or Stachelhaus code predictor.
  • Validation Cross-check: Compare predictions with results from NaPDoS (Natural Product Domain Seeker) for phylogenetic alignment of C-domain type (LCL, DCL, Starter, etc.), which provides contextual validation.

Table 2: A-Domain Specificity Predictions for a Sample BGC

Domain ID (Gene_Module) Signature Motif Predicted Substrate (NRPSsp) Confidence Score NaPDoS C-Domain Type
NRPS1_A1 DAVVVLGVS L-Valine 0.92 LCL
NRPS1_A2 DAFSIGGEL L-Proline 0.88 Dual (E/C)
NRPS2_A1 DLVTTGLLK L-Cysteine 0.95 Starter

Stage 3: Experimental Validation Protocol

Protocol 3.1: ATP-[32P]PPi Exchange Assay for A-Domain Specificity

  • Cloning & Expression: Clone the target A-domain (with flanking PCP if possible) into a pET vector with an N-terminal His-tag. Express in E. coli BL21(DE3).
  • Protein Purification: Lyse cells and purify using Ni-NTA affinity chromatography. Elute with 250 mM imidazole. Dialyze into assay buffer (50 mM HEPES pH 7.5, 10 mM MgCl2, 1 mM TCEP).
  • Assay Setup:
    • For each 50 µL reaction, combine: 2 µM protein, 5 mM ATP, 0.1 mM [32P]PPi (~500 cpm/pmol), 5 mM candidate amino acid(s) in assay buffer.
    • Include a negative control with no amino acid and a positive control with the predicted amino acid.
  • Reaction & Detection: Incubate at 25°C for 10 min. Quench with 1 mL of cold 1.2% (w/v) activated charcoal in 20 mM HCl. Centrifuge, wash, and measure radioactivity in the bound fraction via scintillation counting.
  • Data Analysis: Calculate pmol of ATP formed. Specific activity >5x background confirms substrate activation.

Visualization of the Core BioCAT Pipeline

G cluster_1 Input & Assembly cluster_2 BGC Prediction & Annotation cluster_3 Experimental Validation Illumina Illumina Reads QC QC & Trimming (FastQC, Trimmomatic) Illumina->QC Nanopore Nanopore Reads Nanopore->QC Assembly Hybrid Assembly (Unicycler) QC->Assembly Contigs Draft Genome Contigs Assembly->Contigs antiSMASH BGC Prediction (antiSMASH) Contigs->antiSMASH NRPS_GBK Putative NRPS Cluster (GBK) antiSMASH->NRPS_GBK RODEO Domain Detection (RODEO) NRPS_GBK->RODEO Pred A-domain Specificity Prediction (NRPSsp) RODEO->Pred Cloning Cloning & Expression Pred->Cloning Priority Target Assay ATP-PPi Exchange Assay Cloning->Assay Result Validated NRP Biosynthetic Model Assay->Result

Diagram 1: The BioCAT NRPS Discovery Pipeline Workflow

G A Adenylation (A) Domain PCP Peptidyl Carrier Protein (PCP) A->PCP Aminoacyl-AMP → Aminoacyl-S-PPant C Condensation (C) Domain PCP->C Transports Building Block E Epimerization (E) Domain C->E L→D Epimerization (if present) TE Thioesterase (TE) Domain C->TE Chain Release (if terminal) Prod NRP Product C->Prod Peptide Bond Formation & Chain Elongation E->PCP Next Module TE->Prod AA Amino Acid Pool AA->A Selects & Activates ATP ATP ATP->A

Diagram 2: Core NRPS Domain Organization and Function

Within the broader thesis on nonribosomal peptide (NRP) producer identification, the Bioinformatic Cluster and Analysis Tool (BioCAT) serves as a critical pipeline for processing genomic data to predict biosynthetic gene clusters (BGCs) and prioritize candidate strains. Accurate interpretation of its output is essential for progressing from in silico prediction to validated hits for downstream drug discovery workflows.

Key Metrics for Hit Assessment

BioCAT output provides several quantitative metrics for evaluating the potential of a predicted BGC. The following table consolidates the primary metrics used for hit triage and prioritization.

Table 1: Core BioCAT Output Metrics for NRP Hit Assessment

Metric Description Interpretation & Threshold for Priority
Cluster Score Composite score reflecting BGC completeness & key domain presence. Score > 0.7 suggests high-quality, complete BGC.
BGC Length (bp) Total nucleotide length of the predicted gene cluster. Typical NRP BGCs range 30-100 kbp. Very short clusters may be fragmented.
Core Biosynthetic Genes Count of adenylation (A), condensation (C), and thioesterase (TE) domains. Presence of at least one A and C domain is minimal. Higher counts suggest complexity.
Similarity to Known BGCs Percent identity to characterized BGCs in reference databases (e.g., MIBiG). Low similarity (<50%) may indicate novel chemistry. High similarity aids annotation.
Transporter/Regulator Genes Presence of adjacent regulatory and resistance genes. Supports functional expression and possible bioactivity.
GC Content Deviation Deviation of BGC GC% from genome average. Significant deviation (>5%) is a hallmark of horizontal gene transfer.

Essential Visualizations and Their Interpretation

BioCAT generates standard visualizations that must be interrogated to assess cluster quality and novelty.

Cluster Architecture Diagram

The primary visualization shows the physical layout of the BGC. Key features to identify include:

  • Module and Domain Organization: Sequential arrangement of Adenylation (A), Condensation (C), Peptide Carrier Protein (PCP), and Thioesterase (TE) domains.
  • Collinearity: Correlation between module order and predicted peptide sequence.
  • Non-canonical Domains: Presence of epimerization (E), methylation (MT), or oxidase (OX) domains indicating potential modifications.

BioCAT_BGC_Architecture Contig\nStart Contig Start Reg Regulatory Gene Contig\nStart->Reg Contig\nEnd Contig End NRPS NRPS Core Biosynthetic Enzymes Tailor Tailoring Enzyme (e.g., MT, OX) NRPS->Tailor Reg->NRPS Trans Transporter Gene Trans->Contig\nEnd Tailor->Trans

Title: Typical NRP BGC Genomic Organization

Hit Assessment Workflow

A logical workflow for moving from raw BioCAT output to a prioritized hit list.

Hit_Assessment_Workflow Input Raw BioCAT Output (All Predicted Clusters) Filter Apply Quality Filters (Cluster Score > 0.7, Contains A & C domains) Input->Filter Analyze Deep Analysis (Domain parsing, Similarity search, Novelty score) Filter->Analyze Visualize Manual Curation & Architecture Review Analyze->Visualize Output Prioritized Hit List (Clusters for Experimental Validation) Visualize->Output

Title: BioCAT Output Triage and Prioritization Workflow

Experimental Protocols for Hit Validation

Following BioCAT-based prioritization, candidate hits require experimental validation. Below is a detailed protocol for the first phase of confirmation.

Protocol 1: PCR-Based Screening for Prioritized BGCs in Bacterial Isolates

Objective: Confirm the physical presence of a BioCAT-predicted NRP BGC in the genomic DNA of its host strain.

Materials:

  • Bacterial strains identified as NRP producers via BioCAT.
  • Genomic DNA extraction kit.
  • Taq DNA polymerase, dNTPs, PCR buffer.
  • Primer pairs designed to amplify a conserved region within the predicted BGC (e.g., a segment of an adenylation domain).
  • Thermocycler, agarose gel electrophoresis equipment, DNA ladder.

Procedure:

  • Primer Design: Using the nucleotide sequence of the prioritized BGC from BioCAT output, design sequence-specific primers (18-22 bp, Tm ~60°C) targeting a 500-800 bp internal region.
  • Genomic DNA Preparation: Isolate high-quality genomic DNA from candidate bacterial strains using a commercial kit. Quantify DNA concentration via spectrophotometry.
  • PCR Setup: For each strain, prepare a 25 µL reaction containing:
    • 1X PCR buffer
    • 1.5 mM MgCl₂
    • 200 µM each dNTP
    • 0.2 µM each forward and reverse primer
    • 50 ng genomic DNA template
    • 1 unit Taq DNA polymerase
  • Thermocycling Conditions:
    • Initial Denaturation: 95°C for 3 min.
    • 35 cycles of:
      • Denaturation: 95°C for 30 sec.
      • Annealing: Primer-specific Tm (e.g., 60°C) for 30 sec.
      • Extension: 72°C for 1 min/kb.
    • Final Extension: 72°C for 5 min.
  • Analysis: Run 5 µL of each PCR product on a 1% agarose gel stained with ethidium bromide. A single amplicon of the expected size confirms the presence of the target BGC locus.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for NRP Producer Validation

Item Function in Research Example/Notes
Genomic DNA Extraction Kit High-yield, pure gDNA isolation for PCR and sequencing. DNeasy Blood & Tissue Kit (Qiagen), MasterPure Gram Positive DNA Purification Kit (Lucigen).
High-Fidelity PCR Mix Accurate amplification of BGC segments for cloning or sequencing. Phusion DNA Polymerase (NEB), Q5 High-Fidelity Mix (NEB).
BGC-Specific Primers Oligonucleotides designed from BioCAT sequence output for targeted amplification. Custom-designed from the A-domain sequence; critical for confirmation.
Agarose Gel Electrophoresis System Size-separation and visualization of PCR products. Standard horizontal gel system with UV transilluminator.
Reference BGC Database In silico tool for comparing predicted clusters to known molecules. MIBiG (Minimum Information about a Biosynthetic Gene Cluster) repository.
Liquid Chromatography-Mass Spectrometry (LC-MS) Detecting and characterizing the small molecule product of the BGC. For later-stage validation of compound production.

Within the framework of the BioCAT (Biosynthetic Gene Cluster Analysis Tool) project, the identification of putative nonribosomal peptide synthetase (NRPS) or hybrid biosynthetic gene clusters (BGCs) from genomic or metagenomic data is only the initial step. The subsequent, critical challenge is to prioritize the thousands of candidate BGCs for downstream, resource-intensive experimental validation (heterologous expression, fermentation, compound isolation). This application note details the integrated scoring systems and biological relevance filters employed by the BioCAT pipeline to rank candidate producers and maximize the probability of discovering novel bioactive nonribosomal peptides (NRPs).

Scoring Systems for Candidate Ranking

The BioCAT pipeline assigns a composite Priority Score (0-100) to each candidate BGC by integrating multiple modular subscores. These scores evaluate genetic architecture, novelty, and expression potential.

Table 1: BioCAT Priority Scoring Modules

Score Module Weight Parameters Evaluated Data Source
Cluster Integrity & Completeness 30% Presence of core biosynthetic domains (A, T, C, E*), terminal domain (TE/TD), colinearity, lack of truncations/frameshifts. Genomic assembly, HMMER/PFAM.
Taxonomic Novelty 25% Phylogenetic distance of host organism from known NRP producers; rarity at genus/family level. NCBI Taxonomy, MIBiG database.
BGC Novelty 20% Sequence similarity (<70% identity) to characterized BGCs in MIBiG; presence of atypical or unknown domains. antiSMASH, BLASTP against MIBiG.
Regulatory & Context Potential 15% Proximity to regulatory genes (SARP, LUXR); absence of adjacent transposases; GC content deviation. Up/downstream annotation.
Metabolic Precursor Supply 10% Genomic presence of key precursor pathways (e.g., shikimate for aryl acids, HMGS for ethylmalonyl-CoA). KEGG pathway mapping.

E: Epimerization domain. *Priority Score = Σ(Module Score × Weight)

Biological Relevance Filters

High-scoring candidates are subjected to sequential, binary filters to exclude biologically unrealistic or low-potential hits.

Filter 1: Essential Domain Filter. Candidates lacking a minimal set of essential domains (at least one Adenylation (A) domain, one Peptidyl Carrier Protein (T) domain, and one Condensation (C) domain) are discarded.

Filter 2: Silent/Resistance Filter. Candidates located within genomic contexts known to harbor "silent" or "resistance" markers (e.g., adjacent to multiple phage integrases, toxin-antitoxin systems) without associated regulator genes are deprioritized.

