GRASP Library Designer
Codon-optimize GRASP (Farley et al., NAR 2025) binder DNA for Golden Gate assembly.
PyPI: grasp-library-designer · Import: grasp_library
Open in Google Colab
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| Notebook | Open |
|---|---|
| One-shot (one RNA → Golden Gate oligos for the binder gene) | |
| Library (42-module redesign → GAP compile) |
Direct links:
- One-shot: https://colab.research.google.com/github/JustABiologist/grasp-library-designer/blob/main/grasp_oneshot_designer.ipynb
- Library: https://colab.research.google.com/github/JustABiologist/grasp-library-designer/blob/main/grasp_library_designer.ipynb
Each notebook installs with:
%pip install -q -U "grasp-library-designer>=0.1.15"
Bundled GenBank modules, Potapov ligase-only matrices, and Pryor Golden Gate
cycling matrices ship inside the package (materialize_project()).
Install locally
pip install grasp-library-designer
# optional notebook extras
pip install "grasp-library-designer[notebook]"
from grasp_library import materialize_project, build_default_config, LigationFidelityCalculator
project = materialize_project() # ./grasp_library_project + GenBank
config = build_default_config(project / "input")
print(LigationFidelityCalculator(25, 18).set_fidelity(["AATG", "GATA"]))
From a blank Colab / Jupyter, you can also drop the Forms notebooks onto disk:
%pip install -q -U grasp-library-designer
from grasp_library import write_notebook
write_notebook("oneshot") # or "library"
# then open the written .ipynb from the file browser
What each notebook does
| