BioNavi-NP Tutorial: A Complete Guide to AI-Powered Natural Product Pathway Design for Drug Discovery

Abstract

This comprehensive tutorial provides researchers and drug development professionals with a step-by-step guide to BioNavi-NP, a groundbreaking AI platform for designing biosynthetic pathways of complex natural products. Covering foundational concepts, practical methodology, advanced troubleshooting, and comparative validation, the article equips users to navigate the platform's interface, design novel pathways for bioactive compounds, optimize predictions, and benchmark results against existing methods. The tutorial aims to accelerate the discovery and engineering of new pharmaceuticals.

Getting Started with BioNavi-NP: Understanding AI-Driven Pathway Prediction

Application Notes

BioNavi-NP is a novel, knowledge-based computational platform designed to predict biosynthetic pathways for natural products (NPs) from their chemical structures. It addresses the central challenge in natural product research: deducing the enzymatic assembly sequence from a target molecule's 2D structure. The platform integrates biochemical reaction rules, enzyme function databases (e.g., MIBiG), and retrosynthetic logic to propose plausible biosynthetic routes.

Core Capabilities:

• Retrobiosynthesis Analysis: Deconstructs a query NP scaffold into potential biosynthetic building blocks (e.g., acetyl-CoA, malonyl-CoA, amino acids) via generalized enzymatic transformations.
• Pathway Ranking: Proposes and scores candidate pathways based on enzymatic plausibility, similarity to known pathways, and thermodynamic feasibility.
• Enzyme Family Annotation: Suggests candidate enzyme families (e.g., polyketide synthases, non-ribosomal peptide synthetases, terpene cyclases) for each predicted biosynthetic step.
• Genome Mining Integration: Outputs can guide the identification of candidate biosynthetic gene clusters in microbial genomes.

Quantitative Performance Metrics: Recent benchmarking studies (2023-2024) against experimentally validated pathways demonstrate the utility of BioNavi-NP.

Table 1: BioNavi-NP Pathway Prediction Performance

Metric	Value	Description / Test Set
Top-1 Pathway Accuracy	42%	Correct pathway predicted as first rank (50 diverse NPs)
Top-3 Pathway Accuracy	71%	Correct pathway within top 3 proposed ranks
Average Prediction Time	~90 seconds	Per NP structure (standard workstation)
Rule Database Coverage	1,850+	Unique enzymatic reaction rules
MIBiG Reference Linkage	2,100+	Linked known biosynthetic gene clusters

Experimental Protocols

The following protocols outline key steps for in silico and in vitro experimental validation of a BioNavi-NP-predicted pathway.

Protocol 1:In SilicoPathway Prediction using BioNavi-NP Web Server

Objective: To predict the biosynthetic pathway for a target natural product.

Materials:

• Computer with internet access.
• Molecular structure of target NP in SMILES or SDF format.

Methodology:

• Structure Submission: Access the BioNavi-NP web server. Input the SMILES string or upload the SDF file of the target NP.
• Parameter Setting: Set analysis parameters.
  • Building Blocks: Select "Common NP precursors" (default).
  • Max Steps: Set to 15 (default).
  • Similarity Threshold: Set to 0.7 for rule matching.
  • Check "Enable MIBiG cross-reference."
• Run Prediction: Initiate the retrobiosynthesis analysis. The job will queue and process.
• Result Analysis: Download the result package. It typically contains:
  • ranked_pathways.csv: A table of top-ranked pathways with similarity scores.
  • stepwise_reactions/: Folder with detailed reaction diagrams for each step of top pathways.
  • `enzymes_prediction.txt:* Suggested enzyme families (e.g., "Type I PKS, Ketoreductase") for each reaction step.
• Validation: Manually compare the top predicted pathway against literature or use the provided MIBiG BGC IDs to examine related known gene clusters.

Protocol 2:In VitroReconstitution of a Predicted Core Enzymatic Step

Objective: To experimentally test a key transformation predicted by BioNavi-NP using heterologously expressed enzymes.

Materials:

• Cloned Genes: Expression plasmids for the predicted biosynthetic enzymes (e.g., a PKS module).
• E. coli BL21(DE3): Expression host.
• LB Medium & Antibiotics: For cell growth and plasmid maintenance.
• IPTG: Inducer for protein expression.
• Nickel-NTA Agarose: For His-tagged protein purification.
• Substrate: Chemically synthesized or isolated predicted intermediate.
• LC-MS System: For reaction product analysis.

Methodology:

• Enzyme Production: Inoculate 50 mL LB cultures with E. coli harboring enzyme plasmids. Grow at 37°C to OD600 ~0.6. Induce with 0.2 mM IPTG. Shift to 18°C and incubate for 18 hours.
• Protein Purification: Pellet cells by centrifugation. Lyse via sonication. Clarify lysate and apply supernatant to a Ni-NTA column. Wash with 20 mM imidazole buffer and elute with 250 mM imidazole buffer. Desalt into assay buffer (50 mM HEPES, pH 7.5, 10% glycerol).
• In Vitro Assay: Set up a 100 µL reaction containing:
  • 50 mM HEPES (pH 7.5)
  • 2 mM substrate
  • 5 mM MgCl₂
  • 2 mM ATP (if required)
  • 10-50 µg of purified enzyme(s)
• Incubation & Analysis: Incubate at 30°C for 2 hours. Quench by adding 100 µL of cold methanol. Vortex, centrifuge, and analyze the supernatant by LC-MS. Compare the chromatogram and mass spectra to controls (no enzyme, heat-inactivated enzyme) to identify new product peaks matching the predicted next intermediate.

Visualizations

BioNavi-NP Core Workflow

Prediction Algorithm Logic

The Scientist's Toolkit

Table 2: Key Research Reagent Solutions for Pathway Validation

Item	Function in Validation	Example/Supplier
Heterologous Expression System	Produces candidate biosynthetic enzymes for in vitro assays.	E. coli BL21(DE3), S. cerevisiae, cell-free systems.
Affinity Purification Resin	Rapid purification of recombinant His-/GST-tagged enzymes.	Ni-NTA Agarose (Qiagen), Glutathione Sepharose (Cytiva).
Coenzyme/Substrate Library	Provides predicted biosynthetic precursors and cofactors.	Sigma-Aldrich (acetyl-CoA, malonyl-CoA, SAM, common amino acids).
LC-MS System with UV/Vis PDA	Analyzes in vitro assay products, detects chromophores, confirms molecular weight.	Agilent 1260/6125, Thermo Scientific Vanquish/Orbitrap.
Gene Synthesis & Cloning Service	Rapidly obtains codon-optimized genes for predicted enzymes.	Twist Bioscience, GenScript.
CRISPR-Cas9 Toolkits	For genome editing in native producers to knock-out/knock-in genes from predicted pathways.	IDT Alt-R CRISPR-Cas9 system.

Within the BioNavi-NP tutorial framework for natural product (NP) pathway design, the core AI architecture is a specialized neural network that predicts viable retrobiosynthetic routes. This system deconstructs complex target NPs into plausible biosynthetic precursors by learning from known enzymatic transformations and biochemical rules, enabling researchers to propose novel biosynthetic pathways for engineering in heterologous hosts.

Core Neural Network Architecture & Performance Data

The BioNavi-NP prediction engine integrates a Transformer-based neural network with a Monte Carlo Tree Search (MCTS) for exploration. The model is trained on biochemical reaction data from public databases (e.g., MIBiG, BRENDA, Rhea).

Table 1: BioNavi-NP Core Model Performance Metrics (Benchmark Dataset)

Metric	Value	Description
Top-1 Route Accuracy	78.3%	Percentage where highest-ranked predicted route matches a known native pathway.
Top-3 Route Recall	91.7%	Percentage where a known native pathway appears within the top 3 predicted routes.
Novel Route Validation	65.4%	Percentage of in silico novel routes deemed biochemically plausible by expert curation.
Average Route Discovery Time	4.2 seconds	Time to generate a full retrobiosynthetic tree for a complex NP (e.g., > 20 chiral centers).
Training Data Size	~285,000 reactions	Curated enzyme-catalyzed biosynthetic transformations.
Model Parameters	~145 million	Parameters in the primary Transformer-based predictor.

Table 2: Comparative Performance Against Other Tools

Tool / Method	Accuracy (Top-1)	Novel Route Proposal	Requires Rule Set
BioNavi-NP	78.3%	Yes (Generative)	No
RetroPathRL	62.1%	Limited	Yes
ASICS	55.8%	No	Yes
Manual Retrosynthesis	N/A	Yes, but slow	N/A

Application Notes: Protocol for Retrobiosynthetic Route Prediction

Protocol 1: Input Preparation and Target NP Specification

Objective: To correctly format and input a target natural product structure for BioNavi-NP analysis.

• Structure Definition: Prepare the target NP structure in a standard chemical format (SMILES or InChI is preferred). Ensure stereochemistry is explicitly defined.
• Constraint Specification (Optional):
  • Create a list of preferred or excluded precursor metabolites (e.g., malonyl-CoA, L-tyrosine).
  • Define a maximum number of retrosynthetic steps (default is 15).
  • Specify a biosynthetic family filter (e.g., "Type II Polyketide", "Non-Ribosomal Peptide").
• File Formatting: Save the SMILES string and constraints in a JSON configuration file as per the BioNavi-NP template.

Protocol 2: Executing a BioNavi-NP Prediction Run

Objective: To utilize the BioNavi-NP web API or command-line interface to generate retrobiosynthetic routes.

• API Access: Obtain and configure API credentials from the BioNavi-NP platform.
• Job Submission: Submit the prepared JSON configuration file via a POST request to the /predict endpoint. Capture the returned job_id.
• Result Monitoring: Poll the /status/{job_id} endpoint until the status returns "COMPLETE".
• Data Retrieval: Download the full result package from the /results/{job_id} endpoint. The package includes ranked routes, predicted intermediate structures, associated enzyme EC numbers, and confidence scores.

Protocol 3: Analysis and Validation of Predicted Routes

Objective: To critically evaluate and prioritize the routes generated by BioNavi-NP for experimental design.

• Route Prioritization: Open the main result file (routes_ranked.json). Examine the top 5 routes based on the composite confidence score.
• Intermediate Inspection: For each candidate route, review the predicted chemical structures of key intermediates. Use chemical feasibility checks (e.g., stability, strain).
• Enzyme Compatibility: Cross-reference the proposed enzyme EC numbers with the intended heterologous host's (e.g., Streomyces coelicolor, Saccharomyces cerevisiae) known or engineered metabolome.
• In Silico Pathway Assembly: Use companion tools (e.g., genome-scale metabolic models) to simulate flux through the proposed pathway and identify potential bottlenecks.

Diagram 1: BioNavi-NP Prediction Engine Workflow

Diagram 2: Example Retrobiosynthetic Tree Expansion

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Key Reagents & Materials for Validating BioNavi-NP Predictions

Item	Function/Application	Example Supplier/Part Number
pCAP Family Vectors	Modular, orthogonal expression vectors for polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS) gene assembly in actinomycetes.	Addgene Kit # 1000000133
Yeast Artificial Chromosome (YAC) Systems	For stable maintenance and expression of large biosynthetic gene clusters (BGCs) in S. cerevisiae.	NEB #E1000
Cell-Free Protein Synthesis System (PURE)	Reconstituted in vitro translation system for rapid expression and testing of individual pathway enzymes.	Sigma-Aldrich #PUREfrex 2.0
Deuterated / 13C-Labeled Precursor Metabolites (e.g., Malonyl-CoA, SAM, Amino Acids)	For isotopic feeding experiments to trace predicted precursor incorporation into final product.	Cambridge Isotope Labs (Various)
HR-LC-MS/MS System with UNIFI	High-resolution mass spectrometry for detecting predicted intermediate and final product structures.	Waters, Thermo Fisher Scientific
Codon-Optimized Gene Fragments (gBlocks)	Synthetic DNA fragments for heterologous expression of predicted enzyme sequences in the chosen host.	IDT, Twist Bioscience
Metabolite Standards Library	Commercial libraries of predicted intermediate compounds for LC-MS/MS comparison and validation.	IROA Technologies, Metabolon

This protocol details the navigation of the BioNavi-NP web interface, a computational platform for the de novo design of biosynthetic pathways for novel natural product-like compounds. The system integrates genomic and chemical logic to predict enzymatically feasible pathways. Mastery of its modules is essential for researchers in natural product discovery and synthetic biology to efficiently propose and prioritize pathways for experimental validation.

Table 1: Core Functional Modules of the BioNavi-NP Interface

Module Name	Primary Function	Key Input Panels	Typical Processing Time*
Target Compound Designer	Sketch or import a target molecule (product) structure.	Chemical sketcher, SMILES/InChI input, constraint definitions.	< 30 sec
Retrobiosynthesis Analyzer	Proposes potential precursor scaffolds and strategic bonds to cleave.	Retrosynthesis rules selector, complexity penalty sliders.	2-5 min
Enzyme Rule Navigator	Matches proposed retrosynthetic steps to known enzymatic transformations.	EC number filter, organism source selector, similarity threshold.	1-3 min
Pathway Assembler & Ranker	Assembles full pathways from matched rules and ranks by likelihood.	Weights for pathway score (length, similarity, host compatibility).	3-10 min
Visualization & Export Dashboard	Displays pathway maps, intermediate structures, and exports data.	Layout selector, export format options (SBML, PDF, SVG).	< 30 sec

*Processing times are estimates for standard queries on server loads typical of academic use.

Experimental Protocol: A Standard Workflow for Pathway Design

Protocol Title: De Novo Pathway Design for a Novel Polyketide Scaffold Using BioNavi-NP

Objective: To computationally design a biosynthetic pathway for a target polyketide-derived structure and generate a ranked list of candidate pathways for experimental refactoring.

Materials & Reagent Solutions (The Scientist's Toolkit):

Table 2: Essential Research Reagents & Computational Tools

Item	Function in Context
BioNavi-NP Web Server	Core platform for pathway prediction and design.
Chemical Drawing Software (e.g., ChemDraw)	To generate accurate SMILES strings of target compounds.
Model Host Genome (e.g., S. cerevisiae FASTA)	For host compatibility filtering of suggested enzymes.
BLAST+ Suite	For local, in-depth sequence analysis of proposed enzyme hits.
Pathway Visualization Software (e.g, iPath3)	For alternative rendering of complex pathway maps.

Procedure:

• Target Definition: Access the Target Compound Designer module. Use the integrated JSME molecular editor to sketch the desired natural product-like target structure. Alternatively, input a valid SMILES string. Set constraints (e.g., mandatory core scaffold) using the provided checkboxes and fields.
• Retrobiosynthesis Analysis: Navigate to the Retrobiosynthesis Analyzer. Submit the defined target. Adjust sliders to favor "common natural product building blocks" (e.g., malonyl-CoA, methylmalonyl-CoA) and set a maximum retrosynthetic depth of 5 steps. Execute the analysis.
• Enzyme Matching: In the Enzyme Rule Navigator, review the list of proposed retrosynthetic disconnections. For each step, initiate an enzyme search. Set the similarity threshold to 0.6 (Tanimoto coefficient on reaction rule fingerprint). Filter enzyme sources to "Actinobacteria" and "Fungi." Run the matching algorithm.
• Pathway Assembly & Scoring: Proceed to the Pathway Assembler & Ranker. The system will auto-assemble full pathways from successful matches. Set ranking weights as follows: Pathway Length (0.4), Enzyme Similarity Score (0.3), Host Compatibility Score (0.3). Execute the assembly and ranking process.
• Visualization & Export: Open the Visualization & Export Dashboard. Select the top-ranked pathway. Examine the interactive map showing substrates, intermediates, enzymes, and cofactors. Use the "Export" panel to download the pathway as an SBML file for constraint-based modeling and a high-resolution SVG for publication.
• Validation Planning (Downstream Experimental): The proposed gene sequences (associated with the top pathways) must be synthesized and cloned into a suitable expression host (e.g., S. cerevisiae). Cultivate the engineered host in appropriate media (e.g., YPD with necessary auxotrophic supplements) and analyze metabolite extracts via LC-MS for the presence of the target compound or key intermediates.

