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Cell Free Protein Expression

Community
BioTender-max
cell-free-protein-expression

Cell-free protein synthesis (CFPS) planning and optimization guidance. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).

Overview

PublisherBioTender-max
Repositoryawesome-bio-agent-skills
Skill namecell-free-protein-expression
Stars
204
Forks
36
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 BioTender-max on GitHub. Read the source before you install it.

Installation

Install the Cell Free Protein Expression 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.
    https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/bioclaw_hub/cell-free-protein-expression
  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/BioTender-max/awesome-bio-agent-skills.git /tmp/awesome-bio-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-bio-agent-skills/skills/bioclaw_hub/cell-free-protein-expression .claude/skills/cell-free-protein-expression
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cell Free Protein Expression 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 Cell Free Protein Expression 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 Cell Free Protein Expression 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.

Cell-Free Protein Synthesis (CFPS)

Plain-language role: Use this skill to plan expression screens and improve yield or solubility in cell-free systems.

System Selection Guide

SystemBest ForYieldPTMsDisulfidesCost
E. coli extractRapid prototyping, prokaryotic proteinsHigh (100-400 μg/mL)NonePoor (reducing)Low
E. coli PUREDefined conditions, unnatural AAsMedium (50-150 μg/mL)NoneControllableHigh
Wheat germEukaryotic proteins, membrane proteinsHigh (100-500 μg/mL)LimitedModerateMedium
Rabbit reticulocyteMammalian proteins, post-translational studiesLow (10-50 μg/mL)SomePoorHigh
Insect (Sf21)Glycoproteins, complex foldsMedium (50-100 μg/mL)GlycosylationGoodHigh
HeLa/CHONative mammalian proteinsLow (10-50 μg/mL)Full mammalianGoodVery High

CFPS Troubleshooting Matrix

ProblemLikely CausesDesign FixReagent Fix
No expressionRare codons at N-terminus, poor RBSCodon optimize first 30 codonsUse BL21-CodonPlus extract
Low yieldStrong mRNA secondary structure, template issuesOptimize 5' UTR (ΔG > -5 kcal/mol)Increase Mg²⁺ (10-18 mM), ATP
AggregationHydrophobic protein, fast translationAdd solubility tags (MBP, SUMO)Add 0.1% Tween-20, chaperones
Inactive proteinMisfolding, missing cofactorsSlow translation (use rare codons!)Add GroEL/ES, DnaK/J
TruncationRare codon clusters, mRNA instabilityRemove AGG/AGA/CUA clustersSupplement rare tRNAs
DegradationProteolysisN-terminal Met-AlaAdd protease inhibitors

Codon Optimization for CFPS

Codons to Avoid in E. coli CFPS

CodonAmino AcidIssuetRNA Abundance
AGGArgVery rare, stalling0.2%
AGAArgVery rare, stalling0.4%
CUALeuLow abundance0.4%
AUAIleRare0.5%
CGAArgInefficient decoding0.6%
CCCProCan cause pausing0.5%
GGAGlyModerate1.1%

Design Rules

  1. First 30 codons: Most critical - use only high-frequency codons
  2. Rare codon clusters: Avoid 2+ rare codons within 10 nt
  3. Rare codon content: Keep overall <5% of coding sequence
  4. GC content: Target 40-60% for balanced expression
  5. Avoid runs: No >6 consecutive G or C residues (secondary structure)
  6. Strategic slow codons: Place rare codons between domains (aids folding!)

When to Use Rare Codons

  • Domain boundaries (allow cotranslational folding)
  • Before complex structural elements
  • When protein is prone to misfolding

mRNA Template Design

5' UTR Optimization

ElementOptimal DesignImpact
RBS (SD sequence)AGGAGG, 7-9 nt from startRibosome binding
Spacing7 nt between SD and AUGTranslation initiation
Secondary structureΔG > -5 kcal/molAccessibility
Upstream AUGAvoid (causes false starts)Reduces truncations

Secondary Structure Targets

RegionIdeal ΔGImpact
-30 to +30 around AUG> -5 kcal/molTranslation initiation
Full 5' UTR> -10 kcal/molRibosome loading
RBS accessibilityUnpairedCritical

Template Format

FormatAdvantagesDisadvantages
PlasmidStable, high yieldRequires cloning
Linear PCRFast, no cloningMay need stabilization
mRNADirect translationUnstable, expensive

Disulfide Bond Formation

System Capabilities

SystemNative Disulfide SupportAdditives Needed
Standard E. coli extractPoor (DTT present)IAM, PDI, GSSG/GSH
Oxidizing E. coli extractGoodPre-oxidized glutathione
Wheat germModerateLower DTT, add PDI
PURE systemMinimalFull oxidative system
Insect/MammalianGoodMicrosome membranes

Oxidative Folding Protocol (E. coli extract)

1. Deplete DTT from extract (dialysis or treatment with IAM 5 mM)
2. Add oxidized/reduced glutathione: 4 mM GSSG, 1 mM GSH (4:1 ratio)
3. Add 10 μM PDI (protein disulfide isomerase)
4. Optional: Add 5 μM DsbC (disulfide isomerase)
5. Express at 25°C (not 37°C) for better folding
6. Incubation time: 4-6 hours

