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Binding Characterization

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FreedomIntelligence
binding-characterization

Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.

Overview

PublisherFreedomIntelligence
RepositoryOpenClaw-Medical-Skills
Skill namebinding-characterization
Stars
3K
Forks
410
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by FreedomIntelligence on GitHub. Read the source before you install it.

Installation

Install the Binding Characterization AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills.git /tmp/OpenClaw-Medical-Skills
mkdir -p .claude/skills
cp -r /tmp/OpenClaw-Medical-Skills/skills/binding-characterization .claude/skills/binding-characterization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Binding Characterization in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Binding Characterization on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Binding Characterization is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Binding Characterization: SPR and BLI

SPR vs BLI Decision Matrix

FactorChoose SPRChoose BLI
SensitivitySmall molecules, fragments (<500 Da)Large complexes, antibodies
ThroughputLow-medium (serial)High (96-well parallel)
Sample purityRequired (clogs fluidics)Tolerates crude lysates
Kinetic resolutionHigher (better for fast kinetics)Lower
Mass transportMore sensitive (may distort kon)Less sensitive
MaintenanceHigh (fluidics system)Low (dip-and-read)
Sample consumptionHigher (continuous flow)Lower
Cost per experimentLower chip cost, higher run costHigher tip cost, lower run cost

Key differences

SPR (Surface Plasmon Resonance)

  • Mechanism: Detects refractive index changes at gold surface
  • Surface: Gold chip with dextran matrix (CM5, CM7, etc.)
  • Flow: Continuous microfluidics
  • Best for: Small molecules, high-affinity, precise kon/koff

BLI (Biolayer Interferometry)

  • Mechanism: Measures optical interference pattern shift
  • Surface: Fiber optic biosensor tips (SA, Ni-NTA, AHC)
  • Flow: Dip-and-read (no microfluidics)
  • Best for: High-throughput, crude samples, antibody screening

Troubleshooting: Why BLI works but SPR doesn't

CauseMechanismSolution
Hydrophobic CDRsAdsorb to SPR gold/dextran surfaceAdd 0.05% Tween-20, use CM7 chip with longer dextran
AggregationMass transport artifacts in SPR fluidicsFilter sample (0.22μm), reduce ligand density
High instabilityDegrades during continuous flowShorter cycle time, add stabilizers (trehalose 5%)
Charge mismatchNonspecific binding to charged dextranAdjust buffer pH ±1 from pI, add BSA 1mg/mL
Slow dissociationLong regeneration needed (damages ligand)Use BLI (disposable tips)

Why SPR works but BLI doesn't

CauseMechanismSolution
Small analyteBLI less sensitive for <10 kDaUse SPR with appropriate chip
Weak affinity (KD >10μM)Fast dissociation in BLI dipIncrease analyte concentration
Low expressionNot enough signalIncrease biosensor loading

Mass transport considerations

Mass transport limitation occurs when analyte cannot diffuse to the surface fast enough to maintain equilibrium. This distorts kinetic parameters.

Symptoms

  • Observed kon appears slower than true kon
  • Linear association phase (instead of exponential)
  • kon varies with ligand density
  • Rmax varies with flow rate

When mass transport matters

  • High-affinity interactions (kon >10^6 M^-1s^-1)
  • High ligand density (>500 RU)
  • Slow flow rates (<30 μL/min in SPR)
  • Large analytes (slow diffusion)

Mitigation strategies

StrategySPRBLI
Reduce ligand density<200 RU for high-affinity<0.5 nm shift loading
Increase flow rate50-100 μL/minIncrease shake speed (1000 rpm)
Use oriented immobilizationHis-tag captureBiotinylated ligand
Include in fittingMass transport model (kt)Usually less critical

Nonspecific binding mitigation

Buffer additives (ranked by effectiveness)

