# Tooluniverse Molecular Cloning

> Molecular Cloning Assembly Design (Gibson & Golden Gate)

- Skill: `mims-harvard/tooluniverse-molecular-cloning-2` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add mims-harvard/tooluniverse-molecular-cloning-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mims-harvard/tooluniverse-molecular-cloning-2/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: mims-harvard (https://skillmd.com/u/mims-harvard)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/mims-harvard/tooluniverse-molecular-cloning-2

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# Molecular Cloning Assembly Design (Gibson & Golden Gate)

Plan how to join DNA fragments into a construct: design the **overlaps** (Gibson) or **Type IIS overhangs** (Golden Gate) and avoid the failures that come from internal sites and non-unique junctions.

## Step 0 — Pick the method

| Use **Gibson Assembly** when | Use **Golden Gate** when |
|---|---|
| A few fragments, **scarless/seamless** junctions anywhere you choose | Many parts, **standardized reusable** parts (MoClo/modular), one-pot |
| You can add ~20–40 bp homology by PCR | You can remove internal BsaI/BbsI sites (domestication) |
| One-off constructs | Combinatorial libraries / repeated assemblies |

Both are sequence-independent (no scar at the junction for Gibson; a 4-bp fusion scar for Golden Gate). For 2–4 unique fragments, Gibson is usually simplest; for libraries or a parts toolkit, Golden Gate.

## Step 1 — Gibson Assembly

```bash
tu run DNA_gibson_design '{"operation":"gibson_design",
  "fragments":["ATGGCG...GAGGAC","GAGGAC...GGCAAG","GGGCAAG...ATCCT"],
  "overlap_length":20}'
```

For each fragment it returns `left_overlap`, `right_overlap`, and `with_overlaps` (the fragment extended with the homology arms you'd add to your PCR primers — hand these to `tooluniverse-primer-design`).

**Gibson design rules**
- **Overlap length 15–40 bp** (20–25 typical); longer for GC-poor junctions.
- **Overlap Tm ≈ 48–65 °C** and balanced between junctions.
- **Fragment order matters** — list fragments in assembly order; the last fragment's 3′ overlaps the first only if you're making a circle (vector).
- **Avoid repeats/secondary structure** at the junctions (hairpins, direct repeats) → misassembly.
- **Unique junctions** — if two junctions share homology, fragments can swap; redesign so each overlap is unique.

## Step 2 — Golden Gate Assembly

```bash
tu run DNA_golden_gate_design '{"operation":"golden_gate_design",
  "parts":["ATGGCG...AAGAAC","CTGAGC...CTGATC","GAGGAG...GTGGTG"],
  "enzyme":"BsaI"}'
```

Returns `parts_with_overhangs`: each part's unique 4-bp `left_overhang`/`right_overhang` and the `full_sequence` flanked by the Type IIS recognition sites (e.g. BsaI `GGTCTC(N1)` … cutting outside its site to leave the 4-bp fusion overhang).

**Golden Gate design rules**
- **Domestication is mandatory.** The chosen enzyme's site (BsaI `GGTCTC`, BbsI `GAAGAC`) must NOT occur **inside** any part, or it will be cut internally. Remove internal sites by silent mutation before assembly — check every part. Don't rely on memorized recognition sites for a less common enzyme, or when a part's internal site can't be silently removed and you need an isoschizomer instead: `REBASE_get_enzyme` (site + methylation sensitivity) and `REBASE_list_isoschizomers` (alternative enzymes cutting the same site) are the authoritative lookup.
- **Overhangs must be unique and non-palindromic.** Each 4-bp fusion site must differ from the others and not equal its own reverse complement, or junctions misligate. The tool assigns unique non-palindromic overhangs; keep them.
- **Avoid high-GC or all-AT overhangs**; published high-fidelity overhang sets (e.g. Potapov 2018) ligate most cleanly.
- **Order is encoded by the overhangs**, not by listing order — the 4-bp junctions define assembly.

## Step 3 — QC before ordering

`scripts/cloning_qc.py` screens parts for the problems above: internal BsaI/BbsI sites (Golden Gate), overhang uniqueness/palindromes, and Gibson overlap GC/length — and flags PASS/WARN.

## Step 4 — Gotchas (state these)

- **Internal Type IIS sites** (Golden Gate) — the #1 failure; domesticate every part.
- **Non-unique Gibson overlaps** or shared homology → fragment swapping / misassembly.
- **Repeats and strong secondary structure** at junctions reduce efficiency in both methods.
- **Overlap Tm imbalance** (Gibson) → some junctions form, others don't.
- **Generating the fragments** still needs primers with the overlaps/overhangs appended — design and QC those in `tooluniverse-primer-design` (and BLAST for specificity).

## Working backwards — what does an existing assembly produce?

The reverse question ("I combined these plasmids in a Golden Gate reaction with
Esp3I — what does the product express / what does the gRNA target?") is answered by
one call. Do **not** hand-write a digestion/ligation simulator.

```bash
tu run DNA_golden_gate_assemble '{"fragments":["<plasmid1>","<plasmid2>","<plasmid3>"],
    "enzyme":"Esp3I","labels":["pLAB-CTU","pLAB-gTU2E","pLAB-CH3"]}'
```

It digests each input, drops the fragments that keep a recognition site (those are
re-cut in the reaction and cannot persist), chains the rest by matching 4-bp
overhangs, and returns `product_sequence`, `product_length` and the `assembly_order`
with the overhang at every junction. Inputs are treated as circular plasmids unless
you pass `circular: false`.

Then **annotate the product**. Locate features in `product_sequence` (promoter, ORF,
gRNA spacer). For a gRNA cassette the spacer is the ~20 nt immediately 5′ of the
scaffold (`GTTTTAGAGCTAGAAATAGCAAG`); identify its target by matching that spacer
against the genome (`BLAST_*`, or an Ensembl/NCBI/SGD sequence lookup) and check for
an adjacent PAM. Match the **species implied by the construct** — yeast tRNA/Pol III
parts mean search the yeast genome, not human.

If the assembly reports that the fragments do not chain, digest the inputs
individually with `DNA_virtual_digest` (`circular: true`) to see what each released:
a Golden Gate donor carries its two Type IIS sites **inverted** around the insert, so
a correct digest gives **2 fragments** per plasmid. Getting 1 means the enzyme name or
`circular` is wrong — not that the plasmid lacks sites.

Enzyme names: `Esp3I` and `BsmBI` are the same enzyme (`CGTCTC`); `BsaI`
(`GGTCTC`), `BbsI` (`GAAGAC`) and `SapI` (`GCTCTTC`) all resolve too.

## Honest limitations

- Digestion and overhang-driven ligation are simulated faithfully (`DNA_virtual_digest`
  and `DNA_golden_gate_assemble` cut both strands at each enzyme's real offset, including
  Type IIS enzymes that cut outside their site). What is *not* modelled is reaction
  efficiency — overhang ligation bias, partial digestion, incorrect-but-possible
  junctions — so a returned product is the intended assembly, not a yield prediction.
  Validate by sequencing the assembled construct.
- No vector-backbone or ORF-frame checking — confirm reading frame and backbone compatibility yourself.

## Related skills
- `tooluniverse-primer-design` — design the PCR primers (with homology arms / Type IIS tails) to make the fragments.
- `tooluniverse-sequence-analysis` — handle the input sequences.