Disulfide-Rich Protein Tips

  • Start with wheat germ or oxidizing extract
  • Use PURE system for precise control
  • Consider co-expression of PDI/DsbC
  • Verify by non-reducing SDS-PAGE

Expression Prediction from Sequence

FeatureGoodMarginalBad
Rare codon content<3%3-8%>10%
First 30 codons rare01-2>2
GC content45-55%35-45% or 55-65%<30% or >70%
5' UTR ΔG> -3 kcal/mol-3 to -8< -10 kcal/mol
Hydrophobic stretches<5 consecutive5-7>8 consecutive
N-terminal residueMet-Ala, Met-Ser, Met-GlyMet-Val, Met-ThrMet-Arg, Met-Lys
Cysteine pairsPaired (even number)MixedOdd number (free thiols)

Solubility Enhancement Strategies

Fusion Tags (ranked by effectiveness)

TagSizeSolubility EnhancementCleavageNotes
MBP40 kDaExcellentTEV, Factor XaBest overall
SUMO11 kDaVery GoodSUMO proteaseNative N-terminus after cleavage
NusA55 kDaExcellent-Large size
Trx12 kDaGoodEnterokinaseFor disulfide proteins
GST26 kDaModerate-Dimeric
His₆1 kDaMinimal-Mainly for purification

Buffer Additives for Solubility

AdditiveConcentrationMechanism
Trehalose50-100 mMChemical chaperone
Glycerol5-10%Reduces hydrophobic aggregation
L-Arginine50-100 mMSuppresses aggregation
Tween-200.05-0.1%Prevents surface adsorption
Proline50 mMOsmolyte stabilization

Chaperone Supplementation

Chaperone SystemTarget ProblemConcentration
GroEL/GroESGeneral folding1-2 μM
DnaK/DnaJ/GrpEAggregation-prone1 μM each
Trigger FactorNascent chain1-2 μM
ClpBAggregate resolubilization0.5 μM

Temperature Optimization

TemperatureUse CaseTrade-offs
37°CFast expression, stable proteinsHigher aggregation risk
30°CBalanced (default)Good compromise
25°CDisulfide proteins, complex foldsSlower, better folding
18-20°CAggregation-prone proteinsMuch slower, best folding
16°CCold-shock proteinsVery slow, specialized

E. coli Extract Preparation (Key Variables)

VariableImpactOptimal Range
Cell density at harvestRibosome contentOD₆₀₀ 2.5-3.5
Lysis methodExtract activitySonication, bead beating
Run-off reactionRemoves endogenous mRNA20-80 min at 37°C
Mg²⁺ concentrationTranslation fidelity10-18 mM
K⁺ concentrationTranslation rate150-200 mM
Energy systemSustained synthesisATP/GTP, creatine phosphate

PURE System Specifics

Advantages

  • Defined composition (no proteases/nucleases)
  • Linear DNA templates work well
  • Unnatural amino acid incorporation
  • Reproducible between batches

Limitations

  • No chaperones (add separately)
  • No post-translational modifications
  • Lower yields than crude extracts
  • Higher cost

When to Use PURE

  • Unnatural amino acid incorporation
  • Studying translation mechanisms
  • "Clean" proteins needed
  • Protease-sensitive targets
  • Linear template expression

Common Artifacts and Solutions

Low Molecular Weight Bands

Causes: Premature termination, proteolysis, internal initiation Solutions:

  • Optimize rare codon clusters
  • Add protease inhibitors
  • Check for internal AUG codons
  • Use PURE system

Higher MW Bands

Causes: Incomplete termination, read-through, aggregation Solutions:

  • Ensure strong stop codon (UAA preferred)
  • Check template 3' end
  • Add release factors (RF1/RF2)
  • Reduce protein concentration

No Soluble Protein

Causes: Aggregation during synthesis Solutions:

  • Lower temperature (25°C → 18°C)
  • Add chaperones
  • Use solubility tag
  • Optimize translation rate

References

CFPS Overview

Extract Preparation

PURE System

Wheat Germ

Codon Optimization

Disulfide Formation

Solubility Tags

Temperature Effects

Inputs

  • A protein construct or sequence plus the intended expression objective and desired scale.
  • Known liabilities such as disulfides, toxicity, membrane association, or aggregation.
  • Available CFPS system options and readouts for yield or solubility.

Outputs

  • A recommended CFPS platform, construct design guidance, and reaction optimization plan.
  • Troubleshooting suggestions for low yield, precipitation, or inactive product.
  • An experiment-ready checklist covering template design, additives, and controls.

Next Step

Run the recommended expression screen, then pair high-yield constructs with spr-bli-binding-characterization or structural validation.

Frequently asked questions

What does the Cell Free Protein Expression AI skill do?

Cell-free protein synthesis (CFPS) planning and optimization guidance. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).

Why use Cell Free Protein Expression on TypingMind?

Because you install it once and use it with any model. Cell Free Protein Expression 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 Cell Free Protein Expression in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/bioclaw_hub/cell-free-protein-expression. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cell Free Protein Expression?

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 Cell Free Protein Expression?

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

Is the Cell Free Protein Expression AI skill free?

Yes. It is published on GitHub by BioTender-max 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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