AdditiveConcentrationMechanismBest For
BSA0.5-1 mg/mLBlocks hydrophobic sitesGeneral use
Tween-200.02-0.05%Prevents surface adsorptionHydrophobic analytes
Trehalose1-5%Stabilizes + blocksUnstable proteins
Sucrose5%BLI-specific blockerBLI tips
Carboxymethyl dextran1 mg/mLCompetitive blockingSPR with charged proteins
NaCl150-500 mMReduces ionic interactionsCharged proteins

pH optimization

  • Keep buffer pH at least 1 unit away from analyte pI
  • pI near 7: Use pH 6.0 or 8.0 buffer
  • Acidic proteins (pI <5): Use neutral or basic buffer
  • Basic proteins (pI >9): Use slightly acidic buffer

Reference subtraction

Always include:

  • Blank reference channel (no ligand)
  • Buffer-only injections
  • Non-specific binding controls

Regeneration conditions

SPR regeneration scouting (try in order)

ConditionTargetsCaution
10 mM Glycine pH 2.0-2.5Most protein-proteinMay denature ligand
10 mM Glycine pH 1.5Strong interactionsHarsh, limit exposure
1-2 M NaClIonic interactionsMild, try first
10 mM NaOHVery stable ligandsCan hydrolyze proteins
10 mM Glycine pH 9-10Acid-stable proteinsCan aggregate
10 mM EDTAHis-tag, metal-dependentStrips Ni-NTA
4 M MgCl2Hydrophobic interactionsCheck ligand stability

Regeneration protocol

  1. Start with mildest condition (high salt)
  2. Test 30s contact time
  3. Verify complete dissociation (return to baseline)
  4. Verify retained ligand activity (repeat binding)
  5. Use shortest effective contact time

BLI tips

  • Tips are often disposable (no regeneration needed)
  • For reuse: Same conditions as SPR, but shorter exposure
  • Anti-His tips: 10 mM Glycine pH 1.5, 30s
  • Streptavidin tips: Generally not regenerable

Common artifacts and solutions

Biphasic binding

Symptoms: Two-rate association or dissociation Causes:

  • Sample heterogeneity (aggregates)
  • Ligand heterogeneity (multiple conformations)
  • Avidity effects (bivalent analyte)

Solutions:

  • Filter/centrifuge sample
  • Use monovalent Fab fragments
  • Reduce ligand density
  • Fit to heterogeneous model

Negative dissociation

Symptoms: Signal increases during dissociation phase Causes:

  • Ligand leaching from surface
  • Analyte aggregation on surface
  • Reference channel drift

Solutions:

  • Use capture antibody instead of direct immobilization
  • Increase buffer stringency
  • Better reference subtraction

Hook effect

Symptoms: Signal decreases at high analyte concentrations Causes:

  • Surface saturation + rebinding suppression
  • Crowding effects

Solutions:

  • Reduce analyte concentration range
  • Reduce ligand density
  • Use smaller analyte fragments

Kinetic data quality checklist

Before analysis

  • Reference-subtracted properly
  • Buffer injection shows flat baseline
  • Rmax consistent across concentrations
  • No systematic drift during association
  • Complete regeneration (return to baseline)
  • Duplicate/triplicate injections consistent

Fitting quality

  • Residuals randomly distributed (no systematic deviation)
  • Chi² < 10% of Rmax (or < 1 RU² for low signals)
  • kon and koff errors < 20% of values
  • KD from kinetics matches equilibrium KD (within 3-fold)
  • Fitted Rmax reasonable (close to theoretical)

Red flags

  • kon approaching mass transport limit (>10^7 M^-1s^-1)
  • koff faster than data acquisition (< 0.01 s^-1 requires faster sampling)
  • Rmax >> theoretical maximum (aggregation or avidity)
  • Large difference between kinetic and equilibrium KD

References

Platform comparisons

SPR protocols

Troubleshooting

Regeneration

Mass transport

Frequently asked questions

What does the Binding Characterization AI skill do?

Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.

Why use Binding Characterization on TypingMind?

Because you install it once and use it with any model. Binding Characterization is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Binding Characterization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/binding-characterization. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Binding Characterization?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Binding Characterization?

As many as you like. As long as a model supports skills, you can use Binding Characterization with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Binding Characterization AI skill free?

Yes. It is published on GitHub by FreedomIntelligence under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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