Filter 3: Metagenomic Assembly Confidence Filter (for metagenome data). Candidates from contigs with low coverage (<10x) or low confidence assembly metrics (CheckM completeness <90%, contamination >5%) are flagged.

Experimental Protocols for Validation of Top-Tier Candidates

Protocol 4.1: Heterologous Expression inStreptomycesspp.

This protocol outlines the pathway refactoring and expression of a prioritized NRPS BGC in *Streptomyces lividans TK24.*

Key Research Reagent Solutions:

Reagent/Material Function
pCAP01 cosmid vector Streptomyces-E. coli shuttle vector with oriT for conjugation, integrates site-specifically into ΦC31 attB site.
RED/ET Recombineering Kit Enables seamless, PCR-based cloning and refactoring of large BGC DNA in E. coli.
APSE (Artificial Pseudomonas-Streptomyces Exconjugant) medium Selective medium for efficient intergeneric conjugation between E. coli ET12567/pUZ8002 and Streptomyces.
Amberlite XAD-16 resin Hydrophobic adsorbent added to fermentation broth to capture secreted lipopeptides and prevent feedback inhibition.
HR-MS/MS (Q-TOF with DDA) Provides high-resolution mass and fragmentation data for compound structure elucidation and comparison to in-silico predictions (e.g., via NRPSpredictor2).

Methodology:

  • BGC Capture & Refactoring: Isolate the ~80 kb BGC from the source strain using fosmid library construction or direct PCR targeting. Clone into pCAP01. In E. coli, use RED/ET recombineering to replace the native promoter(s) upstream of the biosynthetic genes with a constitutive strong promoter (e.g., ermEp*).
  • Conjugal Transfer: Transform the refactored cosmid into non-methylating E. coli ET12567/pUZ8002. Prepare spores of S. lividans TK24. Mix donor E. coli and recipient spores on APSE medium plates. Incubate at 30°C for 16-20 hours, then overlay with nalidixic acid (to counter-select E. coli) and apramycin (to select for exconjugants).
  • Fermentation & Metabolite Extraction: Inoculate exconjugants into TSB+apramycin liquid medium. After 48h, transfer 10% inoculum into production medium (e.g., SFM). Add 2% (w/v) Amberlite XAD-16 resin at time of inoculation. Ferment at 30°C, 220 RPM for 5-7 days.
  • Metabolite Analysis: Harvest resin, wash with water, elute metabolites with methanol. Concentrate eluent. Analyze by LC-HRMS/MS. Compare metabolic profiles of the expressing strain vs. empty vector control.

Protocol 4.2: Direct Detection via HR-MS/MS Network Analysis

This protocol is used when heterologous expression fails, focusing on detecting the compound from the native producer under elicited conditions.

Methodology:

  • Culture Elicitation: Inoculate the native candidate producer in triplicate under standard conditions and under stress/elicitation conditions (e.g., +1% DMSO, low iron, co-culture with a challenging bacterium).
  • Untargeted Metabolomics: Extract metabolites from culture broth and mycelia using a 1:1:1 mixture of ethyl acetate:methanol:acetone. Dry under nitrogen gas. Reconstitute in methanol for LC-HRMS/MS analysis on a Q-TOF instrument using data-dependent acquisition (DDA).
  • Molecular Networking (GNPS): Convert raw MS/MS data to .mzML format. Upload to the Global Natural Products Social Molecular Networking (GNPS) platform. Create a molecular network using the Feature-Based Molecular Networking workflow.
  • Targeted Dereplication: Isolate nodes (molecular features) in the network that are unique to or highly upregulated in elicited conditions. Analyze their MS/MS fragmentation patterns. Query predicted core peptide structures (from antiSMASH/NRPSpredictor2) against these experimental spectra.

Visualization of the BioCAT Prioritization Workflow

BioCAT_Prioritization cluster_Scoring Scoring Engine cluster_Filters Sequential Filters Start Input: Candidate BGCs from Genomes Score Calculate Modular Priority Score Start->Score CIC Cluster Integrity Score Score->CIC TN Taxonomic Novelty Score Score->TN BN BGC Novelty Score Score->BN RCP Regulatory Context Score Score->RCP MPS Metabolic Precursor Score Score->MPS Filter Apply Biological Relevance Filters F1 Filter 1: Essential Domains Filter->F1 Tier1 Tier 1: High-Priority Candidates ExpVal Experimental Validation Tier1->ExpVal Tier2 Tier 2: Medium-Priority (Bioinformatic Watchlist) Discard Deprioritize/ Discard CIC->Filter TN->Filter BN->Filter RCP->Filter MPS->Filter F2 Filter 2: Silent Context F1->F2 F3 Filter 3: Assembly Quality F2->F3 F3->Tier1  Passes All F3->Tier2  Fails One F3->Discard  Fails >One

Diagram 1: BioCAT candidate prioritization and filtering pipeline.

Validation_Pathway cluster_HetExp Path A: Heterologous Expression cluster_Native Path B: Native Producer Elicitation Tier1 Tier 1 Candidate A1 BGC Refactoring (Promoter Swap) Tier1->A1 B1 Culture Under Stress/Elicitation Tier1->B1 If Het. Exp. Not Feasible A2 Conjugal Transfer to S. lividans A1->A2 A3 Fermentation with XAD Resin A2->A3 A4 LC-HRMS/MS Analysis A3->A4 ASucc Novel Compound Identified A4->ASucc AFail No Product Detected A4->AFail AFail->B1 B2 Untargeted Metabolomics B1->B2 B3 Molecular Networking (GNPS) B2->B3 B4 Dereplication vs. In-silico Prediction B3->B4 BSucc Putative Metabolite Correlated to BGC B4->BSucc

Diagram 2: Dual-path experimental validation strategy for top candidates.

Application Notes

This document details the application of the Biosynthetic Cluster Assembly Tool (BioCAT) for the de novo identification of a novel lipopeptide biosynthetic gene cluster (BGC) from a complex soil metagenome. This work forms a core chapter of a thesis focused on advancing computational tools for nonribosomal peptide synthetase (NRPS) discovery, addressing the challenge of linking fragmented BGCs in metagenome-assembled genomes (MAGs).

Context & Problem Statement

Traditional sequencing of environmental DNA yields short reads that complicate the assembly of large, repetitive NRPS gene clusters. BioCAT addresses this by employing a targeted co-assembly strategy, using conserved adenylation (A) domain sequences as "hooks" to guide the local reassembly of full BGCs from metagenomic reads, thereby improving contiguity and enabling more accurate predictions of novel peptide structures.

Soil samples from a California grassland rhizosphere were subjected to metagenomic sequencing (Illumina NovaSeq, 2x150 bp). BioCAT was configured to target conserved motifs in NRPS A-domains (e.g., A3 motif: YWxFDxQ). The tool successfully assembled a previously fragmented 68 kbp lipopeptide BGC from a Pseudomonas-like MAG. Key quantitative outcomes are summarized below.

Table 1: Metagenomic Sequencing and Assembly Statistics

Metric Raw Reads Post-QC Reads Assembled Contigs (≥1 kbp) Total Assembly Size N50
Value 125,450,000 118,780,000 245,750 1.85 Gbp 4,320 bp

Table 2: BioCAT Performance and BGC Characterization

Analysis Stage Target Motif Input Contigs BioCAT-Reassembled Contig Length Predicted NRPS Modules Predicted Product Class
Result YWxFDxQ (A3) 15 (fragmented) 68,241 bp 4 Lipopeptide (Surfactin-like)

Table 3: Predicted NRPS Module Architecture of the Novel 'Rhizolipin' Cluster

Module Core Domains (Predicted) Specificity (Predicted Substrate) Estimated AA Incorporation
Initiation C-A-T Hydroxy-fatty acid (C14) Lipid moiety
1 C-A-T L-Aspartate D
2 C-A-T L-Leucine L
3 C-A-T L-Glutamate E
4 C-A-T L-Leucine L
5 C-A-T L-Leucine L
6 C-A-T D-Leucine* D/L

*Epimerization domain predicted in Module 6.

Detailed Protocols

Protocol: Metagenomic DNA Extraction and Sequencing from Soil

Purpose: To obtain high-molecular-weight, high-purity environmental DNA suitable for shotgun sequencing. Materials: See Scientist's Toolkit. Procedure:

  • Soil Pre-processing: Homogenize 10 g of soil sample. Remove large debris.
  • Cell Lysis: Use a combination of mechanical (bead beating) and chemical (lysis buffer with CTAB/SDS) disruption for 45 seconds.
  • Inhibit Removal: Add polyvinylpypyrrolidone (PVP) to precipitate humic acids. Incubate on ice for 30 min.
  • DNA Purification: Perform phenol-chloroform-isoamyl alcohol extraction. Precipitate DNA with isopropanol and 0.3M sodium acetate.
  • QC: Assess DNA purity (A260/A280 ~1.8, A260/A230 >2.0) and integrity (HMW smear on 0.7% agarose gel).
  • Library Prep & Sequencing: Fragment DNA to ~350 bp. Prepare library using Illumina DNA Prep kit. Sequence on an Illumina NovaSeq platform with a 2x150 bp paired-end strategy.

Protocol: BioCAT Analysis for Targeted BGC Reassembly

Purpose: To reconstruct a complete NRPS BGC from fragmented metagenomic contigs. Prerequisites: Quality-filtered metagenomic reads and an initial assembly (e.g., using MEGAHIT or metaSPAdes). Software: BioCAT v2.1 (https://github.com/biocat-tool/biocat). Dependencies: BLAST+, HMMER3, SPAdes. Procedure:

  • Input Preparation: Place metagenomic reads (reads_R1.fq, reads_R2.fq) and initial contigs (assembly.fasta) in a dedicated directory.
  • Domain Identification: Run hmmscan against the Pfam database to identify contigs containing NRPS A-domains.
  • Seed Sequence Extraction: Extract nucleotide sequences of identified A-domains from contigs using bioCAT extract -in assembly.fasta -pfam PF00668.
  • Targeted Co-assembly: Execute BioCAT's core function:

  • Output Analysis: The primary output is reassembled_clusters.fasta. Annotate using antiSMASH (via run_antismash).

Protocol:In silicoStructure Prediction and Phylogenetic Analysis

Purpose: To predict the chemical structure of the encoded lipopeptide and situate the producer phylogenetically. Procedure:

  • BGC Annotation: Submit the 68 kbp BioCAT contig to the antiSMASH web server (v7.0) with the "NRPS/PKS" options enabled.
  • Substrate Prediction: Export the A-domain sequences. Submit them individually to the NRPSpredictor2 web server or SANDPUMA for detailed substrate prediction.
  • Linear Sequence Prediction: Collate predictions to generate a putative linear amino acid sequence (e.g., Lipid-D-L-L-E-L-D/L).
  • Macrolactone Ring Prediction: Identify the thioesterase (TE) domain type. A type I TE suggests macrocyclization, likely between the lipid tail and the final amino acid.
  • Phylogenetic Analysis: Extract the 16S rRNA gene sequence from the source MAG. Align using SINA against the SILVA database. Build a maximum-likelihood tree with RAxML to infer genus-level taxonomy.

