gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/adaptyv/reference/experiments.md

Experiment Types and Workflows

Overview

Adaptyv provides multiple experimental assay types for comprehensive protein characterization. Each experiment type has specific applications, workflows, and data outputs.

Binding Assays

Description

Measure protein-target interactions using biolayer interferometry (BLI), a label-free technique that monitors biomolecular binding in real-time.

Use Cases

• Antibody-antigen binding characterization
• Receptor-ligand interaction analysis
• Protein-protein interaction studies
• Affinity maturation screening
• Epitope binning experiments

Technology: Biolayer Interferometry (BLI)

BLI measures the interference pattern of reflected light from two surfaces:

• Reference layer - Biosensor tip surface
• Biological layer - Accumulated bound molecules

As molecules bind, the optical thickness increases, causing a wavelength shift proportional to binding.

Advantages:

• Label-free detection
• Real-time kinetics
• High-throughput compatible
• Works in crude samples
• Minimal sample consumption

Measured Parameters

Kinetic constants:

• KD - Equilibrium dissociation constant (binding affinity)
• kon - Association rate constant (binding speed)
• koff - Dissociation rate constant (unbinding speed)

Typical ranges:

• Strong binders: KD < 1 nM
• Moderate binders: KD = 1-100 nM
• Weak binders: KD > 100 nM

Workflow

1. Sequence submission - Provide protein sequences in FASTA format
2. Expression - Proteins expressed in appropriate host system
3. Purification - Automated purification protocols
4. BLI assay - Real-time binding measurements against specified targets
5. Analysis - Kinetic curve fitting and quality assessment
6. Results delivery - Binding parameters with confidence metrics

Sample Requirements

• Protein sequence (standard amino acid codes)
• Target specification (from catalog or custom request)
• Buffer conditions (standard or custom)
• Expected concentration range (optional, improves assay design)

Results Format

{
  "sequence_id": "antibody_variant_1",
  "target": "Human PD-L1",
  "measurements": {
    "kd": 2.5e-9,
    "kd_error": 0.3e-9,
    "kon": 1.8e5,
    "kon_error": 0.2e5,
    "koff": 4.5e-4,
    "koff_error": 0.5e-4
  },
  "quality_metrics": {
    "confidence": "high|medium|low",
    "r_squared": 0.97,
    "chi_squared": 0.02,
    "flags": []
  },
  "raw_data_url": "https://..."
}

Expression Testing

Description

Quantify protein expression levels in various host systems to assess producibility and optimize sequences for manufacturing.

Use Cases

• Screening variants for high expression
• Optimizing codon usage
• Identifying expression bottlenecks
• Selecting candidates for scale-up
• Comparing expression systems

Host Systems

Available expression platforms:

• E. coli - Rapid, cost-effective, prokaryotic system
• Mammalian cells - Native post-translational modifications
• Yeast - Eukaryotic system with simpler growth requirements
• Insect cells - Alternative eukaryotic platform

Measured Parameters

• Total protein yield (mg/L culture)
• Soluble fraction (percentage)
• Purity (after initial purification)
• Expression time course (optional)

Workflow

1. Sequence submission - Provide protein sequences
2. Construct generation - Cloning into expression vectors
3. Expression - Culture in specified host system
4. Quantification - Protein measurement via multiple methods
5. Analysis - Expression level comparison and ranking
6. Results delivery - Yield data and recommendations

Results Format

{
  "sequence_id": "variant_1",
  "host_system": "E. coli",
  "measurements": {
    "total_yield_mg_per_l": 25.5,
    "soluble_fraction_percent": 78,
    "purity_percent": 92
  },
  "ranking": {
    "percentile": 85,
    "notes": "High expression, good solubility"
  }
}

Thermostability Testing

Description

Measure protein thermal stability to assess structural integrity, predict shelf-life, and identify stabilizing mutations.

Use Cases

• Selecting thermally stable variants
• Formulation development
• Shelf-life prediction
• Stability-driven protein engineering
• Quality control screening

Measurement Techniques

Differential Scanning Fluorimetry (DSF):

• Monitors protein unfolding via fluorescent dye binding
• Determines melting temperature (Tm)
• High-throughput capable

Circular Dichroism (CD):

• Secondary structure analysis
• Thermal unfolding curves
• Reversibility assessment

Measured Parameters

• Tm - Melting temperature (midpoint of unfolding)
• ΔH - Enthalpy of unfolding
• Aggregation temperature (Tagg)
• Reversibility - Refolding after heating

Workflow

1. Sequence submission - Provide protein sequences
2. Expression and purification - Standard protocols
3. Thermostability assay - Temperature gradient analysis
4. Data analysis - Curve fitting and parameter extraction
5. Results delivery - Stability metrics with ranking

Results Format

{
  "sequence_id": "variant_1",
  "measurements": {
    "tm_celsius": 68.5,
    "tm_error": 0.5,
    "tagg_celsius": 72.0,
    "reversibility_percent": 85
  },
  "quality_metrics": {
    "curve_quality": "excellent",
    "cooperativity": "two-state"
  }
}

Enzyme Activity Assays

Description

Measure enzymatic function including substrate turnover, catalytic efficiency, and inhibitor sensitivity.