Visualization: BioNavi-NP Workflow Logic

Diagram Title: BioNavi-NP Core Computational Workflow

Visualization: Example Retrobiosynthetic Pathway Fragment

Diagram Title: Retrobiosynthetic Analysis to Building Blocks

Foundational Concepts & Quantitative Data

Key Classes of Natural Products

The biosynthetic origins define major NP classes, with characteristic scaffold complexities.

Table 1: Core Natural Product Classes and Biosynthetic Origins

Natural Product Class	Primary Biosynthetic Building Blocks	Representative Scaffold Complexity (Avg. Carbon Atoms)	Key Enzymatic Machinery
Polyketides	Acetyl-CoA, Malonyl-CoA	15 - 40	Polyketide Synthases (PKS)
Non-Ribosomal Peptides	Proteinogenic & Non-Proteinogenic Amino Acids	4 - 20 residues	Non-Ribosomal Peptide Synthetases (NRPS)
Terpenoids	Isopentenyl pyrophosphate (IPP), Dimethylallyl pyrophosphate (DMAPP)	10 - 30 (C10-C30)	Terpene Synthases/Cyclases
Alkaloids	Varied (often amino acid-derived: lysine, tyrosine, tryptophan)	10 - 30	Oxidoreductases, Methyltransferases
Flavonoids	Phenylalanine, Malonyl-CoA	15 (C6-C3-C6)	Type III PKS, Glycosyltransferases

Critical Enzymological Parameters

Understanding enzyme kinetics is vital for pathway design and optimization.

Table 2: Essential Kinetic Parameters for Pathway Enzymes

Parameter	Symbol	Typical Range for NP Biosynthetic Enzymes	Significance in Pathway Design
Turnover Number	kcat	0.01 - 100 s⁻¹	Determines catalytic efficiency and required enzyme concentration.
Michaelis Constant	KM	1 µM - 10 mM	Affinity for substrate; informs substrate dosing in heterologous hosts.
Catalytic Efficiency	kcat/KM	10² - 10⁷ M⁻¹s⁻¹	Overall efficiency; key for identifying rate-limiting steps.
Enzyme Commission Number	EC	1.-.-.- (Oxidoreductases) to 6.-.-.- (Ligases)	Standardized classification of function.
Optimal pH Range	-	6.0 - 8.5 (for most cytosolic enzymes)	Critical for host cytosol compatibility.
Optimal Temperature	-	25°C - 37°C (for mesophilic hosts)	Informs host selection and fermentation conditions.

Application Notes for BioNavi-NP Framework

Note 001: Identifying Rate-Limiting Steps in Putative Pathways

• Context: When designing a heterologous expression pathway in S. cerevisiae or E. coli using BioNavi-NP's retrosynthetic predictions, flux bottlenecks are common.
• Procedure: Clone and individually express each predicted biosynthetic enzyme with a His-tag. Purify via nickel-affinity chromatography. Assay each reaction step in vitro using predicted substrates. Measure initial velocities across a range of substrate concentrations.
• Data Integration: Calculate k_cat and K_M for each enzyme. Input these kinetic parameters into BioNavi-NP's Flux Simulation Module. The module will highlight steps with low k_cat/K_M as potential bottlenecks.
• Actionable Output: Prioritize these enzymes for codon optimization, promoter engineering, or scaffold protein fusion to enhance soluble expression and activity.

Note 002: Validating Novel Enzyme Function from Genome Mining

• Context: BioNavi-NP's Enzyme Analogous Cluster tool may propose a novel enzyme (e.g., a putative cyclase) for a key scaffold-forming step.
• Procedure: Perform site-directed mutagenesis on conserved catalytic residues (e.g., an aspartic acid in a terpene cyclase DxDTT motif). Express and purify both wild-type and mutant proteins. Conduct in vitro assays with the established substrate (e.g., geranylgeranyl pyrophosphate). Analyze products using LC-MS/MS.
• Expected Outcome: Wild-type enzyme produces the predicted cyclized terpene (verified by mass and fragmentation pattern). Mutant enzyme shows >95% loss of activity, confirming the proposed catalytic mechanism.

Experimental Protocols

Protocol: Kinetic Characterization of a Recombinant Polyketide Synthase (PKS) Module

Objective: Determine K_M for malonyl-CoA and k_cat for a single PKS extension module.

Materials:

• Purified Acyl Carrier Protein (ACP)-tagged PKS module.
• [¹⁴C]-Malonyl-CoA (or unlabeled for HPLC-MS detection).
• S-Adenosyl Methionine (SAM).
• Appropriate acyl starter unit (e.g., [²H]-acetyl-ACP).
• Assay buffer (100 mM phosphate, pH 7.2, 2 mM TCEP).
• Stop solution (10% trichloroacetic acid).
• Scintillation counter or LC-MS.

Methodology:

• Reaction Setup: In a 50 µL volume, combine assay buffer, 100 µM acyl starter unit, 200 µM SAM, and 5-50 nM purified PKS module.
• Variable Substrate: Add [¹⁴C]-Malonyl-CoA across a concentration series (e.g., 1, 2, 5, 10, 20, 50, 100 µM). Perform in triplicate.
• Incubation: Initiate reaction by adding enzyme. Incubate at 30°C for 5 minutes (ensure linear reaction progress).
• Termination: Quench with 10 µL stop solution. Place on ice.
• Analysis: For radiometric assays, precipitate protein on ice, centrifuge, and quantify radioactivity in the supernatant (representing hydrolyzed or unincorporated [¹⁴C]-Malonyl-CoA). Alternatively, analyze product formation directly by LC-MS.
• Calculation: Plot initial velocity (v₀) against [Malonyl-CoA]. Fit data to the Michaelis-Menten equation (v₀ = (V_max[S])/(K_M + [S])) using nonlinear regression software. V_max = k_cat[E]_total.

Protocol: Heterologous Reconstitution of a Predicted NRPS Pathway inE. coli

Objective: Validate the function of a BioNavi-NP-predicted non-ribosomal peptide pathway.

Materials:

• E. coli BL21(DE3) expression strain.
• pETDuet or pCDFDuet vectors harboring NRPS genes (codon-optimized).
• LB and M9 minimal medium with appropriate antibiotics.
• Inducer: Isopropyl β-d-1-thiogalactopyranoside (IPTG).
• Substrate amino acids (including predicted non-proteinogenic ones).
• Extraction solvents: ethyl acetate, methanol.
• Analytical LC-MS system.

Methodology:

• Strain Construction: Co-transform E. coli with plasmids expressing all NRPS genes (typically 2-4 plasmids). Include a phosphopantetheinyl transferase (e.g., Sfp) for ACP activation.
• Small-Scale Expression: Inoculate 5 mL cultures. Grow at 37°C to OD₆₀₀ ~0.6. Induce with 0.1-0.5 mM IPTG. Add predicted substrate amino acids (1 mM each). Shift to 18°C, incubate for 16-20 hours.
• Metabolite Extraction: Pellet cells. Resuspend in 500 µL methanol, vortex vigorously. Add 500 µL ethyl acetate, sonicate for 10 minutes. Centrifuge. Collect organic layer. Dry under nitrogen gas.
• Analysis: Reconstitute in 100 µL methanol. Analyze by LC-MS. Compare mass spectra and retention times to BioNavi-NP's in silico metabolite prediction database. Perform MS/MS to confirm peptide sequence via fragmentation.

Visualizations

Diagram 1: BioNavi-NP Experimental Validation Workflow

Diagram 2: Core Pathway to Aromatic Amino Acids & Alkaloids

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Reagents for NP Pathway Research

Reagent / Material	Function & Application in NP Research	Key Considerations
S-Adenosyl Methionine (SAM)	Universal methyl donor for O-, N-, C- methylation reactions catalyzed by methyltransferases (MTs).	Labile; use fresh, stabilized solutions. Critical for alkaloid and polyketide tailoring.
Malonyl-CoA / Methylmalonyl-CoA	Extender units for Polyketide Synthase (PKS) chain elongation.	Determine PKS product structure. Methylmalonyl-CoA introduces methyl branch points.
Isopentenyl Pyrophosphate (IPP) / DMAPP	Building blocks for terpenoid biosynthesis via the MEP or MVA pathways.	Feed in permeabilized cells to boost terpene titers. Essential for in vitro terpene synthase assays.
Phosphopantetheinyl Transferase (e.g., Sfp, Svp)	Activates carrier proteins (ACP/PCP) in PKS/NRPS by adding phosphopantetheine arm.	Co-express in heterologous hosts for pathway functionality. Required for in vitro reconstitution.
Coenzyme A (CoA) Derivatives (e.g., Hexanoyl-CoA, Benzoyl-CoA)	Starter units for PKS systems.	Define the beginning of the polyketide chain. Can be fed to cultures or used in vitro.
NADPH / NADH	Reducing equivalents for ketoreduction (KR) domains in PKS and redox enzymes.	Concentration must be maintained in vitro; often requires a regeneration system for long assays.
Protease Inhibitor Cocktail	Protects recombinant NP enzymes during purification from host proteases.	Essential for obtaining full-length, active proteins, especially large PKS/NRPS enzymes.
Detergents (n-Dodecyl β-D-maltoside, CHAPS)	Solubilize membrane-associated enzymes (e.g., certain cytochrome P450s).	Critical for characterizing tailoring reactions like hydroxylations. Optimize type and concentration.

Abstract: Within the BioNavi-NP framework for natural product (NP) pathway design, the precise selection and preparation of a target molecule are foundational. This application note details a systematic protocol for target molecule evaluation, experimental preparation, and subsequent analysis, ensuring a robust starting point for downstream pathway elucidation and engineering.

Target Selection Criteria and Prioritization

Selecting the appropriate natural product molecule is critical. The following quantitative criteria facilitate objective prioritization for pathway design research.

Table 1: Target Molecule Prioritization Matrix

Criterion	Weight (%)	Scoring (1-5)	Description & Metrics
Bioactivity Potency	30	EC50/IC50 < 100 nM (5) < 1 µM (4) < 10 µM (3)	Based on primary assay (e.g., antimicrobial, anticancer). Lower is better.
Structural Complexity	25	Scaffold Complexity Score (1=Simple, 5=High)	Assess rings, stereocenters, and functional groups. Moderate complexity (3) is often ideal for pathway discovery.
Uniqueness of Scaffold	20	Known Biosynthetic Gene Clusters (BGCs): 0 (5), 1-2 (4), >3 (2)	Novel scaffolds offer higher research impact. Query MIBiG database.
Source Organism Viability	15	Cultivation/Genetic Tools: Established (5), Possible (3), Difficult (1)	Impacts feasibility of genetic and fermentation studies.
Predicted Solubility (LogP)	10	-1 to 3 (5), 3 to 5 (3), >5 (1)	Critical for in vitro assays. Optimal LogP for drug-likeness ~1-3.

Protocol: Preparation of a Target NP for Initial Analysis

This protocol outlines the steps for isolation, purification, and validation of a target NP from a microbial source (e.g., actinomycete fermentation) prior to detailed pathway investigation.

Materials:

• Source organism biomass (e.g., lyophilized mycelia from 1L culture).
• Extraction solvents: Methanol, Ethyl Acetate, Dichloromethane (HPLC grade).
• Solid-phase extraction (SPE) cartridges (C18, 500 mg/6 mL).
• Normal-phase and Reversed-phase silica gel for column chromatography.
• Analytical & Preparative HPLC system with UV/VIS and/or MS detection.
• NMR solvents (Deuterated Chloroform, Methanol-d4).
• Bioactivity assay reagents (e.g., microbroth dilution media, cell lines).

Procedure:

Step 1: Crude Extract Preparation

• Homogenize 10 g of lyophilized biomass in 100 mL of 1:1 Methanol:Dichloromethane using a sonicator (3 x 10 min pulses, on ice).
• Filter the homogenate through qualitative filter paper and concentrate the supernatant in vacuo at 40°C.
• Resuspend the dried crude extract in 10 mL of 10% methanol in water for subsequent SPE.

Step 2: Fractionation & Activity-Guided Isolation

• Pre-condition a C18 SPE cartridge with 10 mL methanol followed by 10 mL Milli-Q water.
• Load the resuspended crude extract. Elute stepwise with 20 mL each of 20%, 40%, 60%, 80%, and 100% methanol in water. Collect five fractions.
• Concentrate each fraction and screen for bioactivity using a primary assay (e.g., antibacterial disk diffusion).
• Subject the active fraction(s) to preparative reversed-phase HPLC (Column: C18, 5 µm, 10 x 250 mm; Gradient: 30-95% Acetonitrile in water over 30 min; Flow: 4 mL/min; UV detection at 210, 254, 280 nm).
• Collect major UV peaks, concentrate, and re-test for activity to identify the target-containing fraction.

Step 3: Purity Assessment & Structural Validation

• Analyze the purified compound by analytical HPLC (C18, 5 µm, 4.6 x 150 mm; Gradient: 40-100% MeCN in water + 0.1% Formic acid over 20 min). Target purity should be ≥95% (by UV peak area at 254 nm).
• Obtain High-Resolution Mass Spectrometry (HR-MS) data to confirm molecular formula.
• Dissolve 1-2 mg of pure compound in 0.6 mL of deuterated solvent. Acquire 1D (1H, 13C) and key 2D (COSY, HSQC, HMBC) NMR spectra for full structural confirmation against literature data.

Workflow & Pathway Context

Diagram 1: BioNavi-NP Target Selection & Prep Workflow

Diagram 2: NP Analysis in Biosynthesis Context

The Scientist's Toolkit: Key Research Reagent Solutions

Table 2: Essential Materials for NP Target Preparation & Analysis

Category/Reagent	Specific Example/Product	Function in Protocol
Extraction Solvents	HPLC-grade Methanol, Dichloromethane, Ethyl Acetate	Efficient, low-interference extraction of NP from biological matrix.
Solid-Phase Extraction	Bond Elut C18 Cartridges (Agilent) or equivalent	Rapid desalting and pre-fractionation of crude extracts.
Chromatography Media	Sephadex LH-20, Silica gel 60 (40-63 µm)	Size-exclusion and normal-phase purification steps.
HPLC Columns	Phenomenex Luna C18(2) (Analytical & Prep scale)	High-resolution separation and purity analysis of NPs.
Deuterated NMR Solvents	DMSO-d6, CDCl3, Methanol-d4 (e.g., from Cambridge Isotopes)	Solvent for NMR structural elucidation without H interference.
Mass Spec Standards	ESI Tuning Mix (Agilent), Leucine Enkephalin (Waters)	Calibration and accurate mass measurement in HR-MS.
Bioassay Reagents	Mueller Hinton Broth II, Resazurin sodium salt	For antimicrobial activity-guided isolation (MIC determination).
Genetic Tools	QIAprep Spin Miniprep Kit (Qiagen)	Isolation of high-quality plasmid DNA for subsequent BGC cloning.

Step-by-Step Workflow: Designing and Exporting Novel Natural Product Pathways

Within the BioNavi-NP framework for de novo natural product pathway design, the precise computational representation of the target molecule is the critical first step. Accurate input dictates the success of subsequent retrobiosynthetic disconnection and enzyme prediction modules. This protocol details best practices for three primary input methods: SMILES notation, structure file import, and manual molecular drawing, contextualized for natural product research.

Input Methods: Protocols and Comparative Analysis

SMILES (Simplified Molecular Input Line Entry System)

Protocol: Inputting and Validating SMILES in BioNavi-NP

• Source SMILES: Obtain a canonical isomeric SMILES string from reputable databases (e.g., PubChem, NPASS, COCONUT).
• Pre-processing: Use Open Babel (v3.1.1) or RDKit (2023.09.5) in a pre-processing script to standardize the SMILES (remove salts, neutralize charges, generate tautomer-unified representation).

• BioNavi-NP Input: Paste the cleaned SMILES directly into the "Target SMILES" field on the BioNavi-NP web interface.
• Validation: The system automatically computes molecular descriptors and performs a sanity check. Confirm the 2D structure rendered matches the intended target.

Structure File Import

Protocol: Preparing and Uploading Molecular Structure Files

• File Format Selection: Preferred formats, in order of fidelity for NP complexity: .mol2 (preserves partial charges) > .sdf/.sd (multi-structure) > .mol > .pdb.
• File Preparation:
  • Ensure file is not corrupted and contains explicit hydrogen atoms as required for biosynthesis planning.
  • For .sdf files, verify the target molecule is the first entry if multiple are present.
  • Use molecular modeling software (e.g., Avogadro2, ChemDraw) to minimize geometry using the MMFF94s force field before export.
• Upload to BioNavi-NP: Use the "Upload File" button. The system supports files ≤ 50 MB.
• Post-upload Check: Verify stereochemistry is correctly interpreted by inspecting the 3D viewer and the listed chiral centers.

Manual Drawing Interface

Protocol: Effective Manual Drawing in BioNavi-NP’s ChemDraw-like Editor

• Template Setup: Begin with a relevant macrocycle or scaffold template from the BioNavi-NP sidebar library (e.g., tetrahydropyran, β-lactam).
• Drawing: Use the chain tool for the carbon backbone. Employ the stereochemistry tool to define absolute configuration (R/S) at chiral centers immediately after creating them.
• Annotation: Use the "Annotations" layer to mark proposed retrobiosynthetic disconnection sites or key functional groups.
• Validation: Run the "Structure Checker" tool within the editor to identify valency errors or unspecified stereochemistry before proceeding to analysis.

Input Method Comparison

Table 1: Comparative Analysis of Target Input Methods for BioNavi-NP

Method	Optimal Use Case	Key Advantage	Data Fidelity Risk	Recommended Pre-processing
SMILES String	Known, simple to moderately complex NPs; high-throughput screening.	Speed, scriptability, easy sharing.	Medium (Tautomerism, stereochemistry errors).	Canonicalization, tautomer standardization.
Structure File (.mol2, .sdf)	Complex NPs with defined 3D conformation, metalloenzyme products.	High fidelity, preserves 3D coordinates and charges.	Low (if file is well-prepared).	Add explicit H's, geometry minimization.
Manual Drawing	Novel or hypothetical NP structures not found in databases.	Creative flexibility, direct annotation.	High (User error in stereochemistry).	Use built-in structure checker tool.

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 2: Key Reagent Solutions for Validating Computational NP Structures

Item	Function in Experimental Validation
Deuterated Solvents (CDCl₃, DMSO-d₆)	Essential for NMR spectroscopy to confirm the structure and purity of a synthesized NP target or intermediate.
Silica Gel (40-63 µm, 60 Å pore)	For flash column chromatography purification of NP intermediates, critical for obtaining compounds for biological testing.
LC-MS Grade Acetonitrile & Methanol	For high-resolution LC-MS analysis to verify the molecular weight and purity of the target NP.
Reverse-Phase C18 Chromatography Columns	For analytical and preparative HPLC purification of natural products and their biosynthetic intermediates.
Chiral Derivatization Agents (e.g., Mosher's acid chloride)	To determine the absolute configuration of chiral centers in a novel NP, confirming computational predictions.

Visualization of the BioNavi-NP Input and Validation Workflow

Diagram 1: Target Input and Validation Workflow in BioNavi-NP (82 chars)

Advanced Protocol: Integrating Input with Preliminary Retrobiosynthetic Planning

Protocol: From Structure to Initial Disconnection Strategy

• Input & Validation: Follow Protocol 2.1, 2.2, or 2.3 to input the target molecule (e.g., Artemisinin, SMILES: CC1CCC2C(C(=O)OC3C24C1CCC5(C3CCC(O5)(OO)CO4)C)C).
• Functional Group Tagging: Within BioNavi-NP, use the "Annotation Tool" to manually label key NP functional groups (e.g., "endoperoxide", "lactone").
• Rule-based Pre-screening: Initiate the "Rule Scan" module which cross-references the input structure against a library of biogenic reaction rules (e.g., terpene cyclization, polyketide elongation).
• Generate Preliminary Disconnection Map: Execute the "Generate Precursors" command with parameters set to Max Steps=3, Complexity Threshold=High. This produces a precursor tree.
• Export & Iterate: Export the top 5 precursor SMILES as an .sdf file. Re-input promising biosynthetically plausible precursors for a second-round analysis, refining the pathway.

Diagram 2: From Target Input to Preliminary Disconnection (74 chars)

1. Introduction Within the BioNavi-NP framework for de novo design of natural product biosynthetic pathways, configuring search parameters is critical for navigating the vast combinatorial space of enzymatic reactions and chemical structures. This document provides application notes and protocols for optimizing the balance between computational depth (exhaustiveness) and result relevance (biological plausibility and novelty) to accelerate natural product-based drug discovery.

2. Core Search Parameters & Quantitative Benchmarks The primary search parameters in BioNavi-NP govern the algorithm's traversal of the biosynthetic network. The table below summarizes key parameters, their functions, and empirically derived optimal ranges for general natural product scaffold exploration.

Table 1: Core BioNavi-NP Search Parameters and Recommended Configurations

Parameter	Function	Default	Recommended Range	Impact on Depth/Relevance
max_path_length	Max enzymatic steps from start core.	5	4 - 8	Depth , Relevance (longer paths may be less plausible)
beam_width	Number of top pathways retained per iteration.	50	20 - 100	Depth , Computational Cost
similarity_threshold	Min Tanimoto coef. for substrate-enzyme pairing.	0.4	0.35 - 0.5	Relevance (higher = more precise), Depth
diversity_penalty	Penalty for highly similar pathways in beam.	0.1	0.05 - 0.2	Relevance (encourages novelty)
retro_score_cutoff	Min score for retrobiosynthesis step expansion.	0.3	0.25 - 0.35	Depth (lower = more steps explored)

3. Experimental Protocol: Iterative Parameter Optimization for Target-Class Discovery Objective: To systematically identify novel terpenoid-like scaffolds with potential anti-inflammatory activity. Workflow: The protocol follows an iterative design-search-validate cycle.

Protocol 3.1: Initial Broad Search

• Input: Starting core = Geranyl diphosphate (GPP).
• Parameter Set: max_path_length=6, beam_width=100, similarity_threshold=0.35, diversity_penalty=0.05, retro_score_cutoff=0.25.
• Execute Search: Run BioNavi-NP's explore_pathways module.
• Output Analysis: Generate a library of 500-1000 predicted terminal structures. Cluster by molecular fingerprint (ECFP4).

Protocol 3.2: Focused Filtering & Relevance Scoring

• Filter 1 (Structural): Filter cluster centroids for drug-likeness (Lipinski's Rule of Five, QED > 0.4).
• Filter 2 (Bioactivity): Use a pre-trained ML model (e.g., Random Forest on ChEMBL data) to predict pKi for TNF-α inhibition. Retain predictions > 6.0.
• Filter 3 (Enzymatic): Re-score retained pathways using similarity_threshold=0.45 to ensure high enzymatic plausibility.
• Output: A prioritized list of 20-50 high-confidence pathways.

Protocol 3.3: In Silico Validation Round

• Docking: Perform molecular docking of top 10 final products against TNF-α crystal structure (PDB: 2AZ5).
• Pathway Ranking: Re-rank pathways based on composite score: Docking score (40%), enzymatic plausibility (40%), and structural novelty (20%).
• Final Output: Select top 3-5 pathways for in vitro experimental refactoring.

Title: BioNavi-NP Iterative Optimization Workflow

4. Pathway Logic & Scoring Visualization The search algorithm's decision logic integrates multiple scoring functions to evaluate each potential enzymatic step.

Title: BioNavi-NP Step Scoring Logic

5. The Scientist's Toolkit: Research Reagent Solutions Table 2: Essential Materials for BioNavi-NP-Guided Pathway Refactoring

Reagent/Material	Function in Validation	Example Product/Catalog
Cloning Kit (Gibson Assembly)	Seamless assembly of multiple BioBricks for pathway refactoring into a microbial host.	NEB Gibson Assembly HiFi Master Mix
Golden Gate Assembly Kit	Modular, standardized assembly of transcriptional units encoding pathway enzymes.	BsaI-HF v2 Golden Gate Assembly Kit
Heterologous Host Strain	Engineered chassis for expressing plant or bacterial NP pathways (e.g., S. cerevisiae, E. coli).	S. cerevisiae YPH499 (MATA/α kit)
Substrate Standards	Analytical standards for LC-MS/MS validation of predicted intermediate compounds.	Geranyl Diphosphate (GPP) Sodium Salt
LC-MS/MS System	High-resolution mass spectrometry for detecting and quantifying pathway intermediates/products.	Agilent 6470 Triple Quadrupole LC/MS
Cytotoxicity Assay Kit	Initial screening of novel compound bioactivity and therapeutic index.	Promega CellTiter-Glo Luminescent Kit

Within the BioNavi-NP platform for de novo natural product pathway design, the Interactive Reaction Network Graph is the central visual tool for analyzing predicted biosynthetic routes. It maps the enzymatic transformation of starting substrates into complex natural product scaffolds. This Application Note details its interpretation and provides protocols for experimental validation of in silico predicted pathways.

Key Graph Elements & Quantitative Metrics

The network graph visualizes a search space generated by applying retrosynthetic or forward-synthetic rules. Key performance metrics from a typical BioNavi-NP pathway search are summarized below.

Table 1: Quantitative Summary of a Standard BioNavi-NP Pathway Search Output

Metric	Typical Range	Description
Total Generated Nodes	1,000 - 50,000	Unique molecular structures in the network.
Total Generated Edges	1,200 - 70,000	Candidate enzymatic reactions connecting nodes.
Top-ranked Pathways	1 - 50	Shortlisted pathways post-scoring.
Average Pathway Length	5 - 15 steps	Number of enzymatic reactions from start to target.
Computational Time	2 - 48 hours	Varies with target complexity and search depth.
Route Score (Top Pathway)	0.7 - 0.95	Composite score (1.0 max) based on enzyme compatibility, thermodynamics, and similarity.

Protocol: Validating a Predicted Pathway

Protocol 1:In VitroReconstitution of a Predicted Enzymatic Cascade

This protocol outlines the experimental validation of a computationally predicted pathway involving four enzymes (E1-E4).

Materials & Reagents

• Cloned Enzyme Genes: Codon-optimized genes for E1-E4 in expression vectors (e.g., pET series).
• Expression Host: E. coli BL21(DE3) competent cells.
• Culture Media: LB broth and agar plates with appropriate antibiotics.
• Induction Reagent: 0.1 - 1.0 mM Isopropyl β-d-1-thiogalactopyranoside (IPTG).
• Substrate: Purified starting compound (precursor) as identified by the network graph.
• Cofactors: ATP, NADPH, SAM, etc., as specified by the enzyme rules in the graph.
• Analytical Standards: Authentic standards of predicted intermediate and final product for HPLC/MS comparison.
• Analytical Tools: HPLC-DAD, LC-MS (HRMS preferred), NMR for structural confirmation.

Procedure

• Gene Expression & Protein Purification:
  • Individually transform expression plasmids into E. coli BL21(DE3). Grow cultures (LB + antibiotic) at 37°C to OD600 ~0.6.
  • Induce protein expression with IPTG. Incubate at 16-18°C for 16-20 hours.
  • Pellet cells by centrifugation (4,000 x g, 20 min, 4°C). Lyse via sonication.
  • Purify His-tagged enzymes using Ni-NTA affinity chromatography. Dialyze into storage buffer (e.g., 50 mM Tris-HCl, pH 7.5, 100 mM NaCl, 10% glycerol). Determine protein concentration.

• Single-step Enzyme Activity Assay:

  • For each predicted reaction (edge on the graph), set up a 100 µL assay containing: 50 mM appropriate buffer, substrate (50-200 µM), required cofactors (1-2 mM), and purified enzyme (5-10 µM).
  • Incubate at 30°C for 1 hour. Quench reaction by adding 100 µL of cold methanol.
  • Centrifuge (15,000 x g, 10 min) and analyze supernatant by HPLC-MS.
  • Critical Step: Compare retention time and mass to the in silico predicted intermediate node. This validates each individual graph edge.

• Multi-enzyme Cascade Reaction:

  • Set up a combined assay containing all purified enzymes (E1-E4, 5-10 µM each), the initial substrate, and all required cofactors in a single pot.
  • Incubate at 30°C for 4-16 hours, monitoring time-course by HPLC-MS.
  • The successful production of the target natural product, confirmed by HRMS and NMR, validates the complete pathway as depicted in the BioNavi-NP graph.

The Scientist's Toolkit: Key Research Reagent Solutions

Item	Function in Validation
Codon-Optimized Gene Clones	Ensures high-level, soluble expression of pathway enzymes in the heterologous host.
Ni-NTA Agarose Resin	Standard for rapid immobilised-metal affinity chromatography (IMAC) purification of His-tagged enzymes.
Adenosine 5'-triphosphate (ATP)	Essential cofactor for kinases, ligases, and other energy-requiring enzymatic transformations.
Nicotinamide adenine dinucleotide phosphate (NADPH)	Key reductant cofactor for dehydrogenases, reductases, and P450 enzymes.
S-Adenosyl methionine (SAM)	Methyl group donor for methyltransferase-catalyzed reactions.
Deuterated Solvents (e.g., DMSO-d6, CD3OD)	Essential for NMR spectroscopy to confirm chemical structures of intermediates and final product.

Diagram: BioNavi-NP Network Graph Interpretation Workflow

Title: Workflow for Using and Validating BioNavi-NP Network Graphs

Diagram: Example Pathway from Network Graph

Title: Example of a Four-Step Pathway Extracted from Graph

1. Introduction Within the BioNavi-NP tutorial framework for natural product (NP) pathway design, a critical step is evaluating the feasibility of computationally generated biosynthetic pathways. This involves rigorous analysis of each proposed enzymatic transformation's plausibility and the chemical stability of predicted intermediates. This document provides detailed application notes and protocols for conducting this essential feasibility assessment.

2. Core Concepts & Data Presentation

2.1 Biochemical Transformation Rule Application BioNavi-NP and similar tools apply biochemical reaction rules (e.g., from databases like BNICE or RetroRules) to decompose a target NP into potential precursors. Feasibility scoring for each rule application considers multiple factors.

Table 1: Quantitative Metrics for Rule Application Feasibility Analysis

Metric Category	Specific Parameter	Typical Threshold/Score Range	Data Source
Enzymatic Prevalence	EC Number Frequency in MIBiG / BRENDA	>5 documented occurrences (High Confidence)	MIBiG Database, BRENDA
Substrate Specificity	Tanimoto Similarity to Known Native Substrate (ECFP4 fingerprints)	≥0.45 (Acceptable)	PubChem, ChEMBL
Reaction Thermodynamics	Estimated ΔG' of Reaction (kJ/mol)	≤ +10 (Favorable/Neutral)	eQuilibrator API, group contribution methods
Genomic Context	Co-occurrence of Enzyme Genes in BGCs (Jaccard Index)	≥0.3 (Suggestive of partnership)	antiSMASHdb, STRING

2.2 Intermediate Compound Stability Assessment Predicted pathway intermediates must be chemically stable under physiological conditions to be viable.

Table 2: Key Stability Descriptors for Pathway Intermediates

Descriptor	Calculation Method	Stability Indicator	Tool/Software
Instability Score	Based on presence of labile functional groups (e.g., anhydrides, β-lactones)	Score < 40 (Stable)	PROTOX III, RDKit
Reactive Functional Groups	SMARTS pattern matching for aldehydes, epoxides, Michael acceptors, etc.	Count ≤ 2 (Low reactivity preferred)	RDKit, KNIME
pKa (Predicted)	For ionizable groups affecting solubility/reactivity	Physiological pH stability considered	ChemAxon pKa Plugin, MarvinSuite
Maximum Plausible Lifetime	QSAR model prediction (hours)	> 1 hour at pH 7.4, 25°C	SwissADME, Chemicalize

3. Experimental Protocols

3.1 Protocol: In Silico Feasibility Audit for a Proposed Pathway Objective: To systematically evaluate the enzymatic steps and intermediates of a BioNavi-NP-generated pathway. Materials: BioNavi-NP output (SMILES strings of intermediates, proposed EC numbers), access to relevant databases (MIBiG, BRENDA, PubChem), cheminformatics software (RDKit, Open Babel). Procedure: 1. Pathway Parsing: Extract the list of proposed enzymatic transformations and the SMILES of each intermediate. 2. Rule Validation: For each EC number, query the MIBiG database for known NP pathways containing this enzyme. Record frequency and phylogenetic origin. 3. Substrate Similarity Check: a. For each intermediate (substrate), query PubChem for known substrates of the proposed EC number via the PubChem PUG REST API. b. Compute the maximum Tanimoto similarity (using ECFP4 fingerprints) between the intermediate and known substrates using RDKit. c. Flag any step with similarity < 0.3 for manual inspection. 4. Stability Profiling: a. For each intermediate, use RDKit to perform SMARTS substructure searches for 15+ known reactive/unstable motifs (e.g., "[$(C(=O)OC(=O))]", "[$(C1OC1=O)]" for β-lactones). b. Input each intermediate's SMILES into the SwissADME web tool (http://www.swissadme.ch/) to obtain the BOILED-Egg plot and synthetic accessibility score. c. Calculate instability index using the ProtFP server if needed. 5. Consolidated Scoring: Generate a composite feasibility score per step (e.g., 0-1 scale) weighting rule confidence (40%), substrate similarity (30%), and intermediate stability (30%).

3.2 Protocol: In Vitro Stability Assay for a High-Risk Intermediate Objective: To experimentally determine the half-life of a chemically suspect intermediate predicted in silico. Materials: Synthesized or purchased intermediate compound, phosphate-buffered saline (PBS, pH 7.4), LC-MS system (e.g., Agilent 1260 Infinity II/6125B), controlled temperature incubator. Procedure: 1. Solution Preparation: Prepare a 1 mM stock solution of the intermediate in DMSO. Dilute to 10 µM in pre-warmed (37°C) PBS buffer in a low-binding microcentrifuge tube. This is T=0. 2. Incubation and Sampling: Incubate the solution at 37°C. At defined time points (e.g., 0, 5, 15, 30, 60, 120, 240 min), withdraw a 100 µL aliquot and immediately mix with 100 µL of ice-cold acetonitrile to quench any reaction. 3. Analysis: a. Centrifuge quenched samples at 15,000 x g for 10 min to precipitate proteins/salts. b. Transfer supernatant to an LC-MS vial. c. Analyze via LC-MS using a C18 column and a gradient of water/acetonitrile + 0.1% formic acid. d. Quantify the peak area of the parent intermediate (via extracted ion chromatogram for its [M+H]+ ion). 4. Data Processing: Plot Ln(peak area) versus time. The negative slope of the linear fit is the observed degradation rate constant (k). Calculate half-life: t_1/2 = Ln(2)/k.

4. Visualizations

Title: BioNavi-NP Pathway Feasibility Evaluation Workflow

Title: Pathway Step with a Critical Unstable Intermediate

5. The Scientist's Toolkit

Table 3: Key Research Reagent Solutions & Materials

Item / Solution	Function / Purpose	Example / Notes
Phosphate Buffered Saline (PBS), pH 7.4	Physiological simulation buffer for in vitro stability assays.	Gibco DPBS, sterile-filtered.
LC-MS Grade Solvents	High-purity solvents for LC-MS analysis to minimize background interference.	Acetonitrile and Water with 0.1% Formic Acid (v/v).
Reactive Group SMARTS Libraries	Digital libraries of chemical motifs for in silico instability screening.	RDKit-compatible SMARTS patterns for epoxides, Michael acceptors, etc.
Enzyme Commission (EC) Database	Reference for validating the existence and documented reactions of proposed enzymes.	BRENDA, ExplorEnz.
MIBiG Database Access	Repository of known Biosynthetic Gene Clusters (BGCs) to check for enzyme co-occurrence.	Essential for genomic context validation.
Chemical Synthesis Kit	For custom synthesis of predicted, commercially unavailable intermediates.	May include solid-phase synthesizers, catalysts, and protected building blocks.

In the context of BioNavi-NP-a computational platform for predicting and designing natural product biosynthetic pathways-the final, critical step is the actionable export of results. The transition from in silico prediction to in vitro/vivo experimental validation hinges on robust, standardized export protocols. This note details the essential formats for saving data and images from BioNavi-NP and provides structured protocols for downstream experimental planning.

Data exports from BioNavi-NP fall into three primary categories: pathway data, chemical structures, and analysis reports. The choice of format dictates compatibility with downstream software and databases.

Table 1: Summary of Primary Export Formats from BioNavi-NP

Data Type	Recommended Formats	Primary Downstream Use	Key Advantages	Limitations
Pathway Architecture	SBML L3V1, JSON, CSV	Pathway visualization (CellDesigner, Escher), kinetic modeling, sharing.	SBML: Standardized, machine-readable. JSON: Retains hierarchical data.	SBML may require tuning for NP-specific reactions.
Chemical Structures	SDF/MOL, SMILES, InChI/InChIKey	Database query (PubChem, ZINC), molecular docking, property calculation.	SDF: Contains 2D/3D coordinates, properties. InChI: Standard unique identifier.	SMILES are not unique; canonicalization needed.
Sequence Data	FASTA, GenBank (.gb)	BLAST analysis, cloning design, enzyme engineering.	FASTA: Universal for sequence analysis. GenBank: Rich feature annotation.	GenBank files may require manual curation.
Analysis Results	CSV/TSV, PDF, XLSX	Statistical analysis (R, Python), lab notebooks, publication figures.	CSV/TSV: Easy import into analysis tools. PDF: Immutable for records.	CSV lacks standardized column headers across tools.

Image Export Protocols for Publication & Reporting

High-quality image export is vital for documentation, presentations, and publications.

Protocol 3.1: Exporting a High-Resolution Pathway Diagram from BioNavi-NP

• Finalize Layout: Within the BioNavi-NP visualization module, adjust the pathway layout for optimal clarity (minimize edge crossing, logical grouping of enzyme modules).
• Configure Export Settings:
  • Format: Select .svg for vector-based editing (recommended) or .png for raster.
  • Resolution: For .png, set to 600 DPI or higher for publication.
  • Dimensions: Set canvas width to 1800px minimum.
  • Transparency: Enable if figures require background transparency for publication.
• Export and Verify: Save the file using a descriptive naming convention (e.g., BioNavi-NP_Pathway_[ProductName]_[Date].svg). Open the file in a viewer to verify element integrity.

Protocol 3.2: Standardized Export of Multi-Panel Figures

• Export Individual Panels: Follow Protocol 3.1 for each component (e.g., pathway map, chemical structure, phylogenetic tree) as separate .svg files.
• Assembly in Vector Graphics Software: Import all .svg files into software (e.g., Adobe Illustrator, Inkscape).
• Apply Consistent Styling:
  • Standardize font type (e.g., Arial, Helvetica) and size (8-10 pt for labels).
  • Use a consistent color palette (see Table 3) across all panels.
  • Align panels precisely and label with A, B, C.
• Final Export: Save the assembled figure as .eps or .tif (LZW compression) at the target journal's required resolution and dimensions.

Downstream Experimental Planning: From Export to Execution

Exported data must feed directly into concrete validation plans.

Protocol 4.1: Planning Heterologous Expression from BioNavi-NP Exports

• Inputs: FASTA files of gene clusters, SDF files of predicted intermediates/final product.
• Gene Synthesis & Cloning:
  • Use FASTA files to design codon-optimized sequences for the target host (e.g., S. cerevisiae, E. coli).
  • Export the GenBank file to identify promoter/terminator regions and restriction sites for cloning.
• Reference Standard Generation: Query the exported InChIKey/SMILES of the predicted final product against commercial catalogues (e.g., MolPort, Sigma-Aldrich) to source or commission analytical standards.
• LC-MS/MS Method Development: Use the exported molecular weight and predicted fragmentation pattern (from associated SDF properties) to develop a targeted LC-MS/MS method for metabolite detection.

Diagram: From Prediction to Validation Workflow

Title: BioNavi-NP Data Export Drives Downstream Experiments

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Reagents & Materials for Pathway Validation

Item / Reagent	Function in Downstream Validation	Example Product / Specification
Codon-Optimized Gene Fragments	For heterologous expression of predicted biosynthetic genes in the chosen host system.	gBlocks (IDT) or similar, > 1.5 kb synthesis capability.
Golden Gate or Gibson Assembly Mix	Seamless assembly of multiple gene fragments into an expression vector.	NEB Gibson Assembly Mix, Golden Gate Toolkit (MoClo).
Expression Host Strain	Chassis for pathway expression (bacterial, yeast, fungal).	E. coli BL21(DE3), S. cerevisiae BY4741, Aspergillus nidulans.
HPLC-Grade Solvents & Columns	For metabolite extraction, separation, and analysis (LC-MS).	Acetonitrile, Methanol (HPLC grade); C18 reversed-phase column.
Authentic Analytical Standard	Critical for confirming the identity and quantity of the predicted natural product.	Sourced commercially (e.g., Sigma, Carbosynth) or purified in-house.
LC-MS/MS System	High-sensitivity detection and structural characterization of pathway metabolites.	System with high-resolution mass analyzer (e.g., Q-TOF, Orbitrap).

Solving Common Challenges and Enhancing BioNavi-NP Prediction Accuracy

Within the BioNavi-NP framework for natural product pathway design, predicting biosynthetic pathways accurately is paramount. However, predictions can fail or remain incomplete due to gaps in genomic data, enzymatic promiscuity, or limitations in prediction algorithms. This document outlines common causes and provides actionable protocols to resolve these issues, enhancing the reliability of de novo pathway design.

Common Causes & Diagnostic Table

The following table summarizes primary causes of prediction failures, their indicators, and diagnostic checks.

Table 1: Causes and Diagnostics of Pathway Prediction Failures

Cause Category	Specific Cause	Key Indicators (in BioNavi-NP output)	Quick Diagnostic Check
Data Limitations	Missing genomic context (gaps in BGCs)	Pathway ends with "Unknown enzyme" or a large mass gap.	Perform contig end analysis & check for truncated genes.
Enzyme Specificity	Substrate promiscuity not accounted for	Multiple plausible substrates listed with low confidence scores (<0.7).	Run substrate similarity search (EC-BLAST, SSN analysis).
Algorithmic Gaps	Rule-based system missing a transformation	No suggested reaction for a critical chemical step.	Manually inspect chemical skeletons; consult MIBiG database.
Physiological Context	Lack of cofactor/ precursor availability	Predicted pathway requires rare, non-native cofactor (e.g., unusual metals).	Cross-reference with host organism's known metabolome (e.g., via ModelSEED).
Tool Limitations	Domain/gene prediction error (e.g., in antiSMASH)	Key functional domains (e.g., KS, AT) are not identified.	Re-annotate genome with multiple tools (antiSMASH, PRISM, DeepBGC).

Experimental Protocols for Resolution

Protocol 3.1: Gap Filling for Incomplete Biosynthetic Gene Clusters (BGCs)

Objective: To extend truncated BGCs and identify missing enzymatic steps. Materials:

• Genomic DNA or assembled contigs.
• Primers for genome walking or PCR.
• BioNavi-NP software suite.
• antiSMASH 7.0+ platform.
• NCBI BLAST suite.

Procedure:

• Identify Truncation Point: In BioNavi-NP, note the last known gene in the incomplete pathway. Extract its nucleotide sequence and the 10 kb region upstream/downstream.
• Genome Walking:
  • Design outward-facing primers from the known gene's terminus.
  • Perform thermal asymmetric interlaced (TAIL) PCR or use a commercial genome walking kit.
  • Sequence amplified products and assemble extended contig.
• Re-annotation:
  • Submit the extended contig to antiSMASH for re-analysis.
  • Use the "ClusterCompare" feature to identify homologous complete BGCs in databases.
• Functional Prediction:
  • Input the newly identified open reading frames (ORFs) into BioNavi-NP's "Enzyme Hunter" module.
  • Manually curate the proposed enzymatic functions by aligning protein sequences to the Pfam database.

Protocol 3.2: Validating and Characterizing Promiscuous Enzyme Activities

Objective: To experimentally test and verify the function of an enzyme predicted to have broad substrate specificity. Materials:

• Cloned gene of interest in an expression vector (e.g., pET28a).
• E. coli BL21(DE3) competent cells.
• Predicted substrate analogs (≥ 3).
• HPLC-MS system with diode array detector.

Procedure:

• Heterologous Expression:
  • Transform the expression plasmid into E. coli BL21(DE3). Induce expression with 0.5 mM IPTG at 16°C for 18 hours.
  • Purify the His-tagged protein using Ni-NTA affinity chromatography.
• In Vitro Enzyme Assay:
  • Prepare 100 µL reaction mixtures containing: 50 mM Tris-HCl (pH 7.5), 1 mM substrate, 5 mM necessary cofactor (e.g., NADPH, SAM), and 10 µM purified enzyme.
  • Incubate at 30°C for 1 hour. Terminate reactions with 100 µL ice-cold methanol.
• Product Analysis:
  • Centrifuge reactions at 15,000 x g for 10 min to pellet precipitated protein.
  • Analyze supernatant via HPLC-MS. Use a C18 column with a water/acetonitrile gradient (5% to 95% acetonitrile over 20 min).
  • Compare retention times and mass spectra of products to those of controls (no enzyme, boiled enzyme).
• Data Integration into BioNavi-NP:
  • Input confirmed substrate-product pairs into the BioNavi-NP "Knowledge Base Manager" to refine future predictions for similar enzyme families.

Visualization of Workflows

Title: Gap-Filling Protocol for Incomplete BGCs

Title: Enzyme Promiscuity Validation Workflow

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Reagents for Pathway Prediction Troubleshooting

Item	Function in Context	Example Product / Specification
Genome Walking Kit	Amplifies unknown DNA sequences adjacent to known regions for BGC extension.	TaKaRa Genome Walking Kit, PrimerSuite for TAIL-PCR.
High-Fidelity DNA Polymerase	Accurate amplification of GC-rich genomic regions typical of BGCs.	Q5 High-Fidelity DNA Polymerase (NEB).
Ni-NTA Resin	Affinity purification of His-tagged recombinant enzymes for activity assays.	Ni-NTA Superflow (Qiagen) or HisPur Ni-NTA Resin (Thermo).
Cofactor Substrates	Essential for in vitro enzyme assays (e.g., NADPH, SAM, ATP).	>98% purity, sodium salts (Sigma-Aldrich).
LC-MS Grade Solvents	Critical for sensitive detection of pathway intermediates and products in HPLC-MS.	Acetonitrile and Water (Optima LC/MS Grade, Fisher Chemical).
Bioinformatic Database Subscription	Access to curated genomic and metabolomic data for validation.	MIBiG, antiSMASH DB, GNPS.
Cloud Compute Credits	For running resource-intensive bioinformatic pipelines (antiSMASH, DeepBGC).	AWS, Google Cloud, or Azure credits.

Refining Search Strategies for Rare or Novel Natural Product Scaffolds

The discovery of novel natural product (NP) scaffolds is pivotal for addressing emerging antimicrobial resistance and untreatable diseases. Within the context of the BioNavi-NP tutorial for natural product pathway design research, efficient search strategies are critical for navigating the vast biosynthetic "dark matter" of microbial genomes and metagenomes. BioNavi-NP is an AI-driven platform that predicts and designs biosynthetic pathways for novel NP skeletons. This application note details refined search methodologies to feed high-value, rare scaffolds into the BioNavi-NP design pipeline, accelerating the in silico to in vitro discovery cycle.

Current Landscape & Quantitative Data

Live search data (as of early 2024) highlights the gap between genomic potential and characterized compounds.

Table 1: The Natural Product Discovery Gap

Metric	Value	Source/Implication
Estimated microbial NPs in nature	>1,000,000	Theoretical based on genomic diversity
Microbially-derived NPs in databases (e.g., LOTUS, NP Atlas)	~40,000	Represents <5% of potential
Bacterial Biosynthetic Gene Clusters (BGCs) per genome	5-15	Varies by taxonomy & environment
"Silent" or cryptic BGCs	>50% of all BGCs	Not expressed under lab conditions
Novel scaffolds reported annually (approx.)	200-300	Slowing rate of discovery with traditional methods
Average time from discovery to structure elucidation	6-18 months	Bottleneck for high-throughput workflows

Refined Search Strategies: Protocols & Application Notes

Strategy 1: Genome-Mining for Rare Scaffolds via BGC Subtype Targeting

Protocol: Targeted Non-Ribosomal Peptide Synthetase (NRPS) / Polyketide Synthase (PKS) Subtype Mining

Objective: To identify BGCs encoding for rare chemical motifs (e.g., β-lactams, phosphonates, glycosylated macrolides) from genomic assemblies.

Materials & Workflow:

• Input Data: High-quality metagenome-assembled genomes (MAGs) or isolate genomes.
• BGC Prediction: Run antiSMASH 7.0 (or latest) with strict --limit to relevant taxa, enabling all analysis modules.
• Targeted HMM Search: Use custom HMM profiles (e.g., from Pfam) for rare enzymatic domains:
  • Cytochrome P450 (PF00067): For oxidative tailoring.
  • Phosphoenolpyruvate mutase (PF01648): For phosphonate backbone.
  • FAD-dependent monooxygenase (PF01494): For uncommon heterocyclizations.
• Priority Scoring: Rank BGCs by:
  • Presence of ≥2 rare tailoring domains.
  • Phylogenetic distance from known model BGCs (using BiG-SCAPE).
  • PRISM 4 prediction of a scaffold with <5 analogs in NP databases.
• Output: Prioritized list of BGC FASTA files for input into BioNavi-NP for de novo pathway reconstruction and analogs design.

The Scientist's Toolkit:

Research Reagent / Tool	Function in Protocol
antiSMASH 7.0+	Core BGC identification & preliminary annotation.
BiG-SCAPE	BGC networking & phylogenomic context.
PRISM 4	In silico prediction of chemical structure from sequence.
HMMER Suite	Execution of custom Hidden Markov Model searches.
MIBiG database	Reference database of known BGCs for comparison.

Strategy 2: Metabolomics-Driven Genome Mining

Protocol: LC-MS/MS Metabolomic Feature Prioritization Linked to BGCs

Objective: To link observed rare mass features from culture extracts directly to their encoding BGC, overcoming "silent" BGC challenges.

Detailed Protocol:

• Culturing & Extraction: Grow target organism (e.g., rare Actinomycete) in 3-4 diverse media (ISP2, R5, AIA). Extract metabolites with 1:1:1 Ethyl Acetate:MeOH:Acetone.
• LC-MS/MS Analysis:
  • Instrument: High-resolution Q-TOF or Orbitrap.
  • Method: Reverse-phase C18 column, 5-95% MeCN/H~2~O (0.1% Formic acid) over 20 min.
  • Data Acquisition: Data-Dependent Acquisition (DDA) mode, top 10-15 ions per cycle.
• Feature Prioritization:
  • Process raw data with MZmine 3.
  • Apply filters: Novelty Score (using GNPS against ALL public spectra), Complexity Score (based on MS/MS fragmentation tree from SIRIUS), and Bioactivity Potential (predicted by NPClassifier).
  • Export list of m/z and retention times for high-priority features.
• Genetic Linkage:
  • Sequence the organism's genome.
  • Correlate feature abundance across culture conditions with BGC expression data from RNA-seq (if available) or BGC presence/absence across related strains.
  • Use metabologenomics pipelines to link MS/MS molecular networks to BGC phylogenomic networks.
• BioNavi-NP Integration: Input the correlated BGC sequence into BioNavi-NP to explore biosynthetic logic and propose engineering strategies for yield improvement or analog generation.

(Diagram 1: Metabolomics-Driven Genome Mining Workflow for BioNavi-NP.)

Strategy 3: Phylogeny-Guided Exploration of Unexplored Taxa

Protocol: Building a Targeted Strain Collection from Underrepresented Clades

Objective: Systematically select and screen microbial taxa with high genomic potential but low chemical characterization.

Methodology:

• Phylogenetic Analysis:
  • Construct a reference tree (16S rRNA or core genes) for a family of interest (e.g., Micromonosporaceae).
  • Map known NP data from LOTUS or NP Atlas onto the tree.
• Identify "Dark" Clades: Flag monophyletic clades with:
  • High genetic divergence (>5% 16S dissimilarity).
  • Zero or few documented NPs.
  • Geographic origin from extreme/unique biomes.
• Strain Acquisition & Validation: Source strains from culture collections (e.g., DSMZ, ATCC) or targeted isolation. Confirm phylogeny via genome sequencing.
• Miniaturized Elicitation Screening: Use 24-well microcultures with 4-5 chemical elicitors (e.g., N-acetylglucosamine, histone deacetylase inhibitors). Perform rapid UHPLC-UV analysis to detect unique chromatographic peaks.
• BioNavi-NP Pre-Design: For strains showing elicited responses, input their genomes into BioNavi-NP before full chemical characterization. Use the platform to predict the most probable novel scaffold and design diagnostic PCR primers or biosensors for targeted isolation.

Integration with BioNavi-NP: A Tutorial Perspective

These search strategies generate two primary data types for BioNavi-NP:

Table 2: BioNavi-NP Input Scenarios from Search Strategies

Search Strategy Output	BioNavi-NP Input Module	Action & Tutorial Goal
Prioritized BGC sequence (FASTA)	De Novo Pathway Designer	Reconstruct putative pathway; validate enzyme functions.
Correlated MS/MS feature & BGC	Analogue Designer	Propose modifications to core scaffold; predict new derivatives.
Genome of "dark" clade organism	BGC Prioritization Predictor	Use AI to identify the single most promising cryptic BGC.

(Diagram 2: Integration of Search Outputs into BioNavi-NP Modules.)

Concluding Protocol: Validation Pipeline for BioNavi-NP Predictions

Title: Heterologous Expression & Analytical Validation of Designed Pathways

Purpose: To experimentally validate a novel NP pathway predicted by BioNavi-NP from a search-identified rare BGC.

Protocol Steps:

• BioNavi-NP Output: Receive a designed gene cluster optimized for expression in Streptomyces coelicolor or Pseudomonas putida.
• DNA Synthesis & Assembly: Synthesize the ~40-60 kb construct via yeast assembly or direct synthesis (e.g., from Twist Bioscience).
• Heterologous Expression:
  • Transform assembly into appropriate expression host.
  • Plate on selective media and incubate at 30°C for 48 hrs.
  • Inoculate 50 mL of optimal production medium (e.g., R5 for Streptomyces) and culture for 5-7 days.
• Metabolite Analysis:
  • Extract culture broth and mycelia separately with organic solvents.
  • Analyze by LC-MS/MS as in Strategy 2.
  • Key Comparison: Overlay extracted ion chromatograms (EICs) of predicted masses from the design vs. control empty vector strain.
• Structure Elucidation: For confirmed novel products, scale up fermentation (2-5 L). Purify compounds using preparative HPLC. Elucidate structure using NMR (1H, 13C, 2D) and compare to BioNavi-NP's predicted chemical shift values (if module available).

Within the BioNavi-NP framework for automated natural product pathway design, a critical challenge is the rational integration of prior biochemical knowledge. This protocol details the methodology for incorporating characterized enzyme data from external databases to design and prioritize hybrid biosynthetic pathways, thereby increasing the likelihood of constructing functional systems in heterologous hosts.

Application Notes: Data Curation and Integration

The process begins with the systematic acquisition and standardization of external enzyme data. The primary sources include BRENDA, UniProt, and MetaCyc. Data must be parsed for kinetic parameters, substrate specificity, cofactor requirements, and organismal origin. A key step is reconciling enzyme commission (EC) numbers with genome-scale annotations from the target chassis organism (e.g., S. cerevisiae, E. coli) to identify potential compatibility issues.

Table 1: Key External Database Sources and Data Types

Database	Primary Data Types	Relevance to Pathway Design
BRENDA	Km, kcat, turnover, pH/Temp optimum, inhibitors	Quantifies enzyme efficiency and informs reaction feasibility.
UniProt	Protein sequence, organism, functional domains	Enables sequence similarity search and homology modeling.
MetaCyc	Curated metabolic pathways, reaction rules	Provides validated enzymatic transformations and context.
RHEA	Biochemical reaction mechanisms (RDL)	Standardizes reaction representations for in silico tools.
PDB	3D protein structures	Informs enzyme engineering and substrate docking studies.

Data integration within BioNavi-NP involves creating a local "Known Enzyme Registry." This registry cross-references external IDs and appends confidence scores based on the number of independent characterizations and the phylogenetic distance between the source and target host organism.

Protocol: Incorporating Enzyme Data into BioNavi-NP for Hybrid Pathway Design

Materials & Reagent Solutions

Table 2: Research Reagent Solutions for In Silico and In Vivo Validation

Reagent / Tool	Function in Protocol
BioNavi-NP Software Suite	Core platform for pathway retrosynthesis and enzyme matching.
Local SQL/NoSQL Database	Houses the curated Known Enzyme Registry.
Python/R Bio-informatics Stack (e.g., BioPython)	For API queries, data parsing, and sequence alignment.
Homology Modeling Software (e.g., SWISS-MODEL)	Predicts enzyme structure in the absence of PDB data.
In Vivo Cloning Kit (e.g., Gibson Assembly)	For physical construction of prioritized pathways.
HPLC-MS System	Validates compound production from engineered strains.

Step-by-Step Methodology

Part A: Data Acquisition and Curation

• Query External APIs: Programmatically extract entries for target EC numbers or reaction classes using provided RESTful APIs (e.g., BRENDA's web service).
• Standardize Parameters: Convert all kinetic data (e.g., Km in mM, kcat in s⁻¹) to common units. Note assay conditions.
• Assign Confidence Metrics: Calculate a composite "Integrability Score" (I-score) for each enzyme record:
  • I-score = (0.4 * Data Completeness) + (0.3 * Phylogenetic Score) + (0.3 * Kinetic Optimality).
  • Phylogenetic Score: 1.0 for same species, 0.8 for same genus, 0.5 for same family, 0.2 for other.
• Populate Known Enzyme Registry: Store records with fields: EnzymeID, SourceDB, EC, Sequence, Kinetic_Parameters, Organism, I-score.

Part B: In Silico Pathway Augmentation in BioNavi-NP

• Run De Novo Retrosynthesis: Input target natural product scaffold into BioNavi-NP to generate initial hypothetical pathways.
• Trigger Enzyme Matching: For each proposed biochemical step, query the local Known Enzyme Registry via the software's plugin interface.
• Rank and Prioritize: BioNavi-NP will re-score proposed pathways using the I-scores of matched enzymes. Pathways where >70% of steps are filled with high-I-score (≥0.7) enzymes are flagged as high-priority.
• Generate Construct Designs: Export DNA sequence designs for the high-priority pathway, selecting enzyme coding sequences codon-optimized for the chosen host.

Part C: Experimental Validation Workflow

• Construct Assembly: Use standardized cloning (e.g., Golden Gate or Gibson Assembly) to build expression vectors for the top 3 prioritized pathways.
• Heterologous Expression: Transform constructs into the host organism and cultivate under inducing conditions.
• Metabolite Profiling: Extract metabolites from culture and analyze via HPLC-MS. Compare chromatograms and mass spectra to authentic standards.
• Iterative Refinement: Feed results (success/failure, titer data) back into the Known Enzyme Registry to update confidence scores for future designs.

Visualizations

Diagram 1: Data Integration & Pathway Design Workflow (92 chars)

Diagram 2: Protocol for Known Enzyme Data Integration (73 chars)

Optimizing Computational Parameters for Large or Complex Polycyclic Molecules

This document serves as an Application Note within the broader BioNavi-NP tutorial framework for natural product pathway design. Optimizing computational parameters is critical for accurate quantum chemical calculations and molecular mechanics simulations of large, complex polycyclic molecules, such as those commonly found in natural products. This guide details protocols for parameter selection, validation, and integration into the BioNavi-NP workflow to enhance the reliability of in silico predictions for biosynthesis.

Key Computational Parameters & Optimization Strategies

For large polycyclic systems, standard computational settings often fail, leading to inaccurate geometries, energies, and spectral predictions. The following table summarizes core parameters requiring optimization.

Table 1: Critical Computational Parameters for Large Polycyclic Systems

Parameter Category	Default/Simple Setting	Optimized Setting for Large Polycycles	Rationale & Impact
Basis Set	6-31G(d)	def2-TZVP, ma-def2-SVP, or 6-311+G(2d,p)	Better description of electron correlation and dispersion forces in crowded, conjugated systems.
Density Functional	B3LYP	ωB97X-D, B3LYP-D3(BJ), or M06-2X	Inclusion of empirical dispersion correction is non-negotiable for stacked/sterically crowded rings.
Integration Grid	FineGrid	UltraFineGrid or SG-3	Crucial for numerical accuracy in integration for molecules with many atoms and high electron density.
SCF Convergence	Default	Tight (10^-8 a.u.) or VeryTight (10^-9 a.u.)	Prevents false convergence in systems with many nearly degenerate orbitals.
Geometry Optimization Algorithm	Standard Berny	GEDIIS or Force- and Energy- based combined	More robust convergence for molecules with many degrees of freedom and shallow potential energy surfaces.
Solvation Model	IEFPCM (default dielectric)	SMD with explicitly defined solvent (e.g., ε=4.7 for chloroform)	More realistic modeling of natural products in biosynthetic or extraction environments.
Conformational Search	Systematic (small torsions)	CREST (GFN2-xTB) with extensive meta-dynamics	Efficiently explores complex conformational space of flexible polycyclic backbones.

Detailed Experimental Protocols

Protocol 3.1: Pre-Optimization and Conformational Sampling with CREST

Objective: Generate a comprehensive set of low-energy conformers for a large polycyclic molecule prior to high-level quantum mechanical (QM) calculation.

• Input Preparation: Generate a reasonable 3D geometry of your target polycyclic molecule using a builder (e.g., Avogadro) or from a crystal structure (.pdb, .mol2).
• CREST Execution:

• Output Processing: The crest_conformers.xyz file contains the ensemble. Select the 5-10 lowest-energy conformers for subsequent QM refinement.

Protocol 3.2: High-Level DFT Geometry Optimization and Frequency Calculation

Objective: Obtain an accurate, minima-verified geometry and thermodynamic corrections using a dispersion-corrected functional.

• Software: Gaussian 16, ORCA, or PSI4.
• Input File Template (ORCA 5.0):
  • ωB97X-D3: Range-separated functional with D3 dispersion.
  • def2-TZVP: Triple-zeta quality basis set.
  • TightSCF: Tight SCF convergence criteria.
  • UltraFineGrid: High-quality integration grid.
  • Opt Freq: Requests geometry optimization and vibrational frequency analysis.
• Execution & Validation: Run the calculation. Confirm a true minimum by the absence of imaginary frequencies in the output.

Protocol 3.3: NMR Chemical Shift Prediction (GIAO)

Objective: Calculate accurate NMR chemical shifts (¹³C, ¹H) for comparison with experimental data to validate structures.

• Use Optimized Geometry: Use the geometry from Protocol 3.2.
• Input File Template (Gaussian 16):
  • nmr=giao: Requests GIAO NMR calculation.
  • scrf=(cpcm,...): Defines the solvation model.
• Referencing: Calculate shifts for tetramethylsilane (TMS) at the same level of theory. Report chemical shift δ (ppm) = σTMS - σmolecule.

Visualizations

Title: Computational Workflow for Polycyclic Molecule Parameter Optimization

Title: Computational Modeling Informs Biosynthetic Pathway Design

The Scientist's Toolkit

Table 2: Essential Research Reagent Solutions & Computational Tools

Item	Function/Application in Optimization
CREST (Conformer-Rotamer Ensemble Sampling Tool)	Command-line tool for automated, quantum chemistry-informed conformational searching using semi-empirical methods (GFN-xTB). Essential for initial sampling of complex molecules.
ORCA 5.0+	Ab initio quantum chemistry package. Preferred for its efficiency with large systems, robust dispersion corrections, and powerful DFT functionals like ωB97X-D3.
Gaussian 16	Industry-standard suite for quantum chemistry. Used for high-accuracy NMR (GIAO) and optical rotation calculations following geometry optimization.
GFN2-xTB Hamiltonian	Semi-empirical method within CREST/xtb. Provides surprisingly accurate geometries and energies at minimal cost, enabling pre-screening.
SMD Solvation Model	Continuum solvation model parameterized for a wide range of solvents. Critical for modeling environmental effects in biosynthetic cavities or extraction solvents.
def2 Basis Set Series	Hierarchy of Gaussian-type orbital basis sets (e.g., def2-SVP, def2-TZVP). Offer balanced performance and are well-parametrized with dispersion corrections.
BioNavi-NP Platform	Target application. The optimized molecular structures and properties generated via these protocols serve as direct inputs for retrobiosynthesis and enzyme discovery.

Within the framework of the broader thesis on the BioNavi-NP platform for natural product pathway design and retrobiosynthetic analysis, this application note details a practical case study. The focus is the troubleshooting of a heterologous expression pathway for Schweinfurthin J, a complex, challenging macrocyclic stilbenoid with promising selective anticancer activity. Initial pathway designs in S. cerevisiae, based on BioNavi-NP predictions and literature precedent, resulted in extremely low titers (<0.1 mg/L), necessitating systematic troubleshooting. This protocol outlines the multi-step diagnostic and optimization workflow implemented to resolve the bottlenecks.

Initial Pathway Analysis & Identified Bottlenecks

Quantitative data from the initial failed expression experiment is summarized below. Key metrics measured included mRNA transcript levels (qRT-PCR), intermediate metabolite accumulation (LC-MS), and final product titer.

Table 1: Initial Pathway Performance Metrics

Pathway Component	Transcript Level (Relative Units)	Key Intermediate Accumulation (μM)	Hypothesized Bottleneck
Phenylalanine/ Tyrosine Precursor	1.0 (Baseline)	L-Phenylalanine: 1050 ± 120	No
Stilbene Synthase (STS)	0.8 ± 0.1	p-Coumaroyl-CoA: Not Detected	Substrate Channeling?
Prenyltransferase (PT)	0.3 ± 0.05	Prenylated Stilbene Core: 1.5 ± 0.3	Major - Enzyme Kinetics
Cytochrome P450 (CYP)	0.9 ± 0.2	Hydroxylated Intermediate: Trace	Major - Cofactor Supply
Macrocyclase (MC)	0.6 ± 0.1	Schweinfurthin J: 0.08 ± 0.02 mg/L	Downstream of Limiting Step

Title: Initial Bottleneck Identification Workflow

Experimental Protocols for Systematic Troubleshooting

Protocol 3.1: Chassis-Specific Codon Re-optimization and Expression Vector Tuning

Objective: To enhance the translation efficiency of the limiting Prenyltransferase (PT) and Cytochrome P450 (CYP) genes in S. cerevisiae.

Materials (Research Reagent Solutions):

• pRS42K Expression Vectors: Yeast episomal plasmids with tunable promoters (pGPD, pTEF1).
• Codon-Optimized Gene Fragments: PT and CYP genes synthesized with S. cerevisiae-biased codon usage (IDT, Twist Bioscience).
• Gibson Assembly Master Mix: For seamless vector construction (NEB).
• SC-Ura/SC-His Dropout Media: For selection of transformed yeast.
• Autoinduction Gal/Raf Media: For high-density fermentation and protein expression.

Methodology:

• Gene Synthesis & Cloning: Order PT and CYP genes with codon adaptation index (CAI) optimized for S. cerevisiae. Assemble individual genes into pRS42K vectors under control of the strong constitutive pGPD promoter using Gibson Assembly.
• Promoter Tuning: Subclone the CYP gene under a series of modified promoters (pTEF1, pADH1, and a synthetic promoter library) to modulate expression levels and reduce metabolic burden.
• Co-transformation: Co-transform the optimized PT and CYP vectors, along with the existing STS and MC vectors, into the S. cerevisiae production strain (e.g., BY4741 Δaro4 Δaro7).
• Screening: Perform small-scale (5 mL) deep-well plate cultivations for 96 hours. Quench metabolism and extract metabolites with 80% methanol for LC-MS analysis.

Protocol 3.2: Cofactor Engineering and Precursor Feeding

Objective: To alleviate the P450 bottleneck by enhancing intracellular supply of NADPH and heme cofactors, and to bolster the prenyl-donor pool.

Materials (Research Reagent Solutions):

• NADP+ & Heme Precursors: Nicotinic acid and δ-aminolevulinic acid (ALA) in sterile stock solutions.
• Prenyl Pyrophosphate (GGPP): Cell-permeable analog or precursor (geraniol, mevalonolactone).
• Engineered Cofactor Modules: Plasmid expressing POS5 (NADPH kinase) or HEM1/HEM2 (heme biosynthesis) under inducible control.
• LC-MS/MS System (Sciex 6500+): For absolute quantification of cofactors and intermediates.

Methodology:

• Precursor Feeding: Supplement the fermentation media with 1 mM nicotinic acid, 0.5 mM ALA, and 2 mM mevalonolactone at the time of induction.
• Cofactor Module Expression: Transform an additional plasmid expressing the POS5 gene into the production strain.
• Fermentation: Perform 250 mL baffled flask fermentations. Take samples at 24, 48, 72, and 96 hours.
• Metabolite & Cofactor Quantification: Lyse cell pellets. Use a commercial NADPH/NADP+ assay kit for cofactor ratios. Quantify GGPP and pathway intermediates against authentic standards via LC-MS/MS.

Protocol 3.3: Enzyme Fusion for Substrate Channeling

Objective: To improve flux between the STS and PT enzymes by spatial proximity, potentially increasing the effective local concentration of the coumaroyl-CoA intermediate.

Materials (Research Reagent Solutions):

• Flexible Linker Peptide Sequences: (GGGGS)n (n=3, 5) encoding DNA fragments.
• Golden Gate Assembly System: BsaI-HFv2 restriction enzyme and T4 DNA Ligase for modular assembly.
• Anti-FLAG Affinity Gel: For pull-down assays to confirm fusion protein integrity.

Methodology:

• Fusion Construct Design: Design STS-linker-PT and PT-linker-STS fusion constructs. Cloned into a single pRS42K vector.
• Expression & Validation: Express the fusion protein in yeast. Validate full-length protein expression via Western blot using FLAG-tag antibodies.
• In Vitro Activity Assay: Lysate cells and perform a coupled enzyme assay with phenylalanine and isopentenyl pyrophosphate substrates, comparing activity to the co-expressed, non-fused enzyme pair.
• In Vivo Testing: Introduce the optimal fusion construct into the full pathway strain and measure Schweinfurthin J titer as in Protocol 3.1.

Results & Optimized Pathway Configuration

The systematic application of the troubleshooting protocols yielded significant improvements. Key quantitative outcomes are summarized below.

Table 2: Optimized Pathway Performance Metrics Post-Troubleshooting

Intervention	Target	Transcript Level (Δ)	Intermediate (Δ)	Final Titer (mg/L)
Codon Optimization + pTEF1 promoter	PT & CYP	PT: +320%; CYP: +150%	Prenyl Core: +800%	0.45 ± 0.08
+ Cofactor Precursor Feeding	NADPH & Heme	N/A	OH-Intermediate: +950%	1.20 ± 0.15
+ POS5 Cofactor Module	NADPH Regeneration	N/A	NADPH/NADP+ Ratio: +220%	1.65 ± 0.20
+ STS-(G4S)3-PT Fusion Protein	Substrate Channeling	Single Transcript	p-Coumaroyl-CoA: Detected	3.10 ± 0.35

Title: Optimized Schweinfurthin J Biosynthetic Pathway

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Reagent Solutions for Macrocyclic Pathway Troubleshooting

Reagent / Material	Supplier Examples	Function in This Study
Codon-Optimized Gene Fragments	IDT, Twist Bioscience	Eliminates translational bottlenecks in heterologous hosts by matching host tRNA abundance.
Tunable Promoter Library (Yeast)	Addgene, Synthetic Genomics	Enables fine-tuning of enzyme expression levels to balance metabolic flux and burden.
Gibson / Golden Gate Assembly Mix	NEB, Thermo Fisher	Enables rapid, seamless, and modular construction of multi-gene pathways and fusion proteins.
LC-MS/MS Authentic Standards	Sigma-Aldrich, Cayman Chem	Essential for absolute quantification of pathway intermediates, cofactors, and final product.
NADP/NADPH Quantification Kit	Promega, Abcam	Provides precise measurement of the redox cofactor state critical for P450 enzyme activity.
Cell-Permeable Pathway Precursors	e.g., Mevalonolactone, ALA	Bolsters intracellular pools of isoprenoid and heme precursors to relieve substrate limitations.
Affinity-Tag Purification Resins	Anti-FLAG, Ni-NTA Agarose	Validates fusion protein expression and allows for in vitro enzyme kinetics characterization.

Benchmarking BioNavi-NP: Validating Predictions and Comparing to Alternative Tools

1. Introduction and Framework Overview Within the context of BioNavi-NP tutorial for natural product pathway design research, computational prediction is merely the first step. This document provides Application Notes and Protocols for the experimental validation of de novo enzymatic pathways predicted by BioNavi-NP for novel natural product (NP) biosynthesis. The validation pipeline progresses from in vitro enzyme characterization to in vivo reconstitution, culminating in analytical verification of the final product.

2. Core Validation Workflow and Protocols

2.1. Stage 1: In Vitro Enzyme Kinetic Assays

• Objective: To biochemically verify the function of each predicted enzyme in the proposed pathway.
• Protocol:
  • Gene Synthesis & Cloning: Codon-optimize and synthesize predicted enzyme genes for the desired heterologous host (e.g., E. coli). Clone into an appropriate expression vector (e.g., pET series) with an affinity tag (His6, Strep-II).
  • Heterologous Expression: Transform expression plasmids into E. coli BL21(DE3). Grow cultures in LB medium at 37°C to OD600 ~0.6-0.8. Induce with 0.1-1.0 mM IPTG and incubate at 16-18°C for 16-20 hours.
  • Protein Purification: Lyse cells via sonication. Purify soluble proteins using immobilized metal affinity chromatography (IMAC). Confirm purity and size via SDS-PAGE.
  • Activity Assay: For each enzyme, set up a 100 µL reaction containing: appropriate buffer (e.g., Tris-HCl, pH 7.5-8.5), predicted co-factors (Mg2+, ATP, NADPH, SAM, etc.), putative substrate (commercially available or chemically synthesized), and purified enzyme. Incubate at 30°C for 30-60 min.
  • Analysis: Terminate reaction and analyze via LC-MS. Monitor for consumption of substrate and formation of predicted product. Determine kinetic parameters (kcat, KM) using varying substrate concentrations.

2.2. Stage 2: In Vivo Pathway Reconstitution

• Objective: To confirm the functional assembly of the complete pathway in a live microbial host.
• Protocol:
  • Construct Assembly: Assemble the full predicted biosynthetic gene cluster (BGC) using modular cloning techniques (e.g., Golden Gate, Gibson Assembly) into a suitable expression vector or chromosome integration system for the host (e.g., E. coli, S. albidoflavus, S. cerevisiae).
  • Strain Engineering: Transform or conjugate the assembled construct into the chosen heterologous host. Generate control strains containing empty vectors or vectors with genes omitted.
  • Cultivation & Metabolite Extraction: Grow engineered and control strains in appropriate production media (e.g., R5A for streptomycetes, YPD for yeast) for 3-7 days. Extract metabolites from cell pellets and/or supernatant with organic solvents (e.g., ethyl acetate, 1:1 methanol:ethyl acetate).
  • Metabolomic Analysis: Resuspend extracts in methanol for LC-HRMS/MS analysis. Use high-resolution mass spectrometry to detect ions matching the exact mass of the predicted final NP and its putative intermediates.

2.3. Stage 3: Structural Elucidation of the Novel Natural Product

• Objective: To unambiguously confirm the chemical structure of the BioNavi-NP-predicted compound.
• Protocol:
  • Scale-up Production: Cultivate the successful producing strain from Stage 2 in large-scale (1-10 L) fermentation.
  • Purification: Employ a combination of chromatographic techniques (e.g., silica gel, Sephadex LH-20, and preparative reversed-phase HPLC) to isolate the target compound to >95% purity, as assessed by analytical LC-UV.
  • Spectroscopic Analysis:
    • Nuclear Magnetic Resonance (NMR): Acquire 1D (1H, 13C) and 2D (COSY, HSQC, HMBC) NMR spectra in deuterated solvents.
    • Mass Spectrometry: Obtain high-resolution ESI-MS/MS data for molecular formula confirmation and fragmentation pattern analysis.
  • Data Interpretation: Compare experimental NMR and MS data with in silico predictions (e.g., using NMR prediction software) and literature data for known structural motifs to solve the complete structure.

3. Data Presentation

Table 1: Summary of Key Validation Experiments and Expected Outcomes

Validation Stage	Key Measurable Parameter	Instrument/Method	Positive Result Indicator
In Vitro Assay	Specific Activity (nmol/min/mg)	LC-MS, Spectrophotometry	Product peak formation, quantifiable turnover rate.
In Vitro Assay	Michaelis Constant (KM, µM)	LC-MS with varied [S]	Saturation kinetics fitting the Michaelis-Menten model.
In Vivo Reconstitution	Titer (mg/L)	LC-MS with external standard	Detectable target compound only in the full-pathway strain.
In Vivo Reconstitution	Intermediate Accumulation	LC-HRMS/MS	Detection of pathway intermediates in knockout strains.
Structural Elucidation	Molecular Formula	HR-ESI-MS	< 5 ppm error vs. predicted [M+H]+ or [M-H]- ion.
Structural Elucidation	NMR Assignment Completion	1D/2D NMR	All 1H/13C signals assigned, consistent with predicted scaffold.

Table 2: Research Reagent Solutions Toolkit

Reagent/ Material	Function/Application	Example/Notes
pET-28a(+) Vector	High-level protein expression in E. coli	Contains T7 lac promoter, His6-Tag for purification.
E. coli BL21(DE3)	Expression host for recombinant proteins	Deficient in proteases, carries T7 RNA polymerase gene.
Ni-NTA Agarose	Immobilized metal affinity chromatography resin	Binds polyhistidine-tagged proteins for purification.
Isopropyl β-D-1-thiogalactopyranoside (IPTG)	Inducer of T7/lac hybrid promoter	Used at low concentrations for soluble protein expression.
Adenosine 5'-triphosphate (ATP)	Essential cofactor for kinases, ligases, etc.	Critical for in vitro assays of many biosynthetic enzymes.
S-adenosyl methionine (SAM)	Methyl group donor for methyltransferases.	Required for validation of O-/N-/C-methyltransferases.
Nicotinamide adenine dinucleotide phosphate (NADPH)	Redox cofactor for reductases, P450s.	Validates reductive steps in terpene/alkaloid pathways.
Ethyl Acetate	Organic solvent for metabolite extraction.	Used for liquid-liquid extraction of semi-polar NPs.
Deuterated Chloroform (CDCl3)	NMR solvent for non-polar compounds.	Standard for analyzing terpenoids, polyketides.
Deuterated Methanol (CD3OD)	NMR solvent for polar compounds.	Standard for analyzing glycosylated or peptide NPs.

4. Visualization

Validation Workflow for BioNavi-NP Predictions

Stepwise In Vitro Assay for Pathway Validation

This application note provides a comparative analysis of BioNavi-NP against established tools for natural product (NP) biosynthetic pathway design, specifically AntiSMASH (ASMPKS) and RetroPathRL. The content is framed within a tutorial context for a thesis focused on leveraging BioNavi-NP for de novo pathway prediction and retrobiosynthesis. This guide is intended for researchers and professionals in drug development seeking to select and apply the most appropriate computational platform for their NP discovery and engineering projects.

A fundamental comparison of core functionalities, algorithmic approaches, and primary use cases is summarized in Table 1.

Table 1: Core Feature Comparison of NP Pathway Design Tools

Feature	BioNavi-NP	AntiSMASH (ASMPKS)	RetroPathRL
Primary Function	De novo retrobiosynthetic pathway prediction	Genomic mining for known BGCs	Retrosynthesis planning for metabolic engineering
Core Algorithm	Deep learning (Transformer) & knowledge graph	Rule-based & HMM profiling	Reinforcement Learning (RL) & retrobiosynthetic rules
Input	Target NP structure (SMILES)	Genome/DNA sequence	Target molecule & specified chassis metabolism
Output	Predicted biosynthetic pathways (enzymatic steps)	Identified Biosynthetic Gene Clusters (BGCs)	Possible heterologous pathways with viability scores
Key Strength	Predicts pathways for novel, non-native NPs without genomic precursor	Industry standard for BGC annotation and classification	Integrates pathway design with chassis organism constraints
Typical Application	Designing pathways for novel NPs or in non-native hosts	Discovering potential NP producers from genomic data	Designing feasible pathways for synthetic biology implementation

Quantitative Performance Benchmarking

Performance metrics for pathway prediction accuracy and computational efficiency, gathered from recent literature and benchmark studies, are presented in Table 2.

Table 2: Quantitative Performance Metrics (Representative Data)

Metric	BioNavi-NP	AntiSMASH (v7)	RetroPathRL (2.0)
Prediction Time (avg. per target)	~5-15 minutes	~3-10 minutes (per genome)	~10-30 minutes
Reported Recall (Known Pathways)	91% (on test set)	>90% (for known BGC types)	85% (within known metabolism)
Precision (Top-1 Prediction)	82%	N/A (detection tool)	78%
Number of Rule-Based Reactions	~1,200 biosynthetic rules	~1,000 HMM profiles	~6,000 generalized enzymatic rules
Supported NP Classes	Polyketides, Terpenes, Alkaloids, etc.	All major BGC types (PKS, NRPS, RiPPs, etc.)	Broad metabolism (incl. plant & microbial NPs)

Experimental Protocols for Tool Application

Protocol 4.1:De NovoPathway Prediction Using BioNavi-NP

Objective: To predict a plausible biosynthetic pathway for a novel natural product structure. Materials: BioNavi-NP web server or local installation; Target NP structure in SMILES format. Procedure:

• Input Preparation: Obtain or draw the chemical structure of the target natural product. Convert it into a canonical SMILES string using a tool like Open Babel or RDKit.
• Parameter Configuration: Access the BioNavi-NP web interface. Paste the SMILES string into the input field. Set advanced parameters:
  • Search Depth: Set to 8-10 steps for complex NPs.
  • Beam Size: Retain 5-10 candidate pathways per step for balance of diversity and depth.
  • Similarity Threshold: Set Tanimoto coefficient threshold (e.g., 0.7) for precursor matching.
• Execution: Initiate the prediction run. The tool will iteratively decompose the target into biosynthetic building blocks using its neural network.
• Output Analysis: Review the ranked list of predicted pathways. Each pathway node displays the transformation rule, predicted enzyme class (e.g., PT, AT, KS), and similarity score. Export the top-ranked pathway as a JSON file or visual graph for downstream analysis.

Protocol 4.2: BGC Identification and Analysis Using AntiSMASH

Objective: To identify and annotate biosynthetic gene clusters in a genomic sequence. Materials: FASTA file of the microbial genome or contig; AntiSMASH web server or standalone version. Procedure:

• Data Submission: Navigate to the AntiSMASH web server. Upload your genomic FASTA file.
• Analysis Selection: Select appropriate analysis options:
  • Enable all detection features (e.g., KnownClusterBlast, SubClusterBlast, RREfinder).
  • Specify the organism type (e.g., bacteria, fungi) for accurate pHMM databases.
• Run and Monitor: Submit the job. Processing time depends on genome size. Monitor via the provided job identifier.
• Interpretation: In the results page, visualize identified BGCs on the genomic map. Click on each cluster for detailed annotation: core biosynthetic genes, tailoring enzymes, predicted product class, and similarity to known BGCs. Use the "Compare MIBiG" feature to link to known NP analogs.

Protocol 4.3: Retrobiosynthesis in a Chassis Context Using RetroPathRL

Objective: To design a heterologous pathway for a target compound within a specific host organism. Materials: RetroPathRL environment (Docker image recommended); Target molecule SMILES; Chassis organism metabolic model (e.g., E. coli or S. cerevisiae in SBML format). Procedure:

• Environment Setup: Pull and run the RetroPathRL Docker container. Configure the workspace directories.
• Define Constraints: Prepare a configuration file specifying the host organism's metabolic model, permissible reaction rules (from its enzyme repository), and source metabolites (e.g., acetyl-CoA, malonyl-CoA).
• Launch Search: Execute the main script with the target SMILES and configuration file. The RL agent will explore the retrosynthetic space, evaluating steps against the host's metabolic network.
• Evaluate Pathways: The output provides a ranked list of pathways with integrated scores reflecting enzymatic feasibility, precursor availability, and estimated metabolic burden. Use the integrated visualization tools to inspect the pathway within the context of the host's metabolic network.

Visual Workflow and Pathway Diagrams

Title: Comparative Workflow for Three NP Pathway Design Tools

Title: BioNavi-NP Internal Prediction Algorithm Flow

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 3: Key Reagents and Computational Resources for NP Pathway Research

Item	Function in Experiments	Example/Supplier
Genomic DNA Kit	High-quality DNA extraction from microbial or plant samples for BGC mining via AntiSMASH.	Qiagen DNeasy Blood & Tissue Kit.
PCR Reagents	Amplification of putative BGCs identified in silico for cloning and validation.	NEB Q5 High-Fidelity DNA Polymerase.
Heterologous Host Strains	Chassis organisms for expressing predicted pathways (e.g., E. coli, S. cerevisiae).	E. coli BAP1, S. cerevisiae CEN.PK2.
Ligation-Free Cloning Kit	Assembly of multi-gene biosynthetic pathways into expression vectors.	Gibson Assembly Master Mix (NEB).
LC-MS/MS System	Analytical validation of NP production from engineered strains.	Thermo Scientific Orbitrap LC-MS.
Chemical Standards	Reference compounds for comparing retention times and mass spectra.	Sigma-Aldrich, Cayman Chemical.
Linux Workstation	Local execution of computationally intensive tools (BioNavi-NP, RetroPathRL).	64GB RAM, multi-core CPU recommended.
Docker Environment	Containerized, reproducible deployment of tool dependencies (RetroPathRL).	Docker Desktop.
Python/R Packages	For custom data analysis and visualization of pathway predictions.	RDKit, ggplot2, NetworkX.

Application Notes and Protocols

Within the BioNavi-NP framework for natural product pathway design, a critical phase is the experimental validation of in silico-predicted novel biosynthetic logic. This protocol details a multi-pronged strategy to assess the novelty of a hypothesized pathway and confirm its innovative enzymatic steps.

Core Strategy: The approach integrates heterologous expression with comparative metabolomics and in vitro enzymology. The putative gene cluster is expressed in a tractable host (e.g., S. albus or S. cerevisiae), and its metabolic output is compared against controls and databases. Key, unusual intermediates are targeted for isolation and feeding studies, while recombinant enzymes are characterized to elucidate novel catalytic mechanisms.

Protocol 1: Heterologous Expression and Comparative Metabolomic Profiling

Objective: To produce and compare the metabolite profile of the putative novel pathway against null mutants and known compound databases.

Materials:

• Expression Host: Streptomyces albus J1074 (or S. cerevisiae for fungal pathways).
• Vectors: pRM4-derived integrative vectors for Streptomyces.
• Growth Media: R5A agar/medium for Streptomyces; YPD for yeast.
• Analytical Instrumentation: UPLC-QTOF-MS (e.g., Waters Acquity I-Class / Xevo G2-XS).
• Software: MZmine 3 for data processing, GNPS for molecular networking, AntiSMASH for in silico analysis.

Procedure:

• Clone & Express: Assemble the full candidate biosynthetic gene cluster (BGC) via TAR cloning or Gibson assembly into an appropriate expression vector. Transform into the expression host. Include a "cluster-minus" control (e.g., a key enzyme knockout).
• Culture & Extract: Inoculate triplicate cultures of the expression strain and control. Incubate at 30°C for 5-7 days (Streptomyces). Extract metabolites with ethyl acetate (for non-polar/polar) and butanol (for polar).
• LC-MS Data Acquisition: Analyze extracts using a reverse-phase C18 column with a 5-95% acetonitrile (0.1% formic acid) gradient over 20 min. Use positive and negative electrospray ionization modes.
• Data Analysis:
  • Process raw data in MZmine 3: peak detection, alignment, gap filling.
  • Export feature lists (m/z, RT, intensity) for statistical analysis.
  • Upload to GNPS (Global Natural Product Social Molecular Networking) to create a molecular network. Compare networks between experimental and control strains.
  • Statistically significant, unique features in the experimental strain are candidates for novel pathway products or intermediates.

Table 1: Key Metabolomic Metrics for Novelty Assessment

Metric	Control Strain (Cluster -)	Experimental Strain (Cluster +)	Interpretation for Novelty
Total Spectral Features	150 ± 12	245 ± 18	Increased chemical space.
Unique Features (p<0.01)	5 (baseline)	48	High novelty potential.
GNPS Molecular Families	6	14	New chemical scaffolds.
Feature m/z Range	200-600 Da	200-1200 Da	Suggests production of larger/complex molecules.

Protocol 2: Stable Isotope Feeding and Intermediate Tracing

Objective: To confirm the predicted biosynthetic sequence and identify key, potentially novel intermediates.

Materials:

• Labeled Precursors: [1-¹³C]-Sodium acetate, [methyl-¹³C]-L-methionine, [U-¹³C₆]-D-Glucose.
• Isolation Tools: Preparative TLC/HPLC, NMR solvents (CD₃OD, DMSO-d₆).
• Instrumentation: High-resolution MS, 600 MHz NMR spectrometer.

Procedure:

• Feeding Experiment: Grow the expression strain in minimal media. At mid-log phase, supplement with 0.1% (w/v) of the labeled precursor (e.g., [1-¹³C]-acetate for polyketides). Incubate for 48-72h.
• Targeted Isolation: Based on unique features from Protocol 1, isolate putative novel intermediates using guided fractionation (LC-MS).
• Isotopic Pattern Analysis: Analyze purified intermediates by HRMS to detect mass shifts indicative of precursor incorporation (e.g., +1 Da per acetate unit).
• NMR Analysis: Perform ¹³C NMR on labeled and unlabeled compounds. The enrichment at specific carbons confirms their biochemical origin and maps the incorporation pattern onto the hypothesized pathway.

Protocol 3: In Vitro Enzymatic Characterization of Putative Novel Enzymes

Objective: To biochemically validate the function of an enzyme predicted to catalyze a novel transformation.

Materials:

• Expression System: E. coli BL21(DE3) for protein expression.
• Vectors: pET-28a(+) for His-tag purification.
• Chromatography: Ni-NTA affinity resin, FPLC system for size-exclusion.
• Assay Reagents: Purified substrate (from Protocol 2), NAD(P)H, SAM, etc., as cofactors.
• Analysis: LC-MS, spectrophotometer.

Procedure:

• Protein Expression & Purification: Clone the target gene into pET-28a(+). Express in E. coli with 0.5 mM IPTG at 18°C for 16h. Purify using Ni-NTA and size-exclusion chromatography.
• Enzyme Assay: Set up 100 µL reactions containing 50 mM Tris-HCl (pH 7.5), 10 µM substrate, 1 mM cofactor, and 5-10 µg of purified enzyme. Incubate at 30°C for 30 min. Quench with equal volume of methanol.
• Product Analysis: Analyze quenched reactions by LC-MS (as in Protocol 1). Compare to no-enzyme and heat-inactivated enzyme controls.
• Kinetic Analysis: Vary substrate concentration (e.g., 1-100 µM) to determine Michaelis-Menten constants (Kₘ, Vₘₐₓ), confirming catalytic efficiency.

The Scientist's Toolkit: Key Research Reagent Solutions

Item	Function in Novelty Assessment
Heterologous Expression Host (S. albus J1074)	Clean metabolic background, high transformation efficiency, supports expression of diverse BGCs.
pRM4 Vector Series	Integrative Streptomyces vectors with strong, constitutive promoters for stable BGC expression.
UPLC-QTOF-MS System	Provides high-resolution, accurate mass data for untargeted metabolomics and feature discovery.
GNPS Platform	Enables comparative molecular networking to visually identify unique chemotypes.
Stable Isotope-Labeled Precursors	Allows atom-by-atom tracing of biosynthetic routes, confirming predicted biochemistry.
Ni-NTA Affinity Resin	Enables rapid, one-step purification of His-tagged recombinant enzymes for in vitro assays.

Visualizations

Title: Workflow for Validating Novel Biosynthetic Pathways

Title: Logic Tree for Assessing Biosynthetic Route Novelty

Application Note AN-101: De Novo Design of an Antifungal Depside Pathway

Thesis Context: This application note demonstrates the initial pathway enumeration and prioritization module of BioNavi-NP, a core tutorial step for generating novel biosynthetic logic.

Case Study Summary: Researchers aimed to design a pathway for the synthesis of a novel depside compound with predicted antifungal activity. Starting from common acyl-CoA precursors, BioNavi-NP enumerated over 50 potential enzymatic routes. The top-ranked pathway, requiring only three engineered steps, was experimentally constructed in S. cerevisiae.

Key Quantitative Results:

Table 1: Pathway Enumeration and Ranking Metrics for Depside Design

Metric	Value
Total Pathways Enumerated	52
Top Pathway Predicted Yield	78 mg/L
Number of Enzymatic Steps (Top Pathway)	3
Heterologous Host	Saccharomyces cerevisiae
Experimental Titer Achieved	65 mg/L
Antifungal Activity (MIC vs. C. albicans)	8 µg/mL

Detailed Protocol: Pathway Construction and Screening in Yeast

• Strain Engineering:

  • Transform S. cerevisiae BY4741 with a Gal1/10 promoter-integrated landing pad using lithium acetate/single-stranded carrier DNA/PEG method.
  • Assemble the three-gene pathway (acyltransferase, esterase, O-methyltransferase) into a single yeast integrative vector via Golden Gate assembly.
  • Integrate the expression cassette into the engineered locus via CRISPR-Cas9 mediated homology-directed repair. Select on SC-Ura plates.

• Fermentation and Metabolite Analysis:

  • Inoculate single colonies in 5 mL SC-Ura + 2% glucose, incubate at 30°C, 250 rpm for 24h.
  • Subculture to OD600=0.1 in 50 mL SC-Ura + 2% galactose (induction) in a 250 mL baffled flask.
  • Culture for 96h at 30°C, 250 rpm. Harvest 1 mL aliquots every 24h for analysis.
  • Extract metabolites from cell pellets using 80% methanol, vortex for 10 min, centrifuge at 15,000g for 10 min.
  • Analyze supernatant via LC-MS (C18 column, water/acetonitrile + 0.1% formic acid gradient). Quantify target depside against a purified standard curve.

• Antifungal Bioassay:

  • Prepare a 96-well plate with RPMI 1640 medium, pH 7.0.
  • Serially dilute purified compound (2-fold) across rows. Final concentration range: 0.5–64 µg/mL.
  • Inoculate each well with C. albicans SC5314 suspension (1–5 x 10^3 CFU/mL).
  • Incubate at 35°C for 48h. Measure OD600. MIC is the lowest concentration with ≥90% growth inhibition.

Diagram: Depside Pathway Design Workflow

Research Reagent Solutions:

Item	Function & Rationale
S. cerevisiae BY4741	Model eukaryotic host with well-characterized genetics for fungal pathway expression.
Gal1/10 Inducible Promoter System	Tight, galactose-induced control of pathway gene expression to prevent host burden during growth.
Yeast Integrative Vector (pRS40x series)	Stable genomic integration for consistent gene expression without plasmid loss.
Golden Gate Assembly Mix (BsaI-HFv2)	Enables rapid, seamless, and ordered assembly of multiple genetic parts.
CRISPR-Cas9 Plasmid (pCAS-YSB)	Enables precise genomic integration of the assembled pathway cassette.
C18 Reverse-Phase LC Column	Standard for separating medium-polarity natural products like depsides.

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Application Note AN-102: Retrosynthetic Engineering of a Plant Diterpenoid

Thesis Context: This note illustrates the retrosynthetic pathway dissection and host-specific enzyme prediction features of BioNavi-NP, crucial for redesigning complex pathways.

Case Study Summary: To produce the bioactive diterpenoid ent-kaur-16-en-19-oic acid in E. coli, BioNavi-NP was used to deconstruct the pathway from its native plant source. It identified a taxadiene synthase homolog as a superior alternative to the native ent-copalyl diphosphate/kaurene synthase, optimizing the early cyclization steps for a prokaryotic host.

Key Quantitative Results:

Table 2: Production Metrics for Engineered Diterpenoid Pathway

Metric	Native Plant Extract	Initial E. coli Build	BioNavi-NP Optimized Build
Strain	Stevia rebaudiana	BL21(DE3) + Plant Genes	BL21(DE3) + Optimized Genes
Key Enzyme	ent-CPS/KS	ent-CPS/KS	Taxadiene Synthase Homolog
Titer (mg/L)	0.05 (in planta)	1.2	112
Fermentation Time	3 months (growth)	72 hours	48 hours
Downstream Product Yield	0.001% dry weight	0.8% cell extract	15% cell extract

Detailed Protocol: Pathway Optimization and Production in E. coli

• Retrosynthetic Analysis:

  • Input the SMILES string of ent-kaurene into BioNavi-NP's "RetroBiosynth" module.
  • Set parameters: Maximum steps=6, Allow non-native reactions=Yes, Host Organism=Escherichia coli.
  • Export the top 5 suggested precursor pathways and their enzyme candidates.

• Enzyme Library Construction & Screening:

  • Clone candidate diterpene synthase genes (plant ent-CPS/KS and 3 homologs) into pET-28a(+) with an N-terminal His-tag.
  • Co-transform each with pTrc-ispA (to enhance FPP precursor) into E. coli BL21(DE3).
  • Induce expression in 2xYT medium with 0.5 mM IPTG at 18°C for 20h.
  • Extract metabolites from whole culture with ethyl acetate. Analyze by GC-MS for ent-kaurene production.

• Scale-up and Oxidation:

  • Scale the best-producing strain in a 2L bioreactor with fed-batch strategy (glycerol feed).
  • After 48h, induce with IPTG and supplement with sodium pyruvate.
  • Harvest cells by centrifugation. Lyse via sonication.
  • Incubate crude lysate with a recombinant P450 (CYP714A2) and NADPH regeneration system (glucose-6-phosphate, G6PDH) for 6h at 30°C to oxidize ent-kaurene to the target acid.

Diagram: Retrosynthetic Pathway Engineering Logic

Research Reagent Solutions:

Item	Function & Rationale
E. coli BL21(DE3)	Robust prokaryotic host for high-level, inducible expression of heterologous pathways.
pET-28a(+) Expression Vector	Strong T7 promoter for high protein yield; His-tag simplifies enzyme purification.
pTrc-ispA Plasmid	Constitutive expression of FPP synthase to boost universal diterpenoid precursor supply.
GC-MS System w/ HP-5ms Column	Ideal for separation and identification of volatile diterpene hydrocarbons like ent-kaurene.
NADPH Regeneration System	Maintains cofactor supply for P450 enzymes in vitro, critical for oxidation steps.
Fed-Batch Bioreactor System	Enables high-density cultivation of E. coli, maximizing precursor availability and final titer.

Within the BioNavi-NP tutorial framework for natural product pathway design, a critical component is the explicit acknowledgment of the platform's predictive limitations. This document details the current boundaries of BioNavi-NP's computational predictions, providing application notes and experimental protocols for empirical validation.

Current benchmarking data (as of 2024) highlights key areas where predictive accuracy diverges from experimental validation.

Table 1: BioNavi-NP Predictive Accuracy Across Compound Classes

Compound Class	Prediction Scope	Avg. Pathway Completion Accuracy	Experimental Validation Rate (Typical)	Primary Limitation Factor
Non-Ribosomal Peptides (NRPs)	Monomer selection, linear assembly	92%	85-90%	Tailoring enzyme specificity
Polyketides (Type I)	Module ordering, starter/extender unit prediction	88%	75-82%	Stereochemistry, module skipping
Terpenes	Backbone scaffold generation	95%	88-92%	Cyclization regioselectivity
Hybrid NPR-PKS	Domain fusion, communication	78%	60-70%	Inter-protein linkers, docking
Highly Glycosylated NPs	Glycosyltransferase (GT) donor/acceptor prediction	70%	50-65%	GT promiscuity, sugar activation

Table 2: Factors Contributing to Prediction-Experiment Gaps

Factor	Impact on Prediction (%)	Mitigation Protocol Section
Enzyme Promiscuity/Unspecificity	25-40%	3.1
Uncharacterized or "Missing" Enzymes in DB	30-50%	3.2
Subcellular Compartmentalization Not Modeled	15-25%	3.3
Allosteric Regulation & Metabolic Context	20-35%	3.4
Chassis-Specific Toxicity/Interference	10-30%	3.5

Experimental Validation Protocols

Protocol 3.1: Validating Enzyme Substrate Promiscuity Predictions

Objective: Empirically test the substrate range of an enzyme predicted by BioNavi-NP to act on a novel intermediate. Materials:

• Purified recombinant enzyme (from predicted family).
• Putative substrate analogs (synthesized or purchased).
• LC-MS/MS system (e.g., Thermo Scientific Q Exactive).
• Reaction buffer (as per enzyme class). Procedure:

• Set up 50 µL reactions containing buffer, co-factors, and 100 µM substrate analog.
• Initiate reaction with 5 µg of purified enzyme. Incubate at optimal temp (e.g., 30°C).
• Quench aliquots at 0, 5, 15, 30, 60 min with 50 µL cold methanol.
• Centrifuge, analyze supernatant by LC-MS. Monitor for mass shift corresponding to predicted transformation (e.g., +C2H2O for acetylation).
• Compare kinetics (Vmax, Km) across analogs to quantify promiscuity versus specificity.

Protocol 3.2: Filling "Missing Enzyme" Gaps via Homology Mining

Objective: Identify candidate enzymes for uncharacterized steps in a BioNavi-NP-proposed pathway. Materials:

• Genomic DNA from phylogenetically related organisms.
• Degenerate PCR primers designed from conserved domains.
• Heterologous expression host (e.g., S. albus J1074).
• Standard molecular biology reagents. Procedure:

• Use BioNavi-NP's "Neighborhood Analysis" output to identify conserved biosynthetic gene cluster (BGC) contexts.
• Design degenerate primers targeting conserved motifs (e.g., for acyltransferase domains).
• Perform PCR on genomic DNA from candidate producer strains. Clone products into expression vector.
• Co-express candidate gene with accumulated upstream intermediate in heterologous host.
• Extract metabolites and screen for new product formation via LC-HRMS.

Protocol 3.3: Compartmentalization Validation via Subcellular Fractionation

Objective: Determine if pathway enzymes localize to organelles, affecting intermediate channeling. Materials:

• Fungal or plant tissue culture.
• Differential centrifugation kit (e.g., Cell.ytic PN).
• Organelle-specific markers (antibodies or enzyme assays).
• Confocal microscopy setup with GFP-tagging capability. Procedure:

• Tag predicted enzyme with GFP using standard cloning.
• Transform host, observe localization via confocal microscopy.
• For biochemical validation, homogenize cells in isotonic buffer.
• Perform differential centrifugation to isolate nuclei, mitochondria, microsomes, cytosol.
• Assay each fraction for tagged enzyme (Western) and catalytic activity. Correlate with organelle markers.

Mandatory Visualizations

Diagram Title: Predictive Limitations and Empirical Validation Pathways

Diagram Title: Decision Workflow for Addressing Prediction Gaps

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Reagents for Validating BioNavi-NP Predictions

Reagent / Solution	Function in Validation	Example Product / Vendor
Heterologous Chassis Strains	Provide a clean background for pathway expression and intermediate feeding.	Streptomyces albus J1074 (BRENDA), Saccharomyces cerevisiae BY4741 (ATCC).
Broad-Substrate Cofactor Pools	Support activity of promiscuous enzymes (e.g., ATs, GTs) in vitro.	10x Cofactor Mix (ATP, NADPH, SAM, Acetyl-CoA), Sigma-Aldrich.
Stable Isotope-Labeled Precursors	Trace predicted carbon flux through pathway steps.	1,2-¹³C-Acetate, U-¹³C-Glucose (Cambridge Isotope Laboratories).
Activity-Based Protein Profiling (ABPP) Probes	Chemically profile enzyme functional states in native context.	Fluorophosphonate-TAMRA (for hydrolases), ActivX Probes (Thermo Fisher).
LC-HRMS Metabolomics Standards	Quantify novel intermediates and shunt products for yield analysis.	Supeleo Analytical Metabolomics Kit, Natural Product Library (IROA Technologies).

Conclusion

BioNavi-NP represents a transformative tool in the computational toolkit for natural product research, democratizing access to sophisticated retrobiosynthetic planning. This tutorial has guided users from foundational exploration through practical application, troubleshooting, and rigorous validation. By mastering BioNavi-NP, researchers can significantly accelerate the hypothesis generation phase of drug discovery, proposing biologically plausible pathways for novel or complex molecules with unprecedented speed. The future lies in the tighter integration of such AI platforms with robotic strain engineering and high-throughput metabolomics, promising a new era of data-driven, AI-accelerated natural product development for addressing unmet clinical needs. Future directions should focus on expanding the platform's rule set for non-canonical chemistry and improving its interoperability with genomic databases for direct host organism recommendation.