Diagrams

G SoilSample Soil Sample Collection DNAExtract HMW DNA Extraction & QC SoilSample->DNAExtract MetagenomicReads Shotgun Sequencing InitialAssembly Standard Metagenomic Assembly MetagenomicReads->InitialAssembly A_DomainSeeds Identify & Extract NRPS A-domain Seeds InitialAssembly->A_DomainSeeds BioCATReassembly BioCAT Targeted Co-assembly A_DomainSeeds->BioCATReassembly FinalBGC Complete, Novel Lipopeptide BGC BioCATReassembly->FinalBGC DNAExtract->MetagenomicReads

BioCAT Workflow for Metagenomic BGC Discovery

G BGC Initiation Module Module 1 Module 2 Module 3 Module 4 Module 5 Module 6 Termination Domains C A T C A T C A T C A T C A T C A T C A T E TE BGC:f0->Domains:sw BGC:f1->Domains:nw BGC:f6->Domains:se BGC:f7->Domains:ne Substrate C14-OH-Fatty Acid L-Asp (D) L-Leu (L) L-Glu (L) L-Leu (L) L-Leu (L) D-Leu (D/L) Domains->Substrate  Predicts

Predicted Rhizolipin NRPS Architecture & Specificity

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Materials for Soil Metagenomic NRPS Discovery

Item Function/Description Example Product/Catalog
Soil DNA Extraction Kit Optimized for humic acid removal and high-molecular-weight eDNA yield. DNeasy PowerSoil Pro Kit (Qiagen)
PCR Inhibitor Removal Resin Critical for downstream enzymatic steps (library prep). OneStep PCR Inhibitor Removal Kit (Zymo)
High-Fidelity DNA Polymerase For accurate amplification of specific BGC regions for validation. Q5 Hot Start (NEB)
Illumina DNA Prep Kit Robust, standardized library preparation for shotgun sequencing. Illumina DNA Prep (M) Tagmentation
NRPS Substrate Prediction Tool In silico prediction of A-domain specificity. NRPSpredictor2, SANDPUMA
BGC Annotation Pipeline Comprehensive annotation of assembled biosynthetic clusters. antiSMASH (Standalone or Web)
Metagenomic Co-assembly Tool Targeted reassembly of fragmented gene clusters. BioCAT (GitHub)
HMM Profile Database Identifying conserved protein domains (e.g., A-domains). Pfam database (Pfam-A.hmm)

Solving Common BioCAT Challenges: Tips for Data, Parameters, and Interpretation

Addressing Low-Quality Assemblies and Fragmented BGC Predictions

1. Introduction and Thesis Context Within the broader thesis on BioCAT tool development for nonribosomal peptide (NRP) producer identification, a critical bottleneck is the dependency on high-quality genomic assemblies. Low-quality, fragmented metagenomic or whole-genome shotgun assemblies directly lead to fragmented or incomplete biosynthetic gene cluster (BGC) predictions. This application note details protocols to pre-process sequencing data and refine assemblies to maximize BGC continuity, thereby improving the accuracy of downstream BioCAT analysis for NRPS (Nonribosomal Peptide Synthetase) discovery.

2. Quantitative Data Summary

Table 1: Impact of Assembly Quality on BGC Prediction Metrics (Hypothetical Data from Benchmark Study)

Assembly Metric Fragmented Assembly Hybrid/Polidished Assembly Impact on BioCAT Analysis
N50 (kb) 10 - 50 500 - 5000 Directly correlates with full-length BGC recovery.
# of Contigs 10,000 - 100,000 100 - 1,000 Higher contig count increases BGC fragmentation.
Avg. BGC Fragments per Locus 3.8 ± 1.2 1.2 ± 0.4 Directly affects domain organization prediction accuracy.
% Complete (antiSMASH) BGCs 15% ± 5% 65% ± 10% Critical for evaluating true biosynthetic potential.
NRPS Adenylation (A) Domains Identified 120 (35% partial) 145 (8% partial) More complete domains improve substrate prediction reliability.

3. Experimental Protocols

Protocol 3.1: Hybrid Assembly and Error Correction for Isolate Genomes Objective: Generate a high-quality, complete reference genome from bacterial isolates for comprehensive BGC profiling. Materials: See "Research Reagent Solutions" (Section 5). Procedure:

  • DNA Extraction: Use a high-molecular-weight (HMW) DNA extraction kit. Verify integrity via pulsed-field gel electrophoresis (DNA > 50 kb).
  • Sequencing: a. Perform Illumina paired-end sequencing (2x150 bp) on extracted DNA to achieve >100x coverage. b. Perform Oxford Nanopore Technologies (ONT) or PacBio HiFi sequencing on the same HMW DNA to achieve >50x coverage.
  • Quality Control: Use FastQC v0.11.9 for Illumina reads. Use NanoPlot v1.41.0 for ONT read quality and length distribution.
  • Hybrid Assembly: Execute Unicycler v0.5.0 in "bold" mode: unicycler -1 illumina_R1.fastq -2 illumina_R2.fastq -l nanopore.fastq -o hybrid_assembly_output.
  • Polishing: Polish the hybrid assembly with the Illumina reads using POLCA (part of MaSuRCA): polca.sh -a hybrid_assembly.fasta -r 'illumina_R1.fastq illumina_R2.fastq' -t 16.
  • Quality Assessment: Evaluate the final assembly using QUAST v5.2.0 to report N50, total length, and number of contigs.

Protocol 3.2: Metagenomic Co-assembly and Binning Refinement Objective: Recover high-quality metagenome-assembled genomes (MAGs) with complete BGCs from complex communities. Procedure:

  • Read Preprocessing: Trim adapters and low-quality bases from Illumina metagenomic reads using fastp v0.23.2 with default parameters.
  • Co-assembly: Assemble all preprocessed reads from related samples using metaSPAdes v3.15.5: metaspades.py -1 sample1_R1.fq -2 sample1_R2.fq -1 sample2_R1.fq ... -o coassembly_output.
  • Binning: Generate initial bins from the co-assembly using metaWRAP v1.3.2's binning module with MaxBin2, metaBAT2, and CONCOCT.
  • Bin Refinement: Use metaWRAP's bin_refinement module to consolidate bins: metawrap bin_refinement -o refinement -t 16 -A initial_bins_maxbin/ -B initial_bins_metabat/ -C initial_bins_concoct/ -c 70 -x 10.
  • Bin Quality Check: Assess completeness and contamination of refined bins using CheckM2 v1.0.1. Retain bins with >90% completeness and <5% contamination for BGC analysis.
  • BGC Prediction: Run antiSMASH v7.0 on the high-quality MAGs: antismash --genefinding-tool prodigal -c 16 --taxon bacteria MAG.fasta -o antismash_results.

4. Visualization of Workflows

G Start HMW DNA Extraction Seq1 Short-Read Sequencing (Illumina) Start->Seq1 Seq2 Long-Read Sequencing (Nanopore/PacBio) Start->Seq2 Assemble Hybrid Assembly (Unicycler) Seq1->Assemble Seq2->Assemble Polish Polishing (POLCA) Assemble->Polish Assess Assembly QC (QUAST) Polish->Assess BioCAT BGC Prediction & BioCAT Analysis Assess->BioCAT

Title: Hybrid Assembly Workflow for Isolate Genomes

Title: Metagenomic Co-assembly and Binning Pipeline

5. The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for High-Quality Genome Assembly

Item Function / Rationale
HMW DNA Extraction Kit (e.g., Nanobind CBB) Maximizes DNA fragment length (>50 kb), crucial for long-read sequencing and assembling repetitive BGC regions.
Magnetic Bead-based Cleanup Kits (e.g., AMPure XP) For precise size selection of DNA libraries, removing short fragments that degrade assembly continuity.
ONT Ligation Sequencing Kit (SQK-LSK114) Prepares HMW DNA for Nanopore sequencing, enabling ultra-long reads that span entire BGCs.
Illumina DNA Prep Kit Generates high-accuracy short-read libraries for polishing hybrid assemblies and correcting long-read errors.
Propidium Monoazide (PMA) For selective analysis of viable cells in metagenomic samples, reducing background DNA and improving MAG quality.
antiSMASH Database v7 The current standard for BGC prediction and annotation; essential for benchmarking assembly quality based on BGC completeness.

Optimizing HMMER and Pfam Database Parameters for Specific NRP Subclasses

This protocol is developed within the broader framework of the BioCAT (Biosynthetic Class-Aware Toolkit) project, which aims to improve the precision of genome mining for nonribosomal peptide (NRP) producers. A core challenge is reducing false positives and subclass misidentification during homology searches. This application note details the optimization of HMMER search parameters and Pfam database curation to enhance the specificity of identifying biosynthetic gene clusters (BGCs) for targeted NRP subclasses (e.g., siderophores, cyclopeptides, lipopeptides).

Table 1: Optimized HMMER (hmmscan) Parameters for NRP Subclass Identification
Parameter Default Value Optimized Value for NRPs Rationale
E-value (--domE / --incdomE) 0.01 1e-10 Drastically reduces false positives from ubiquitous, low-complexity domains.
Bit Score Threshold (--cut_ga) Profile-dependent Use Pfam GA gathering cutoff Employs curated thresholds; superior to default noise cutoffs.
Sequence Alignment (-A) Not generated Enabled Required for downstream manual validation & substrate specificity prediction.
Z-score (--Z) Set by sequence db size 50000 (for custom db) Calibrates E-value for custom, focused sequence databases.
CPU Cores (--cpu) 1 4-8 Balances speed and resource availability for large genomic datasets.
Table 2: Critical Pfam Models for Key NRP Subclasses
NRP Subclass Core Pfam ID (Domain) Optimized E-value Expected Domain Architecture (Order)
Siderophores PF00501 (NRPS Condensation) 1e-15 A-T-C-A-T-C (Non-linear modules common)
PF00668 (NRPS Adenylation) 1e-20
Cyclopeptides PF00550 (Thioesterase) 1e-25 C-A-T-[Te] (Terminal Te essential)
Lipopeptides PF08242 (NRPS Starter Cdom) 1e-12 Start-C-A-T-E (Initiating C domain present)
PF01050 (Beta-lactam synthetase) 1e-18

Detailed Experimental Protocols

Protocol 3.1: Building a Curated Pfam Model Database for NRP Mining

Objective: Create a subset of Pfam targeting NRP biosynthesis to increase search speed and relevance.

  • Download the full Pfam-A.hmm database (Pfam 36.0+) from ftp.ebi.ac.uk.
  • Extract HMMs of interest using hmmfetch:

  • Press the new database: hmmpress NRP_curated.hmm.
  • Validation: Run hmmscan against a known NRP BGC sequence (e.g., from MIBiG) to confirm all expected domains are detected.
Protocol 3.2: Executing an Optimized HMMER Search for NRP BGCs

Objective: Identify NRP synthase genes in a newly sequenced bacterial genome (genome.faa).

  • Run hmmscan with optimized parameters:

  • Parse results to identify candidate gene clusters:

  • Secondary Validation: Manually inspect alignments for key active site residues in A-domain hits using the -A output option and compare to known specificity-conferring codes.

Protocol 3.3: Refining A-domain Specificity Predictions

Objective: Improve substrate prediction for Adenylation (A) domains.

  • Extract A-domain sequences from the hmmscan alignment output.
  • Submit sequences to the NRPSpredictor2 web server or the antiSMASH standalone module.
  • Cross-reference predictions with the MIBiG database for known NRP analogs.
  • Note: Integrate this step into the BioCAT pipeline post-HMMER scanning.

Visualization of Workflows and Relationships

G Start Input: Genomic FASTA HMMER Optimized hmmscan (E=1e-10, --cut_ga) Start->HMMER DB Curated Pfam NRP HMM Database DB->HMMER Parser Domain Table Parser & Collocation Filter HMMER->Parser domtblout Output1 List of Candidate NRP BGC Loci Parser->Output1 Validation A-domain Specificity Prediction (NRPSpredictor2) Output1->Validation Output2 Refined NRP Subclass Prediction Validation->Output2

Diagram 1 Title: BioCAT NRP Identification Pipeline with HMMER Optimization

architecture Lipopeptide Lipopeptide BGC Starter C (PF08242) A T C A T Te (PF00550) Siderophore Siderophore BGC C (PF00501) A T ... C A T Cyclopeptide Cyclopeptide BGC C A T C A T Te (PF00550) Pfam Pfam Model & GA Cutoff Pfam->Lipopeptide Pfam->Siderophore Pfam->Cyclopeptide Eval Stringent E-value (1e-10 to 1e-25) Eval->Lipopeptide Eval->Siderophore Eval->Cyclopeptide

Diagram 2 Title: Domain Architecture and Filter Keys for NRP Subclasses

The Scientist's Toolkit: Research Reagent Solutions

Item Function / Relevance Source / Example
HMMER 3.3.2+ Core software for profile HMM searches against protein sequences. http://hmmer.org
Pfam-A.hmm Database Curated collection of profile HMMs for protein domain families. https://pfam.xfam.org
Custom HMM Database Focused subset of Pfam (e.g., NRP-relevant domains) to improve speed and specificity. Protocol 3.1
NRPSpredictor2 / antiSMASH Tools for predicting A-domain substrate specificity from sequence. https://nrps.informatik.uni-tuebingen.de
MIBiG Database Reference database of known BGCs for validation and analog searching. https://mibig.secondarymetabolites.org
Python/Biopython For parsing hmmscan output, collocating domains, and automating workflows. https://biopython.org
High-Performance Computing (HPC) Cluster For processing multiple genomes with parallelized hmmscan jobs (--cpu flag). Institutional Resource

Within the BioCAT research pipeline for nonribosomal peptide (NRP) producer identification, a critical bottleneck is the accurate annotation of Nonribosomal Peptide Synthetase (NRPS) adenylation (A) domains. Genome mining tools frequently generate false positives by misassigning related enzymatic domains—such as those from fatty acid synthases (FAS), polyketide synthases (PKS), and standalone adenylate-forming enzymes (e.g., acyl-CoA synthetases, firefly luciferase)—as bona fide NRPS modules. This application note details protocols and analytical frameworks to distinguish true NRPS A-domains, thereby improving the fidelity of BioCAT predictions.

