Recombinant Conus radiatus Iota-conotoxin-like R11.10

What is the structural classification of Recombinant Conus radiatus Iota-conotoxin-like R11.10?

Recombinant Conus radiatus Iota-conotoxin-like R11.10 belongs to the I1-superfamily of conotoxins, characterized by eight cysteine residues arranged in a distinctive -C-C-CC-CC-C-C- pattern. This cysteine framework is crucial for the peptide's three-dimensional structure and biological activity. Like other members of this family, R11.10 is structurally related to characterized peptides such as ι-RXIA and R11.5, sharing the conserved cysteine framework that forms the structural backbone of these neurotoxins .

To experimentally confirm this classification:

• Perform sequence alignment with other I1-superfamily members

• Verify disulfide connectivity using partial reduction and alkylation followed by MS/MS analysis

• Conduct circular dichroism (CD) spectroscopy to analyze secondary structure elements

What are the primary molecular targets of Iota-conotoxins from Conus radiatus?

Iota-conotoxins from Conus radiatus primarily target voltage-gated sodium (Nav) channels, functioning as excitotoxins by shifting the voltage dependence of activation to hyperpolarized potentials . Based on homology to characterized family members, R11.10 likely targets several Nav channel subtypes with varying affinities:

The pharmacological profile can be determined using:

• Two-electrode voltage clamp in Xenopus oocytes expressing different Nav channel subtypes

• Patch-clamp electrophysiology in mammalian cells expressing human Nav channels

• Competitive binding assays with radiolabeled channel modulators

What is the mechanism of action for Iota-conotoxins like R11.10?

Iota-conotoxins modify Nav channel gating by shifting the voltage dependence of activation to more hyperpolarized potentials (typically by −10 to −15 mV) . This mechanism differs from pore-blocking toxins and instead resembles β-scorpion toxins . The functional consequence is:

• Channels open at voltages that would normally be subthreshold

• Increased neuronal excitability leading to repetitive action potentials

• Potential induction of seizures when administered intracranially to mice

To experimentally characterize this mechanism:

• Generate conductance-voltage (G-V) curves in the presence and absence of the toxin

• Measure the shift in half-maximal activation voltage (V1/2)

• Assess impacts on channel inactivation kinetics and voltage dependence

• Evaluate effects on recovery from inactivation

What expression systems are recommended for recombinant production of Iota-conotoxins?

Two primary expression systems have proven effective for recombinant Iota-conotoxin production:

Yeast Expression System:

• Suitable for R11.5 and likely R11.10 production

• Advantages: Post-translational modification capabilities, secretion of soluble product

• Parameters: Typical yield ranges not specified in available data

E. coli Expression System:

• Often using fusion protein approaches (similar to methods for α-conotoxin LvIA)

• Fusion partners: KSI (ketosteroid isomerase) with His6 tag for purification

• Tandem repeat strategy to enhance stability and yield

• Yields of 100-500 mg/L culture for fusion protein reported for other conotoxins

Post-expression processing is critical:

• Chemical cleavage (e.g., CNBr) to release the peptide from fusion partners

• Oxidative folding to establish the correct disulfide bond pattern

• HPLC purification and MS verification of molecular weight

How should researchers evaluate the purity and identity of recombinant R11.10?

Multi-method validation is essential:

Analytical Techniques:

• RP-HPLC: Target purity >85% (SDS-PAGE) for research applications

• ESI-MS or MALDI-TOF: Verify molecular weight (expected 4 Da less than linear peptide due to two disulfide bonds)

• Circular dichroism: Confirm secondary structure elements

• NMR: Advanced structural validation when needed

Quality Control Parameters:

• Disulfide bond formation confirmation: Compare reduced vs. non-reduced samples by MS

• Bioactivity testing: Functional assay on target Nav channels

• Stability assessment: Monitor integrity after storage in various conditions (lyophilized form should maintain stability for 12 months at -20°C/-80°C)

How do post-translational modifications affect the bioactivity of Iota-conotoxins?