Use Cases

• Screening enzyme variants for improved activity
• Substrate specificity profiling
• Inhibitor testing
• pH and temperature optimization
• Mechanistic studies

Assay Types

Continuous assays:

• Chromogenic substrates
• Fluorogenic substrates
• Real-time monitoring

Endpoint assays:

• HPLC quantification
• Mass spectrometry
• Colorimetric detection

Measured Parameters

Kinetic parameters:

• kcat - Turnover number (catalytic rate constant)
• KM - Michaelis constant (substrate affinity)
• kcat/KM - Catalytic efficiency
• IC50 - Inhibitor concentration for 50% inhibition

Activity metrics:

• Specific activity (units/mg protein)
• Relative activity vs. reference
• Substrate specificity profile

Workflow

1. Sequence submission - Provide enzyme sequences
2. Expression and purification - Optimized for activity retention
3. Activity assay - Substrate turnover measurements
4. Kinetic analysis - Michaelis-Menten fitting
5. Results delivery - Kinetic parameters and rankings

Results Format

{
  "sequence_id": "enzyme_variant_1",
  "substrate": "substrate_name",
  "measurements": {
    "kcat_per_second": 125,
    "km_micromolar": 45,
    "kcat_km": 2.8,
    "specific_activity": 180
  },
  "quality_metrics": {
    "confidence": "high",
    "r_squared": 0.99
  },
  "ranking": {
    "relative_activity": 1.8,
    "improvement_vs_wildtype": "80%"
  }
}

Experiment Design Best Practices

Sequence Submission

1. Use clear identifiers - Name sequences descriptively
2. Include controls - Submit wild-type or reference sequences
3. Batch similar variants - Group related sequences in single submission
4. Validate sequences - Check for errors before submission

Sample Size

• Pilot studies - 5-10 sequences to test feasibility
• Library screening - 50-500 sequences for variant exploration
• Focused optimization - 10-50 sequences for fine-tuning
• Large-scale campaigns - 500+ sequences for ML-driven design

Quality Control

Adaptyv includes automated QC steps:

• Expression verification before assay
• Replicate measurements for reliability
• Positive/negative controls in each batch
• Statistical validation of results

Timeline Expectations

Standard turnaround: ~21 days from submission to results

Timeline breakdown:

• Construct generation: 3-5 days
• Expression: 5-7 days
• Purification: 2-3 days
• Assay execution: 3-5 days
• Analysis and QC: 2-3 days

Factors affecting timeline:

• Custom targets (add 1-2 weeks)
• Novel assay development (add 2-4 weeks)
• Large batch sizes (may add 1 week)

Cost Optimization

1. Batch submissions - Lower per-sequence cost
2. Standard targets - Catalog antigens are faster/cheaper
3. Standard conditions - Custom buffers add cost
4. Computational pre-filtering - Submit only promising candidates

Combining Experiment Types

For comprehensive protein characterization, combine multiple assays:

Therapeutic antibody development:

1. Binding assay → Identify high-affinity binders
2. Expression testing → Select manufacturable candidates
3. Thermostability → Ensure formulation stability

Enzyme engineering:

1. Activity assay → Screen for improved catalysis
2. Expression testing → Ensure producibility
3. Thermostability → Validate industrial robustness

Sequential vs. Parallel:

• Sequential - Use results from early assays to filter candidates
• Parallel - Run all assays simultaneously for faster results

Data Integration

Results integrate with computational workflows:

1. Download raw data via API
2. Parse results into standardized format
3. Feed into ML models for next-round design
4. Track experiments with metadata tags
5. Visualize trends across design iterations

Support and Troubleshooting

Common issues:

• Low expression → Consider sequence optimization (see protein_optimization.md)
• Poor binding → Verify target specification and expected range
• Variable results → Check sequence quality and controls
• Incomplete data → Contact support with experiment ID

Getting help:

• Email: support@adaptyvbio.com
• Include experiment ID and specific question
• Provide context (design goals, expected results)
• Response time: <24 hours for active experiments