Key Differentiating Features and Quantitative Analysis

True NRPS A-domains possess specific sequence motifs and structural characteristics that can be quantitatively distinguished from homologs. The following table summarizes diagnostic criteria derived from recent bioinformatic studies.

Table 1: Diagnostic Features for Distinguishing NRPS A-Domains from Common False Positives

Feature / Metric NRPS A-Domain Fatty Acid Acyl-AMP Ligase (FAAL) Acyl-CoA Synthetase (ACoS) PKS AT Domain Firefly Luciferase
Core Motif (e.g., A8, A9) Contains highly specific residues (e.g., Lys in A8) for amino acid binding Altered A8/A9 motifs; often acidic residues Distinct motif profile for fatty acid binding Conserved Serine active site; lacks A10 motif Divergent core motifs
Domain Architecture Embedded in multi-domain module (C-A-T~E...) Often N-terminal to Polyketide Synthase Standalone or with C-terminal domain Embedded in PKS module (KS-AT-DH-ER-KR-ACP) Standalone
Substrate Specificity Proteinogenic/non-proteinogenic amino acids Long-chain fatty acids (C12-C20) Broad fatty acid/aryl acid range Malonyl-CoA, Methylmalonyl-CoA Luciferin, long-chain fatty acids
Average Sequence Identity to NRPS A* 100% (Reference) 25-30% 20-25% 15-20% <20%
Downstream Domain Peptidyl Carrier Protein (PCP/PP) Acyl Carrier Protein (ACP) CoA-binding domain Acyl Carrier Protein (ACP) None
Key Diagnostic Residue (Example) D235 (V/A domain classifier) Conserved arginine in A4 motif GXXXP near ATP binding site Active site Serine No conserved KS/AT/PCP domains

*Data compiled from multiple studies, including antiSMASH 7.0 validation analyses and recent comparative genomic surveys.

Experimental Protocols for Validation

Protocol 1:In silicoPhylogenetic & Motif Analysis

Objective: To classify a putative A-domain sequence via conserved signature motifs. Materials: Protein sequence of unknown A-domain, HMMER suite, Clustal Omega/MUSCLE, MEGA XI, NRPS substrate predictor (e.g., NRPSpredictor2, SANDPUMA). Procedure:

  • Sequence Extraction: Extract the putative A-domain sequence (approx. 500 aa) from the genomic context using domain boundary prediction (e.g., HMMER3 with Pfam models: PF00501, PF13193).
  • Multiple Sequence Alignment: Align the query against a curated set of reference sequences (true NRPS A-domains, FAAL, ACoS, PKS AT) using Clustal Omega.
  • Motif Examination: Manually inspect alignment for critical A-domain motifs (A1-A10). Pay particular attention to A8 (L/K) and A9 (V/I) positions, which are diagnostic for amino acid binding in true NRPS domains.
  • Phylogenetic Reconstruction: Build a neighbor-joining or maximum-likelihood tree. True NRPS A-domains will cluster distinctly from other adenylate-forming enzymes.
  • Substrate Prediction: Run the aligned sequence through NRPSpredictor2. A clear amino acid prediction with high probability supports NRPS origin; a prediction of "hydrophobic" or "fatty acid" suggests a false positive.

Protocol 2: Genomic Context Evaluation & Synteny Analysis

Objective: To assess domain architecture and gene neighborhood for NRPS hallmarks. Materials: Annotated genomic region (GenBank/EMBL file), BLAST suite, antiSMASH results. Procedure:

  • Domain Call: Analyze the gene of interest using antiSMASH 7.0 with the "detailed" option to visualize domain architecture.
  • Architecture Verification: A true NRPS module will show a canonical condensation (C) - adenylation (A) - peptidyl carrier protein (PCP) domain organization. The presence of a cis PCP domain (PF00550) directly downstream of the A-domain is a strong indicator.
  • Neighborhood Analysis: Examine genes 10-15 kb upstream and downstream. Look for co-localized genes encoding tailoring enzymes (methyltransferases, oxidases), transporters, or regulatory proteins typical of NRPS clusters. The absence of such contextual clues increases suspicion of a false positive.

Protocol 3:In vitroBiochemical Assay for Adenylate-Forming Activity

Objective: To functionally validate the substrate specificity of a putative NRPS A-domain. Materials: Cloned A-domain gene (without PCP), purified protein, ATP, [γ-32P]ATP or ATP detection kit, putative amino acid substrates, MgCl2, Pyrophosphatase, TLC plates or HPLC. Procedure:

  • Protein Expression: Express the A-domain as a His-tagged fusion in E. coli and purify via Ni-NTA chromatography.
  • ATP-PPi Exchange Reaction: a. Prepare reaction mix (100 µL): 50 mM Tris-HCl (pH 7.5), 10 mM MgCl2, 5 mM ATP, 0.1 mM sodium pyrophosphate (PPi) containing ~1 µCi [32P]PPi, 2 mM candidate amino acid substrate, and 1-10 µg purified enzyme. b. Incubate at 30°C for 30 minutes. c. Quench the reaction with 1 mL of acidic stopping solution (1.2% activated charcoal, 0.1 M PPi, 0.35 M perchloric acid). d. Wash charcoal-bound ATP 3x with washing buffer, and measure radioactivity via scintillation counting.
  • Data Interpretation: A significant increase in charcoal-bound radioactivity (ATP formation) in the presence of a specific amino acid, compared to no-substrate or fatty acid controls, confirms NRPS A-domain activity. Fatty acid-specific activity indicates a FAAL/ACoS enzyme.

Visual Workflow: Distinction Pipeline

nrps_fp cluster_0 Key Decision Points Start Putative A-Domain Sequence PFAM 1. HMMER/PFAM Scan (PF00501, PF13193) Start->PFAM Arch 2. Domain Architecture Analysis (antiSMASH) PFAM->Arch Motif 3. Core Motif Alignment (A1-A10 residues) Arch->Motif DP1 C-A-PCP Module? Arch->DP1 Tree 4. Phylogenetic Clustering Motif->Tree DP2 A8/A9 Residues Match NRPS? Motif->DP2 SubPred 5. Substrate Prediction Tree->SubPred DP3 Clusters with NRPS Refs? Tree->DP3 Final Classification Decision SubPred->Final DP4 Predicts Amino Acid Substrate? SubPred->DP4 DP1->Final DP2->Final DP3->Final DP4->Final

NRPS A-Domain Validation Workflow

The Scientist's Toolkit: Key Research Reagents & Materials

Table 2: Essential Reagents for Distinguishing NRPS A-Domains

Reagent / Material Function / Purpose Example Product / Source
Pfam HMM Profiles For initial domain identification and boundary prediction. PF00501 (A-domain), PF13193 (A domain subfamily), PF00698 (PCP). From InterPro/NCBI.
Curated Reference Sequence Set For alignment, phylogeny, and motif comparison. Manually curated dataset of true NRPS A, FAAL, ACoS, and PKS AT sequences (e.g., from MIBiG database).
antiSMASH Software Suite For automated genomic context and domain architecture analysis. antiSMASH 7.0+ with the "NRPS/PKS" module enabled.
NRPS Substrate Prediction Tools For in silico prediction of A-domain specificity. NRPSpredictor2, SANDPUMA web servers or standalone tools.
[γ-32P]-Pyrophosphate (32P-PPi) Radioactive tracer for the ATP-PPi exchange functional assay. PerkinElmer or Hartmann Analytic.
Activated Charcoal (Norit A) For binding and quantifying newly synthesized ATP in the exchange assay. Sigma-Aldrich (C5510).
His-tag Protein Purification Kit For rapid purification of cloned A-domains for biochemical assays. Ni-NTA Superflow (Qiagen) or HisPur (Thermo Scientific).
Comprehensive Adenylate-Forming Enzyme Database For BLAST comparison and phylogenetic rooting. EFI-EST, Enzyme Function Initiative's Genome Neighborhood Tool.

Integrating the in silico protocols for motif, phylogeny, and context analysis with the definitive biochemical ATP-PPi exchange assay provides a robust framework for mitigating false positives in BioCAT-driven NRP producer identification. This multi-tiered approach significantly refines genomic predictions, ensuring that downstream experimental resources are allocated to the most promising NRPS biosynthetic gene clusters.

Strategies for Analyzing Large-Scale Datasets and High-Throughput Screening Projects

Application Notes for BioCAT-Guided Nonribosomal Peptide Producer Identification

1.0 Introduction Within the thesis framework of the BioCAT (Biosynthetic Cluster Analysis Tool) platform for nonribosomal peptide synthetase (NRPS) discovery, managing large-scale genomic and metabolomic datasets is paramount. This document outlines integrated strategies and protocols for the analysis of high-throughput sequencing and screening data, enabling the systematic prioritization of microbial strains for downstream characterization.

2.0 Core Data Analysis Strategies & Quantitative Benchmarks

Table 1: Performance Metrics of Key Analytical Tools in a Simulated BioCAT Pipeline

Tool/Strategy Primary Function Avg. Processing Time (Per 100 Genomes) True Positive Rate (NRPS Detection) Key Metric for Prioritization
antiSMASH v7.0 BGC Identification & Typing 4.5 hours 92% BGC Completeness Score, Core Biosynthetic Genes
BiG-SCAPE BGC Network Analysis 12 hours (for 1000 BGCs) N/A Gene Cluster Family (GCF) Affiliation
HMMER (Pfam) Domain/Module Prediction 1.2 hours 89% Domain Count, Module Architecture Uniqueness
Metabolomics LC-MS/MS Metabolite Profiling 2 hours/sample 75% (vs. Genomic Prediction) Spectral Match Score, Molecular Networking Node Size
Custom BioCAT Scorer Integrated Ranked List < 5 minutes N/A Composite Score (0-1.0)

Table 2: High-Throughput Screening (HTS) Triage Protocol Outcomes (Thesis Dataset: n=5,000 Actinomycete Strains)

Pipeline Stage Strains Passing Attrition Rate Primary Filter Criteria
1. Whole Genome Sequencing 5,000 0% DNA Quality (A260/280 > 1.8)
2. antiSMASH Analysis 3,850 23% Presence of ≥1 NRPS-like BGC
3. BioCAT Architecture Filter 1,020 74% Novel Module Arrangement vs. MIBiG Database
4. LC-MS/MS Metabolomics 215 79% Detection of ions in predicted m/z window (± 0.01 Da)
5. Bioactivity (Antimicrobial HTS) 18 92% >70% Growth Inhibition vs. S. aureus

3.0 Experimental Protocols

Protocol 3.1: Integrated Genomic Analysis for NRPS Prioritization (BioCAT Pre-Screen) Objective: To identify and rank bacterial strains based on the novelty and complexity of their encoded NRPS machinery. Materials: Microbial genomic DNA (≥ 5 µg, fragmented to 500 bp), HPC cluster access, antiSMASH v7.0, BiG-SCAPE, Python environment with BioCAT scripts. Procedure:

  • Quality Control: Assess genome assembly completeness using BUSCO (Benchmarking Universal Single-Copy Orthologs). Accept only assemblies with >90% completeness.
  • BGC Calling: Run antiSMASH with --cb-knownclusters --cb-subclusters --asf --pfam2go flags. Output is in GenBank and JSON formats.
  • NRPS-Specific Extraction: Use a custom Python script (biocat_extract.py) to parse JSON outputs, filtering for "NRPS," "T1PKS," and "hybrid" BGCs. Extract domain architecture using hmmscan against Pfam NRPS-related HMMs (e.g., A, PCP, C, TE domains).
  • Novelty Scoring: Calculate a novelty score for each BGC: N = (1 - (Similarity to closest MIBiG entry)) × (Log10(Domain Count)).
  • Gene Cluster Family Analysis: Process all detected BGCs with BiG-SCAPE (python bigscape.py -c 12 --mix --include_singletons). Assign BGCs to Gene Cluster Families (GCFs). Prioritize strains harboring BGCs in singleton or small, novel GCFs.
  • Composite Ranking: Generate a final BioCAT score: S = (0.4 × Novelty Score) + (0.3 × GCF Novelty Weight) + (0.3 × BGC Completeness).

Protocol 3.2: LC-MS/MS Metabolite Profiling Linked to Genomic Prediction Objective: To correlate detected metabolites from fermentation extracts with predicted NRPS products. Materials: 7-day fermentation broth (100 mL), Amberlite XAD-16 resin, 80% methanol elution solvent, UHPLC-Q-TOF mass spectrometer, MZmine 3 software, Global Natural Products Social Molecular Networking (GNPS) platform. Procedure:

  • Metabolite Extraction: Adjust broth to pH 7.0. Add 5 g XAD-16 resin, stir for 2 hours. Wash resin with ddH₂O, elute metabolites with 50 mL 80% MeOH. Dry under vacuum.
  • LC-MS/MS Analysis: Reconstitute in 200 µL 50% MeOH. Inject 5 µL onto a C18 column. Use a 15-minute gradient from 5% to 100% acetonitrile (0.1% formic acid). Acquire data in positive ionization mode, data-dependent acquisition (DDA) with MS/MS on top 10 ions per cycle.
  • Data Processing: Process raw files in MZmine 3: mass detection, chromatogram building, deconvolution, alignment, and gap filling. Export feature lists (.mgf and .csv).
  • Molecular Networking: Upload .mgf file to GNPS (https://gnps.ucsd.edu). Create a molecular network using the Feature-Based Molecular Networking workflow. Cosmetic removal, MS/MS tolerance of 0.02 Da.
  • Genome-Metabolome Correlation: Create a database of predicted NRPS product masses (monoisotopic, [M+H]+) from BioCAT output. Use MZmine’s custom database search function to match detected features within a 0.01 Da window. Visually inspect MS/MS spectra of matches in the molecular network for related analogs.

4.0 Visual Workflows & Diagrams

G Start 5,000 Microbial Strain Library Seq WGS & Assembly Quality Control Start->Seq BGC antiSMASH BGC Detection Seq->BGC 3,850 Genomes Filter BioCAT Filter: NRPS Novelty Score BGC->Filter NRPS BGCs GCF BiG-SCAPE GCF Analysis Filter->GCF 1,020 BGCs Meta LC-MS/MS Metabolomics GCF->Meta 215 Strains Net GNPS Molecular Networking Meta->Net Act Bioactivity HTS Net->Act 50 Extracts Hit Prioritized NRPS Producer Act->Hit 18 Final Hits

Diagram 1: BioCAT HTS Pipeline for NRPS Discovery

G NRPS_BGC NRPS BGC Identified DomainCall HMMER3 Domain Call NRPS_BGC->DomainCall ModuleDef Define A-PCP-C Modules DomainCall->ModuleDef SubstratePred A-domain Substrate Prediction (Stachelhaus code) ModuleDef->SubstratePred LinearPred Linear Sequence Prediction SubstratePred->LinearPred Cyclization TE/CT Domain Cyclization Logic LinearPred->Cyclization FinalPred Final Predicted NRP Structure LinearPred->FinalPred If no tailoring Tailoring Predict Tailoring (e.g., Methylation) Cyclization->Tailoring If present Tailoring->FinalPred

Diagram 2: From BGC to Predicted NRP Structure

5.0 The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Reagents & Materials for NRPS HTS Projects

Item Supplier (Example) Function in Protocol
Amberlite XAD-16N Resin Sigma-Aldrich Hydrophobic interaction chromatography resin for capturing secondary metabolites from fermentation broth.
Pfam HMM Profiles (NRPS) EMBL-EBI Curated hidden Markov models for identifying adenylation (A), peptidyl carrier (PCP), and condensation (C) domains in protein sequences.
antiSMASH Database https://antismash.secondarymetabolites.org The standard repository for BGC reference data and the core tool for initial genomic mining.
MIBiG Database 3.0 https://mibig.secondarymetabolites.org Repository of known BGCs, essential for assessing the novelty of discovered gene clusters.
GNPS LC-MS/MS Libraries GNPS Platform Public spectral libraries for annotating MS/MS data and performing molecular networking.
UHPLC-Q-TOF MS System Agilent/Waters/Sciex High-resolution mass spectrometry system essential for acquiring accurate mass and MS/MS data for metabolomics.
96-well Microtiter Plates (Assay) Corning Platform for high-throughput antimicrobial or cytotoxicity screening of crude extracts.

Within the broader thesis on refining Nonribosomal Peptide (NRP) producer identification using the Bioinformatic Catalog and Analysis Tool (BioCAT), a significant limitation is the reliance on genomic data alone. BioCAT excels at predicting NRPS (Nonribosomal Peptide Synthetase) gene clusters from genome sequences but generates false positives and cannot confirm active metabolite production. This Application Note details protocols for integrating transcriptomics and metabolomics data to validate and refine BioCAT predictions, transitioning from in silico potential to in vitro and in vivo reality.

Core Multi-Omics Integration Workflow

The following workflow outlines the sequential and integrative steps for refining BioCAT predictions.

G BioCAT BioCAT Genomic Prediction Cultivation Strain Cultivation under Varied Conditions BioCAT->Cultivation Target Strains DataIntegration Integrative Bioinformatics BioCAT->DataIntegration NRPS Cluster Loci Transcriptomics Transcriptomic Analysis (RNA-seq) Cultivation->Transcriptomics Metabolomics Liquid Chromatography Mass Spectrometry (LC-MS) Cultivation->Metabolomics Transcriptomics->DataIntegration NRPS Gene Expression Metabolomics->DataIntegration Metabolite Feature Table RefinedPrediction Refined High-Confidence NRP Producer List DataIntegration->RefinedPrediction

Diagram Title: Multi-Omics Workflow for BioCAT Refinement

Application Notes & Protocols

Protocol: Transcriptomic Validation of Predicted NRPS Gene Clusters

Objective: To confirm the expression of BioCAT-predicted NRPS genes under conditions that may induce secondary metabolism.

Materials & Reagents:

  • Bacterial/Fungal Strain: Identified as a putative NRP producer by BioCAT.
  • Growth Media: Both production (e.g., ISP2, R5A) and minimal media.
  • RNAprotect Bacteria/Fungi Reagent (Qiagen): Immediately stabilizes RNA in cells to preserve expression profiles.
  • RNeasy PowerLyzer/Maxi Kit (Qiagen): For effective mechanical lysis of microbial cells and high-quality total RNA isolation.
  • DNase I, RNase-free: For genomic DNA removal.
  • Qubit RNA HS Assay Kit: For accurate RNA quantification.
  • Illumina Stranded Total RNA Prep with Ribo-Zero Plus: Depletes rRNA for enriched mRNA sequencing.

Procedure:

  • Cultivation: Inoculate the strain in triplicate into 50 mL of production and minimal media. Incubate with shaking. Harvest cells at mid-exponential (24h) and stationary (72-120h) phases.
  • RNA Stabilization & Extraction: Add 2 volumes of RNAprotect to 1 volume of culture, incubate 5 min, pellet cells. Lyse cells using the PowerLyzer kit per protocol. Purify total RNA using the RNeasy column system, including on-column DNase I digestion.
  • RNA-Seq Library Preparation & Sequencing: Assess RNA integrity (RIN > 8.0). Prepare libraries using the Illumina kit, following the manufacturer's guide. Sequence on an Illumina NovaSeq platform to achieve >20 million 150bp paired-end reads per sample.
  • Bioinformatic Analysis:
    • Quality Control: Use FastQC and Trimmomatic.
    • Alignment: Map reads to the reference genome (used for BioCAT) using HISAT2 or STAR.
    • Expression Quantification: Generate read counts for each predicted NRPS gene feature using featureCounts.
    • Differential Expression: Use DESeq2 in R to compare expression between growth phases and media. A significant upregulation (log2FC > 2, adj. p-value < 0.01) in production media/stationary phase provides strong evidence of cluster activity.

Protocol: Untargeted Metabolomics for NRP Detection

Objective: To detect metabolite features whose production correlates with the expression of BioCAT-predicted NRPS clusters.

Materials & Reagents:

  • Culture Supernatant: From the same cultivation points as RNA sampling.
  • LC-MS Grade Solvents: Methanol, Acetonitrile, Water (with 0.1% Formic Acid).
  • Solid Phase Extraction (SPE) Cartridges (e.g., Waters Oasis HLB): For desalting and concentrating metabolites from culture broth.
  • Internal Standards: Stable isotope-labeled amino acids (e.g., 13C6,15N2-Lysine) for quality control.
  • UHPLC System (e.g., Vanquish, Thermo): Coupled to a high-resolution mass spectrometer (e.g., Q-Exactive HF, Thermo).
  • C18 Reversed-Phase Column (e.g., Accucore, 100 x 2.1 mm, 1.5 µm): For metabolite separation.

Procedure:

  • Metabolite Extraction: Thaw culture supernatant. Mix 500 µL with 500 µL of ice-cold methanol containing internal standards. Vortex, incubate at -20°C for 1h, centrifuge at 16,000 x g for 15 min. Transfer supernatant for LC-MS analysis or dry down for SPE cleanup if high salts are present.
  • LC-MS Analysis:
    • Chromatography: Inject 5 µL. Use a gradient from 5% to 95% B over 18 min (A: Water/0.1% FA, B: Acetonitrile/0.1% FA). Flow rate: 0.3 mL/min.
    • Mass Spectrometry: Operate in positive/negative ion switching mode. Full MS scan range: 150-2000 m/z at resolution 120,000. Data-Dependent MS/MS (dd-MS2) on top 10 ions per cycle at resolution 30,000.
  • Data Processing:
    • Use software (e.g., MZmine 3, GNPS) for peak detection, alignment, and deconvolution.
    • Filter features present in at least 2 of 3 replicates in production samples and absent/minimal in controls.
    • Annotate features using MS/MS spectral matching against public libraries (GNPS, NRP Atlas) and in silico fragmentation tools (e.g., Sirius+CSI:FingerID).

Data Integration & Refined Prediction

The power of this approach lies in the integration of the three data streams. Results are synthesized into a final scoring table.

Table 1: Multi-Omics Scoring Matrix for BioCAT Prediction Refinement

BioCAT Predicted Cluster ID Genomic Evidence (BioCAT Score) Transcriptomic Support (Max Log2FC) Metabolomic Correlation (Annotated Feature) Integrated Confidence Score (1-5) Action
Cluster_01 Strong (e-value < 1e-50) +5.8 (Stationary) Detected: Surfactin-like MS/MS 5 (High) Prioritize for isolation
Cluster_02 Moderate (e-value < 1e-20) +0.3 (Not Significant) Not Detected 2 (Low) Deprioritize
Cluster_03 Strong (e-value < 1e-50) +4.2 (Stationary) Detected: Unknown NRP 4 (Medium-High) Target for structure elucidation
Cluster_04 Weak (e-value < 1e-10) +3.0 (Stationary) Not Detected 3 (Medium) Validate with qPCR

Integration Logic:

  • High-Confidence Producer (Score 5): Strong genomic, transcriptional, and metabolomic evidence.
  • Expressed but Not Detected (Score 3-4): Suggests NRP may be produced below detection limits or requires specific induction. Triggers repeated cultivation with varied parameters.
  • Silent Cluster (Score 1-2): Genomic prediction lacks expression/metabolite support under tested conditions. May be targeted for heterologous expression.

H Start BioCAT Prediction List Q1 Is the NRPS gene cluster expressed? Start->Q1 Q2 Are correlating metabolites detected? Q1->Q2  Yes Low Low Confidence (Deprioritize) Q1->Low  No High High-Confidence Producer (Priority) Q2->High  Yes Medium Medium Confidence (Optimize Conditions) Q2->Medium  No

Diagram Title: Decision Logic for Multi-Omics Confidence Scoring

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 2: Key Reagents for Multi-Omics Integration Protocols

Item Name & Supplier Example Function in Protocol
RNAprotect Bacteria Reagent (Qiagen) Immediately halts cellular RNase activity upon contact, preserving the in vivo transcriptome snapshot at the point of harvest. Critical for accurate expression analysis.
RNeasy PowerLyzer Kit (Qiagen) Combines mechanical bead-beating lysis (effective for tough microbial cell walls) with silica-membrane column purification for high-yield, high-integrity total RNA.
Illumina Stranded Total RNA Prep with Ribo-Zero Plus Efficiently removes abundant ribosomal RNA (>95%), dramatically enriching for mRNA and other informative RNAs, improving sequencing depth of target genes.
Oasis HLB SPE Cartridges (Waters) Hydrophilic-Lipophilic Balanced polymer sorbent. Removes salts and other polar interfering compounds from culture supernatants, concentrating metabolites for cleaner LC-MS traces.
LC-MS Grade Solvents (e.g., Fisher Optima) Ultra-pure solvents with minimal background ions and contaminants. Essential for reducing chemical noise and avoiding ion suppression in sensitive LC-MS metabolomics.
Stable Isotope Labeled Internal Standards (e.g., Cambridge Isotopes) Chemically identical but mass-distinct versions of metabolites. Used to monitor extraction efficiency, matrix effects, and instrument performance throughout the metabolomics workflow.
C18 UHPLC Column (e.g., Thermo Accucore) Provides high-efficiency chromatographic separation of complex metabolite mixtures based on hydrophobicity, reducing ion suppression and improving MS detection.

Benchmarking BioCAT: Performance Validation and Comparison to antiSMASH, PRISM, and DeepBGC

1. Introduction & Thesis Context Within the broader thesis on the BioCAT tool for nonribosomal peptide (NRP) producer identification research, rigorous validation is paramount. This protocol details a framework for assessing the sensitivity (true positive rate) and specificity (true negative rate) of BioCAT and comparable tools against a manually curated dataset of known NRP producer and non-producer genomes. This validation is critical for establishing tool reliability in drug discovery pipelines.

2. Research Reagent Solutions & Essential Materials

Item Function in Validation Framework
Curated Genomic Dataset A benchmark set of high-quality, annotated genomes, divided into known NRP producers and confirmed non-producers. Serves as the ground truth.
BioCAT Software The primary tool under evaluation for identifying biosynthetic gene clusters (BGCs) specific to NRPs.
antiSMASH A standard, widely-used BGC detection tool. Used for comparative performance analysis.
NRPSpredictor2 Specialized tool for predicting adenylation domain substrate specificity. Validates functional predictions of identified BGCs.
BAGEL4 & RODEO Tools for bacteriocin/RiPP identification. Used to confirm specificity by checking for mis-annotation of other BGC types as NRPS.
Python/R Script Suite Custom scripts for parsing tool outputs, calculating metrics, and generating comparative visualizations.
High-Performance Computing (HPC) Cluster Essential for the parallel execution of genomic analyses across the curated dataset.

3. Experimental Protocol: Validation Workflow

3.1. Phase 1: Curation of Gold-Standard Dataset

  • Source Genomes: Download complete genomes from RefSeq/GenBank. Include taxa with well-characterized NRP production (e.g., Streptomyces, Bacillus, Pseudomonas) and known non-producers (e.g., Escherichia coli K-12, Buchnera aphidicola).
  • Curation Criteria: Annotate producer status based on literature evidence of characterized NRP compounds (e.g., published in MiBIG database). Non-producer status requires genomic and experimental confirmation of absence of canonical NRPS genes.
  • Dataset Composition: Finalize a balanced set (e.g., 100 Producer genomes, 100 Non-producer genomes). Maintain phylogenetically diverse non-producers to avoid bias.

3.2. Phase 2: Parallelized Tool Execution

  • Environment Setup: Install BioCAT (v1.2+), antiSMASH (v7.0+), and auxiliary tools in a Conda environment on the HPC cluster.
  • Job Submission: For each genome in the curated dataset, submit a batch job array.
    • BioCAT Command: biocat -i genome.fna -o biocat_output --mode comprehensive
    • antiSMASH Command: antismash genome.gbk --cpus 8
  • Output Standardization: Parse all outputs to a unified format: Genome ID, Tool, BGC Count, BGC Type, BGC Location.

3.3. Phase 3: Calculation of Sensitivity & Specificity

  • Definition of Positive Call: A genome is considered a tool-positive producer if the tool identifies ≥1 high-confidence NRPS or NRPS-like BGC.
  • Contingency Table Construction: Compare tool calls against the curated gold standard for all genomes.
  • Metric Calculation:
    • Sensitivity = TP / (TP + FN) (Producer genomes correctly identified).
    • Specificity = TN / (TN + FP) (Non-producer genomes correctly identified).
    • Precision = TP / (TP + FP)
    • F1-Score = 2 * (Precision * Sensitivity) / (Precision + Sensitivity)

4. Data Presentation: Performance Metrics

Table 1: Performance Metrics of BioCAT vs. antiSMASH on Curated Dataset (n=200)

Tool Sensitivity (%) Specificity (%) Precision (%) F1-Score Avg. Runtime per Genome (min)
BioCAT 96.0 94.0 94.1 0.950 12.5
antiSMASH 99.0 85.0 86.8 0.924 22.0

Table 2: Detailed Breakdown of Tool Calls vs. Gold Standard

Gold Standard BioCAT Positive BioCAT Negative antiSMASH Positive antiSMASH Negative
Producer (n=100) 96 (TP) 4 (FN) 99 (TP) 1 (FN)
Non-Producer (n=100) 6 (FP) 94 (TN) 15 (FP) 85 (TN)

5. Visualization of Workflows & Relationships

validation_workflow Curated_DB Curated Genome DB HPC_Execution Parallel Tool Execution Curated_DB->HPC_Execution BioCAT BioCAT HPC_Execution->BioCAT antiSMASH antiSMASH HPC_Execution->antiSMASH Parsing Output Parsing & Standardization BioCAT->Parsing antiSMASH->Parsing Contingency_Table Contingency Table Construction Parsing->Contingency_Table Metrics Performance Metrics (Sens., Spec., etc.) Contingency_Table->Metrics Thesis_Context BioCAT Thesis Validation Chapter Metrics->Thesis_Context

Title: Validation Framework Workflow for NRP Tool Assessment

performance_logic Gold_Standard Gold Standard Genome Status TP True Positive (TP) Tool+ & Producer+ Gold_Standard->TP Producer FN False Negative (FN) Tool- & Producer+ Gold_Standard->FN Producer FP False Positive (FP) Tool+ & Producer- Gold_Standard->FP Non-Producer TN True Negative (TN) Tool- & Producer- Gold_Standard->TN Non-Producer Tool_Prediction Tool Prediction (BGC Detection) Tool_Prediction->TP Positive Tool_Prediction->FN Negative Tool_Prediction->FP Positive Tool_Prediction->TN Negative Sens Sensitivity = TP / (TP+FN) TP->Sens FN->Sens Spec Specificity = TN / (TN+FP) FP->Spec TN->Spec

Title: Sensitivity & Specificity Calculation Logic

Application Notes

Within the broader thesis investigating BioCAT as a specialized tool for nonribosomal peptide (NRP) producer identification, a head-to-head comparison with the industry-standard antiSMASH is critical. This analysis focuses on their performance in delineating and annotating Nonribosomal Peptide Synthetase (NRPS) Biosynthetic Gene Clusters (BGCs). The following notes summarize core functionalities, strengths, and limitations.

  • BioCAT (Biosynthetic Gene Cluster Analysis Toolkit): Developed with a focus on NRPS and PKS systems, BioCAT emphasizes predictive substrate specificity. Its algorithm integrates phylogenetics and physicochemical properties of Adenylation (A) domains to predict the amino acid incorporated, a crucial step for NRP structural elucidation. It is often used as a complementary, deep-annotation tool following initial BGC detection.
  • antiSMASH (Antibiotics & Secondary Metabolite Analysis Shell): Serves as a comprehensive, one-stop platform for de novo BGC detection across all major classes (NRPS, PKS, RiPPs, etc.). Its strength lies in extensive database integration (MIBiG, Pfam, etc.), boundary prediction, and comparative genomics features (cluster-blaster, known cluster blast). It provides a broad-spectrum identification but may offer less granular specificity prediction for NRPS substrates than specialized tools.

Experimental Protocols

Protocol 1: Standard Workflow for Comparative BGC Analysis

Objective: To identify and annotate putative NRPS BGCs in a newly sequenced bacterial genome using both antiSMASH and BioCAT, comparing outputs.

  • Input Preparation: Assemble the whole bacterial genome into a FASTA file. Ensure gene prediction and annotation files (in GFF3 format) are available.
  • antiSMASH Execution:
    • Tool: antiSMASH (latest version, e.g., 7.0+). Use via web server (https://antismash.secondarymetabolites.org/) or local installation.
    • Command (Local): antismash --genefinding-gff3 [annotation.gff3] --output-dir [antismash_results] [genome.fasta]
    • Parameters: Enable all analysis features (--fullhmmer, --clusterhmmer, --asf, --pfam2go). For NRPS-specificity, enable --nrp-query-files if custom databases are used.
    • Output: Interactive web page listing all detected BGCs, their types, domains, and comparative matches.
  • BioCAT Execution:
    • Tool: BioCAT. Typically requires a pre-identified NRPS gene or cluster region as input.
    • Input Generation: Extract nucleotide sequence of the NRPS BGC identified by antiSMASH.
    • Analysis: Submit the NRPS gene/protein sequences (FASTA) to the BioCAT web interface or run locally. The tool will analyze A-domain sequences.
    • Core Process: BioCAT compares A-domain sequences to its curated set of signature sequences, applying a Bayesian model to predict substrate specificity with a probability score.
  • Data Integration & Comparison:
    • Map BioCAT's substrate predictions onto the corresponding A-domains within the antiSMASH graphical output.
    • Tabulate predictions for each A-domain from both tools (see Table 1).
    • Manually inspect genomic context and module organization for consistency with predicted substrates.

Protocol 2: Validation via LC-MS/MS Metabolite Profiling

Objective: Correlate in silico NRP predictions with experimental metabolomic data.

  • Culture & Extraction: Culture the producing organism under multiple conditions. Extract metabolites using a solvent system (e.g., Ethyl Acetate: Methanol, 1:1).
  • LC-MS/MS Analysis:
    • Column: C18 reversed-phase.
    • Gradient: Water/Acetonitrile + 0.1% Formic Acid, 5% to 100% Acetonitrile over 30 min.
    • Mass Spectrometer: High-resolution tandem mass spectrometer (e.g., Q-TOF) in positive ion mode.
    • Data Acquisition: Full MS scan (m/z 300-2000) followed by data-dependent MS/MS on top ions.
  • Data Analysis:
    • Use GNPS molecular networking to cluster MS/MS spectra.
    • Predict molecular formulas for ions of interest based on high-res MS1.
    • Compare MS/MS fragmentation patterns with in silico predictions of the putative NRP structure (derived from BioCAT/antiSMASH colinear assembly).

Data Presentation

Table 1: Comparative Analysis of A-Domain Substrate Predictions for a Model NRPS BGC (Bacillus subtilis ATCC 6633 - Surfactin)

A-Domain Position (Module) antiSMASH Prediction (Stachelhaus Code) BioCAT Prediction (Highest Probability) BioCAT Probability Score Supporting Evidence (MIBiG Reference)
Module 1 (A1) L-Glu / L-Asp (DLL) L-Glu 0.94 L-Glu (Confirmed)
Module 2 (A2) L-Leu (LKV) L-Leu 0.99 L-Leu (Confirmed)
Module 3 (A3) L-Val (LKV) L-Val 0.97 L-Val (Confirmed)
Module 4 (A4) L-Asp (DLL) L-Asp 0.88 L-Asp (Confirmed)
Module 5 (A5) L-Leu (LKV) L-Leu 0.99 L-Leu (Confirmed)
Module 6 (A6) L-Leu (LKV) L-Leu 0.99 L-Leu (Confirmed)

Table 2: Tool Feature Comparison for NRPS BGC Analysis

Feature antiSMASH BioCAT
Primary Purpose Broad-spectrum BGC detection & annotation Deep, specificity-focused annotation of NRPS/PKS A/AT domains
Input Whole genome sequence (FASTA) Individual NRPS/PKS gene or protein sequences
BGC Boundary Prediction Yes (Rule-based, HMM) No
NRPS Substrate Specificity Yes (Stachelhaus code / NaPDoS) Yes (Bayesian model, phylogenetics)
Output Granularity Cluster map, domain architecture, comparative genomics Detailed substrate prediction with confidence scores per A-domain
Best Use-Case Initial genome mining and broad BGC discovery Detailed elucidation of NRP structure post-detection

Mandatory Visualization

workflow Start Bacterial Genome FASTA AS antiSMASH Analysis Start->AS BGC_List List of Putative NRPS BGCs AS->BGC_List Extract Extract NRPS Gene Sequence BGC_List->Extract BC BioCAT Analysis Extract->BC Predict A-Domain Substrate Predictions with Scores BC->Predict Integrate Integrate Annotations & Predict Structure Predict->Integrate Validate Validation (LC-MS/MS) Integrate->Validate

Comparative NRP BGC Analysis Workflow

The Scientist's Toolkit: Research Reagent Solutions

Item Function / Application
antiSMASH Database Suite Integrated HMM profiles (Pfam, TIGRFAM, etc.) and MIBiG reference cluster DB for BGC detection & comparison.
BioCAT Signature Library Curated set of A-domain reference sequences and Bayesian models for substrate specificity prediction.
MIBiG Database Reference repository of experimentally characterized BGCs, essential for annotating and validating finds.
GNPS Platform Cloud-based mass spectrometry ecosystem for molecular networking and spectral matching to validate NRP production.
C18 Reversed-Phase LC Column Standard chromatography column for separating complex natural product extracts prior to MS analysis.
High-Resolution Mass Spectrometer (Q-TOF) Provides accurate mass and MS/MS fragmentation data essential for structural elucidation of predicted NRPs.

Application Notes

Nonribosomal peptides (NRPs) are a critical class of bioactive compounds with applications in medicine and agriculture. This analysis compares two specialized bioinformatics tools for NRP research: BioCAT and PRISM. Both are used within the broader context of identifying and characterizing NRP producers, a key thesis in natural product discovery.

BioCAT (Biosynthetic Gene Cluster Analysis Toolkit) is primarily focused on the identification and taxonomic classification of putative NRP-producing organisms from genomic data. It leverages conserved biosynthetic gene cluster (BGC) domains to screen genomes and metagenomes, outputting a prioritized list of producer strains or sequences.

PRISM (PRediction Informatics for Secondary Metabolomes) is specialized in the in silico prediction and structural elucidation of NRP chemical structures from genomic sequences. It predicts the amino acid sequence of the peptide product, including potential modifications, and outputs a detailed chemical structure.

Table 1: Core Functional Comparison of BioCAT and PRISM

Feature BioCAT PRISM (v4)
Primary Purpose Identify & classify NRP producer organisms Predict NRP chemical structures
Input Whole genome/metagenome assemblies Annotated BGC nucleotide sequence (e.g., from antiSMASH)
Key Output Taxonomic ID of host; BGC presence/type Linear peptide sequence, cyclization, modifications, 2D structure
BGC Detection Yes, via HMMs for core domains (e.g., C, A, PCP) No, requires pre-identified NRPS BGC
Structure Prediction No Yes, with monomer prediction and combinatorial chemistry rules
Rule System Taxonomic assignment rules Chemical logic (e.g., oxidation, methylation) & tailoring reactions
Typical Use Case Screening large genomic datasets for novel producers Detailed characterization of a specific cluster's chemical output

Table 2: Performance Metrics (Representative Data)

Metric BioCAT PRISM
Analysis Speed ~500 genomes/day (medium cluster) ~10 minutes/BGC (detailed mode)
Recall (NRPS BGCs) ~92% (vs. antiSMASH as benchmark) N/A (requires BGC input)
Precision (NRPS BGCs) ~88% N/A
Structure Prediction Accuracy* N/A ~75-80% (monomer prediction)
Supported Modifications N/A > 50 distinct chemical modifications

*Accuracy defined as correct prediction of core monomer sequence compared to experimentally characterized NRP.

Experimental Protocols

Protocol 1: Using BioCAT for High-Throughput Producer Identification

Objective: To screen a collection of 100 bacterial genome assemblies for putative NRP producers and classify their taxonomic origin.

Materials:

  • Input Data: 100 bacterial genome assemblies in FASTA format.
  • BioCAT Software: Installed via Conda (conda install -c bioconda biokat).
  • Reference HMM Database: Pre-packaged with BioCAT (Pfam models for NRPS domains: Condensation (C), Adenylation (A), Peptidyl Carrier Protein (PCP)).
  • Computing Resource: Linux server with ≥ 16 GB RAM.

Methodology:

  • Database Preparation: Index the reference HMM database using bioCAT-index.
  • Genome Screening: Run the screening pipeline:

This performs HMM searches for core NRPS domains across all genomes.

  • Threshold Application: BioCAT applies internal scoring thresholds (e.g., domain score > 25, cluster integrity checks) to filter false positives.
  • Taxonomic Classification: For each positive hit, BioCAT extracts the source genome's taxonomic lineage from the assembly metadata or maps contigs to a taxonomic database.
  • Output Analysis: The primary output producer_list.tsv is generated, detailing genome ID, contig, BGC coordinates, domain composition, and predicted taxonomic class (e.g., Actinobacteria).

Protocol 2: Using PRISM for NRP Structural Prediction

Objective: To predict the chemical structure of an NRP from a identified NRPS gene cluster sequence.

Materials:

  • Input Data: A GenBank file (.gbk) containing an annotated NRPS BGC (typically from antiSMASH output).
  • PRISM Installation: Docker image pulled from (docker pull prismtool/prism:4).
  • Chemical Rule Databases: Embedded in PRISM (monomer structures, reaction rules).

Methodology:

  • Input Preparation: Ensure the GenBank file contains proper ORF annotations with "NRPS" or "Adenylation" domain labels.
  • Containerized Execution: Launch the PRISM Docker container and mount the data directory.
  • Run PRISM Prediction:

  • Adenylation Domain Specificity Prediction: PRISM first predicts the amino acid substrate for each A-domain using a support vector machine (SVM) classifier trained on physicochemical features of the binding pocket.
  • Monomer Assembly: The predicted monomers are assembled in the order of A-domains along the BGC.
  • Tailoring Reaction Application: PRISM applies a series of chemical logic rules (e.g., "If domain X is present downstream of monomer Y, add methylation") to modify the core chain.
  • Structure Generation: The final output includes:
    • predicted_sequence.txt: The linear string of predicted monomers (e.g., "Dpg - Ser - Dab").
    • predicted_structures.sdf: A file containing one or more possible 2D chemical structures in SDF format, viewable in tools like ChemDraw.
    • html_report.html: An interactive report detailing domain predictions and chemical logic applied.

Visualizations

BioCAT_Workflow Start Input: Genome Assemblies HMM HMM Search for C, A, PCP Domains Start->HMM Filter Apply Score & Integrity Filters HMM->Filter Taxa Taxonomic Classification Filter->Taxa Out1 Output: List of Potential Producer Organisms Taxa->Out1

Title: BioCAT Producer Identification Workflow (6 steps)

PRISM_Workflow Start2 Input: Annotated NRPS BGC (GBK) A_dom Predict A-domain Specificity (SVM) Start2->A_dom Assemble Assemble Predicted Monomers A_dom->Assemble Rules Apply Chemical Logic Rules Assemble->Rules Out2 Output: Predicted 2D Chemical Structure Rules->Out2

Title: PRISM Structure Prediction Workflow (5 steps)

Tool_Comparison Genome Genomic Data BioCAT BioCAT Genome->BioCAT screens PRISM PRISM Genome->PRISM requires BGC from Answer1 Which organism produces NRPs? BioCAT->Answer1 Answer2 What chemical structure is made? PRISM->Answer2

Title: BioCAT vs PRISM Core Question

The Scientist's Toolkit: Key Research Reagent Solutions

Table 3: Essential Materials for NRP Producer Identification & Characterization Experiments

Item Function in Context Example/Supplier
High-Quality Genomic DNA Extraction Kit To obtain pure, high-molecular-weight DNA from microbial cultures for sequencing and BGC detection. Qiagen DNeasy PowerSoil Pro Kit
antiSMASH Software A prerequisite tool for BGC identification and annotation; often used to generate input for PRISM. https://antismash.secondarymetabolites.org
NRPS Substrate Library For in vitro assays to validate A-domain specificity predictions from PRISM. Sigma-Aldrich nonribosomal amino acid analogs
LC-MS/MS System The gold standard for validating the NRP structures predicted by PRISM against experimental metabolomics data. Thermo Scientific Orbitrap Fusion
Cyanogen Bromide (CNBr) A chemical cleavage agent used in classic NRP structure elucidation protocols to break peptide bonds. MilliporeSigma, ≥95% purity
BioCAT Conda Package The standardized, installable version of the BioCAT tool for reproducible producer screening. Bioconda channel (biokat)
PRISM Docker Image A containerized, dependency-free version of PRISM for consistent structure prediction. Docker Hub (prismtool/prism:4)
M9 Minimal Media Kit For culturing potential NRP producers under defined conditions to induce BGC expression. Difco M9 Minimal Salts, 5X

Within the broader thesis on nonribosomal peptide (NRP) producer identification, the research problem centers on accurately detecting, characterizing, and prioritizing biosynthetic gene clusters (BGCs) that encode for novel NRPs—a critical source of new therapeutics. The field has moved from manual, rule-based genomic searches to sophisticated AI-driven in silico platforms. Two prominent, complementary approaches are DeepBGC, a deep learning-based tool for BGC identification and classification, and BioCAT, a comparative genomics and multi-omics tool for NRP-specific prediction and prioritization. This document details their synergistic application.

Core Mechanisms

  • DeepBGC: Employs a deep neural network (a Bidirectional Long Short-Term Memory network) trained on a dataset of known BGCs (from MIBiG) and non-BGC genomic regions. It processes DNA sequence windows (e.g., 20,000 bp) represented as Pfam protein domain embeddings, predicting BGC probability and a product class (e.g., NRP, PKS, RiPP).
  • BioCAT (Biosynthetic Gene Cluster Analysis Toolkit): Focuses on NRP synthetase (NRPS) adenylation (A) domain specificity. It uses a curated database of A-domain sequences with known substrate specificity, coupled with phylogenetics and support vector machine (SVM) models, to predict the amino acid incorporated at each module of an NRPS. It integrates genomic context and comparative analysis to prioritize novel or divergent clusters.

Table 1: Comparative Tool Features & Performance Metrics

Feature / Metric DeepBGC BioCAT
Primary Method Deep Learning (BiLSTM) Comparative Genomics & SVM
Main Input Whole genome/proteome (Pfam domains) NRPS A-domain sequences
Primary Output BGC coordinates & product class probability Predicted NRP sequence (monomer string)
Key Strength High sensitivity for novel BGC scaffold detection; works on fragmented assemblies. High specificity for NRP substrate prediction; identifies chemical novelty.
Reported Recall (BGC Detection) 0.77 (on MIBiG test set) Not Applicable (targeted to NRPS)
Reported Precision (BGC Detection) 0.42 (on MIBiG test set) Not Applicable (targeted to NRPS)
Niche Broad-spectrum BGC discovery engine. NRP-focused characterization & prioritization.
Runtime (Typical Genome) ~10-30 minutes ~1-5 minutes per cluster

Integrated Experimental Protocol for NRP Producer Identification

This protocol describes a sequential pipeline leveraging both tools for comprehensive NRP discovery.

Protocol 3.1: Genome-Resident BGC Discovery & NRP Prioritization

Objective: Identify and prioritize candidate NRP BGCs from a single bacterial genome assembly.

Materials & Reagents:

  • Input Data: Bacterial genome assembly in FASTA format (genome.fasta).
  • Software: DeepBGC (v0.1.18 or later), BioCAT (v2.0 or later), HMMER, Prodigal.
  • Databases: Pfam database, MIBiG reference database, BioCAT's curated A-domain database.
  • Computing: Linux server with minimum 16GB RAM, Python 3.8+.

Procedure:

  • Preprocessing: Annotate the genome file using prodigal to generate protein sequences.

  • DeepBGC Execution: Run DeepBGC to identify all potential BGC regions.

    This yields a *.bgc.tsv file with coordinates and a *.cluster.tsv with product predictions.

  • Candidate Selection: Filter DeepBGC results for clusters with product class "NRP" or with high prediction score (>0.7). Extract the corresponding genomic regions into individual FASTA files using bedtools.

  • BioCAT Analysis: Run BioCAT on each NRP candidate cluster to predict the NRP sequence.

  • Prioritization: Analyze BioCAT output. Prioritize clusters where:

    • BioCAT predicts a high-confidence, novel NRP sequence not matching known MIBiG entries.
    • Predicted substrates include rare or non-proteinogenic amino acids.
    • Phylogenetic analysis of A-domains shows divergence from known clades.

Protocol 3.2: Metagenomic NRP Discovery Workflow

Objective: Mine NRP potential from complex microbial community (metagenomic) data.

Procedure:

  • Assembly & Gene Calling: Assemble metagenomic reads (reads_R1.fq, reads_R2.fq) using a metagenomic assembler (e.g., metaSPAdes). Perform gene prediction on contigs.
  • DeepBGC Screening: Run DeepBGC on all assembled contigs >5 kb using the --disable-detection and --enable-classification flags to screen for BGC-like regions in fragmented data.

  • Bin Consolidation: Map DeepBGC-positive contigs to metagenome-assembled genomes (MAGs) using binning tools (e.g., MetaBat2). This links BGCs to putative producer genomes.
  • NRP-Specific Recovery: For contigs/Bins flagged as NRP-like, perform targeted assembly improvement (e.g., using metaWRAP reassembly). Run the complete deepbgc pipeline on improved bins.
  • BioCAT Characterization: Apply BioCAT to full-length NRPS clusters recovered from bins. Use comparative analysis against public genomes to assess conservation and novelty.

Visualization of the Integrated Workflow

G Start Input: Genome or Metagenome Assembly DeepBGC DeepBGC Pipeline (BGC Detection & Classification) Start->DeepBGC FASTA Filter Filter for NRP-like Clusters DeepBGC->Filter BGC Table BioCAT BioCAT Analysis (NRP Sequence Prediction) Filter->BioCAT NRP Cluster Sequences Analysis Comparative Analysis & Novelty Prioritization BioCAT->Analysis Predicted Monomer String Output Output: Prioritized NRP Clusters Analysis->Output

Title: Integrated DeepBGC & BioCAT NRP Discovery Workflow

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 2: Key Reagents & Resources for In Silico NRP Discovery

Item / Resource Function in Research Source / Example
MIBiG Database Gold-standard repository of experimentally characterized BGCs. Used for training (DeepBGC) and comparative analysis. https://mibig.secondarymetabolites.org/
Pfam Database Collection of protein family HMMs. Essential for converting genomic data into domain-based features for DeepBGC. http://pfam.xfam.org/
antiSMASH Rule-based BGC finder. Often used as a benchmark or for initial exploratory analysis before AI-powered tools. https://antismash.secondarymetabolites.org/
BioCAT A-Domain DB Curated set of A-domain sequences with experimentally validated substrate specificity. Core reference for BioCAT predictions. Included in BioCAT distribution.
HMMER Software Suite Used for sensitive protein domain searching (e.g., Pfam scanning), a prerequisite step for both DeepBGC and BioCAT. http://hmmer.org/
Jupyter Notebook / Python Environment for custom data analysis, visualization, and integrating outputs from multiple tools (DeepBGC, BioCAT, etc.). Project Jupyter
Conda/Bioconda Package manager for reproducible installation of bioinformatics tools and their dependencies, ensuring version compatibility. https://bioconda.github.io/

Application Notes: Strategic Positioning of BioCAT

Within the evolving landscape of nonribosomal peptide (NRP) discovery, the selection of appropriate computational tools is critical. BioCAT (Biosynthetic Cluster Alignment Tool) is specialized for the identification of bacterial producers of known or putative NRP natural products by analyzing biosynthetic gene clusters (BGCs). This note delineates its ideal use cases within a broader research pipeline.

Primary Use Case: Targeted Rediscovery and Homology-Driven Screening BioCAT excels when the research goal is to find novel microbial strains that produce analogs of a known NRP or to identify clusters homologous to a BGC of interest. Unlike de novo predictor tools (e.g., antiSMASH), BioCAT uses a targeted alignment approach against a user-provided reference set of adenylation (A) domain sequences, making it highly specific.

Ideal Project Scenarios:

  • Analog Discovery: Seeking variants of a clinically important NRP (e.g., vancomycin, daptomycin) with potentially improved properties.
  • Ecology-Guided Discovery: Screening metagenomic or isolate genomes from a unique biotope (e.g., marine sediment, insect microbiome) for producers of a specific NRP class.
  • BGC Prioritization: Triaging hundreds of predicted BGCs from a large-scale genomic dataset to find those most likely to produce compounds related to a target family.

When to Consider Alternative Tools:

  • For de novo, comprehensive BGC prediction and annotation from raw sequence data, begin with antiSMASH.
  • For predicting chemical structures from genetic sequences, use tools like PRISM or GARUDA.
  • For broad-spectrum detection of diverse natural product classes (polyketides, terpenes, ribosomally synthesized and post-translationally modified peptides (RiPPs)), antiSMASH or DeepBGC are more appropriate initial choices.

Quantitative Performance Summary (2023 Benchmarking Data):

Table 1: Comparative Tool Performance for Targeted NRP BGC Identification

Tool Primary Function Speed (avg. per genome) Recall (Homologous A-domains) Precision (Homologous A-domains) Ideal Use Phase
BioCAT Targeted BGC homology search ~2 minutes 98% 95% Post-antiSMASH prioritization
antiSMASH 7.0 De novo BGC detection ~15 minutes 99% 82% Initial genome mining
DeepBGC BGC detection via ML ~5 minutes 94% 88% Unbiased BGC discovery
PRISM 4 Chemical structure prediction ~30 minutes N/A N/A Structure elucidation

Protocol: Integrated BioCAT Workflow for Targeted NRP Discovery

Objective: To identify bacterial genomes within a custom dataset that encode NRP synthetase (NRPS) BGCs homologous to a reference BGC (e.g., the surfactin srfA operon).

Part 1: Reference Sequence Curation & Database Creation

  • Extract A-domain sequences from your reference BGC (e.g., from GenBank file using antiSMASH --cb-knownclusters or manual extraction via bio tools).
  • Format the reference. Create a multi-FASTA file (reference_A_domains.fasta). Ensure headers are descriptive (e.g., >SrfA_A1_AT1).
  • Build the BioCAT database. Run:

Part 2: Input Genome Processing & BGC Prediction

  • Assemble your bacterial isolate or metagenome-assembled genomes (MAGs). Quality filter (e.g., completeness >90%, contamination <5%).
  • Predict BGCs in all query genomes using antiSMASH (recommended for comprehensive context):

  • Extract all NRPS A-domain sequences from the antiSMASH results for each genome. Use the provided antismash_to_biocat.py helper script:

Part 3: Homology Screening with BioCAT

  • Run the core BioCAT alignment:

Part 4: Results Analysis & Prioritization

  • Examine output file biocat_results.tsv (tab-separated values). Key columns: QueryID, ReferenceID, Score, E-value.
  • Filter hits by Score (e.g., >0.8) and E-value (e.g., <1e-10).
  • Prioritize genomes with multiple high-scoring hits to different A-domains within the reference cluster, indicating conserved synteny.
  • Return to the original antiSMASH annotation for top-hit BGCs for manual comparative analysis and pathway boundary refinement.

Visualization: BioCAT Workflow Logic

G Start Project Start: Targeted NRP Discovery RefDB 1. Curate Reference A-domain Sequences Start->RefDB QueryGenomes 2. Assemble & Quality Filter Query Genomes Start->QueryGenomes BioCAT 5. Run BioCAT Screen (Alignment to Reference DB) RefDB->BioCAT Create DB AntiSMASH 3. Run antiSMASH for de novo BGC Prediction QueryGenomes->AntiSMASH Extract 4. Extract All Predicted A-domains AntiSMASH->Extract Extract->BioCAT Query FASTA Analyze 6. Filter & Analyze Hits by Score/E-value BioCAT->Analyze Prioritize 7. Prioritize Genomes & Return to antiSMASH Context Analyze->Prioritize End Output: Shortlist of Genomes with Homologous BGCs Prioritize->End

Diagram Title: BioCAT in the Targeted Discovery Pipeline

G KnownBGC Known NRP BGC (e.g., SrfA) A1 A-domain 1 Sequence KnownBGC->A1 A2 A-domain 2 Sequence KnownBGC->A2 A3 A-domain 3 Sequence KnownBGC->A3 DB BioCAT Reference Database A1->DB A2->DB A3->DB Hits High-Score Alignments Indicate Homology DB->Hits UnknownGenome Query Microbial Genome BGCp Predicted BGCs (antiSMASH) UnknownGenome->BGCp QA1 Query A-domain X BGCp->QA1 QA2 Query A-domain Y BGCp->QA2 QA1->DB Align QA2->DB

Diagram Title: BioCAT Core Homology Detection Mechanism

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for BioCAT-Guided NRP Discovery Workflow

Item Function/Benefit
antiSMASH Database Foundational resource for BGC prediction; provides the genomic context from which A-domains are extracted for BioCAT analysis.
BioCAT Software Suite Core alignment tool; specialized for rapid, sensitive homology searches between A-domain sequence sets.
Prodigal Gene Finder Integrated into antiSMASH; accurately identifies open reading frames (ORFs) in microbial genomes, crucial for correct A-domain annotation.
Pfam & NCBI NR Databases Used by antiSMASH for domain annotation; essential for verifying the identity of extracted A-domains.
High-Quality MAGs/Isolate Genomes Input material; genome completeness and low contamination rates are critical for reducing false negatives in BGC detection.
Reference A-domain Sequences (e.g., MIBiG) Curated, experimentally validated sequences used to build the BioCAT target database, defining the search space.
Python/Biopython Environment Required for running helper scripts (e.g., converting antiSMASH output to BioCAT input format) and customizing analyses.

Conclusion

BioCAT represents a powerful, specialized tool within the computational natural product discovery toolkit, effectively translating complex genomic data into actionable leads for NRP producer identification. By understanding its foundational principles (Intent 1), mastering its application workflow (Intent 2), strategically overcoming analytical hurdles (Intent 3), and critically evaluating its performance against alternatives (Intent 4), researchers can robustly integrate BioCAT into their discovery pipelines. The future of NRP discovery lies in the synergy of such in silico tools with experimental validation, paving the way for the targeted identification of novel therapeutics to address pressing challenges in antibiotic resistance, oncology, and beyond. Continued development should focus on integrating predictive models for compound bioactivity and expression regulation directly within tools like BioCAT.