Post-translational modifications significantly impact Iota-conotoxin bioactivity, with D-amino acid incorporation being particularly critical. For example:

D-Phenylalanine in ι-RXIA:

• Position 44 D-Phe is crucial for bioactivity

• The D-Phe-containing peptide shows significantly higher excitotoxic potency than the L-Phe analog

• Crystallography reveals subtle structural alterations that enhance binding

Experimental approaches to study modification impacts:

• Site-directed mutagenesis to create modified variants

• Comparison of synthetic peptides with specific modifications

• Electrophysiological characterization of each variant's activity

• Binding kinetics studies (using BLI or SPR) to determine kon and koff rates

• Thermal stability assays to assess structural impacts

Research data from ι-RXIA shows that the D-Phe variant has approximately two-fold higher affinity and two-fold slower off-rate than the L-Phe variant on Nav1.6, and the L-Phe variant loses activity on Nav1.2 completely .

What methodologies are most effective for evaluating channel subtype selectivity?

A comprehensive evaluation of channel subtype selectivity requires multiple approaches:

Electrophysiological Methods:

• Two-electrode voltage clamp in Xenopus oocytes:

  • Advantages: Robust expression of multiple channel subtypes

  • Applications: Initial selectivity screening, concentration-response relationships

  • Analysis: Calculate EC50 values for voltage shift or % inhibition for each subtype

  • Expected Results: For related peptides like ι-RXIA, selectivity order is Nav1.6 > Nav1.2 > Nav1.7

• Patch-clamp in mammalian cells:

  • Advantages: Native-like cellular environment, human channel variants

  • Applications: Detailed kinetic analysis, state-dependent interactions

  • Analysis: Determine binding rates (kon and koff) and state preferences

Binding Assays:

• Displacement of radiolabeled ligands:

  • Applications: High-throughput screening across multiple subtypes

  • Limitations: Indirect measure of functional impact

Ex Vivo and In Vivo Validation:

• Mouse sciatic nerve compound action potentials:

  • Applications: Assess effects on myelinated (A-fibers) vs. unmyelinated (C-fibers) axons

  • Expected results: Induction of repetitive action potentials with conduction velocities reflecting affected fiber types

• Mouse behavioral models:

  • Applications: Validate functional consequences of channel modulation

  • Metrics: Seizure activity, pain responses, motor function

What are the challenges in maintaining native disulfide bond patterns in recombinant Iota-conotoxins?

Maintaining the correct disulfide connectivity presents significant challenges:

Challenges and Solutions:

• Multiple potential isomers:

  • Eight cysteines can theoretically form 105 different disulfide isomers

  • Directed folding strategies using orthogonal protection groups may be necessary

  • Oxidative folding under optimized conditions (buffer, pH, redox agents) favors thermodynamically stable native conformations

• Verification methods:

  • Partial reduction and alkylation followed by MS/MS analysis

  • Comparison with native peptide standards

  • Functional assays to confirm bioactivity of the folded product

• Expression system considerations:

  • Yeast systems may provide better disulfide formation environment than E. coli

  • Co-expression with disulfide isomerases can enhance correct folding

  • Periplasmic targeting in E. coli enhances disulfide formation

Practical approach:
For R11.10 and related peptides, reconstitution recommendations include using deionized sterile water with 5–50% glycerol to maintain stability and prevent disulfide scrambling.

How can researchers integrate electrophysiological data with structural insights for drug development applications?

Integrating structural and functional data requires a systematic approach:

Comprehensive Framework:

• Structure-function relationship mapping:

  • Create an array of point mutations or chimeric constructs

  • Measure changes in electrophysiological parameters (voltage shift, binding kinetics)

  • Correlate with structural elements (using NMR or X-ray data)

  • Identify the pharmacophore responsible for subtype selectivity

• Molecular dynamics simulations:

  • Dock conotoxin to homology models of different Nav channel subtypes

  • Simulate binding interactions and energy landscapes

  • Validate predictions with mutagenesis experiments

• Rational design cycle:

  • Use integrated data to design peptide variants with enhanced properties

  • Synthesize/express variants and test electrophysiological parameters

  • Refine structural models based on functional outcomes

  • Iterate to optimize therapeutic index

Case Example from Related Peptides:
κM-RIIIJ from C. radiatus (potassium channel modulator) was optimized through extensive selectivity mapping, leading to identification of asymmetric channel complexes as high-affinity targets. This insight enabled classification of somatosensory neuron subclasses based on their distinctive potassium channel signatures .

How does the voltage-dependent mechanism of R11.10 differ from other sodium channel-targeting conotoxins?

Iota-conotoxins employ a distinctive mechanism compared to other Nav-targeting conotoxins:

Comparative Mechanistic Analysis:

Experimental approach to distinguish mechanisms:

• Voltage protocol comparisons:

  • Holding potential dependence

  • Use-dependence characteristics

  • Recovery from inactivation protocols

  • Voltage ramp protocols to detect shifts in activation threshold

• Competition assays with site-specific ligands:

  • TTX (site 1)

  • Batrachotoxin (site 2)

  • α-scorpion toxins (site 3)

  • β-scorpion toxins (site 4)

The ι-conotoxin mechanism most closely resembles that of β-scorpion toxins, acting as gating modifiers rather than channel blockers .

What are the optimal neurophysiological preparations for studying R11.10 effects in complex neural circuits?

To understand R11.10 effects beyond single-cell electrophysiology, several preparations are recommended:

Ex Vivo Preparations:

• Mouse sciatic nerve recording:

  • Advantages: Preserved axonal architecture, multiple fiber types

  • Measurements: Compound action potentials, conduction velocities

  • Expected results: Repetitive firing in both A- and C-fibers due to Nav1.6 presence in both myelinated and unmyelinated axons

• Hippocampal/cortical slice preparations:

  • Advantages: Preserved neural circuits, regional differences in Nav distribution

  • Measurements: Field potentials, local circuit activity

  • Expected effects: Enhanced excitability, potential seizure-like activity

In Vitro Network Models:

• Multi-electrode array recordings from neuronal cultures:

  • Advantages: Network-level activity, long-term recordings

  • Measurements: Burst frequency, synchronization, propagation patterns

  • Analysis: Graph theory metrics to quantify network perturbations

Practical considerations:

• Include TTX controls to confirm Nav channel involvement

• Use selective blockers of other channels (Kv, Cav) to isolate specific contributions

• Compare effects at different concentrations to establish dose-response relationships

• Consider species differences in Nav channel distribution and sensitivity

How can machine learning approaches enhance conotoxin research and the development of R11.10 analogs?

Machine learning (ML) offers powerful tools for advancing R11.10 research:

ML Applications in Conotoxin Research:

• Structure-activity relationship prediction:

  • Train models using electrophysiological data from related conotoxins

  • Predict activity of novel R11.10 variants before synthesis

  • Feature importance analysis to identify critical residues

• Target selectivity optimization:

  • Use classification algorithms to predict subtype selectivity profiles

  • Design focused libraries with enhanced selectivity

• De novo peptide design:

  • Generate novel sequences maintaining the critical I1-superfamily scaffold

  • Optimize for specific properties (stability, selectivity, BBB penetration)

Implementation Strategy:

• Database integration: Combine structured data on sequence, structure, and functional parameters

• Feature extraction: Encode physicochemical properties, structural elements

• Model development: Test various algorithms (random forests, neural networks, support vector machines)

• Experimental validation: Synthesize top candidates for biological testing

• Feedback loop: Retrain models with new experimental data

Recent advances have enabled integration of multimodal data and refinement of predictive frameworks to enhance the therapeutic potential of conotoxins like R11.10 .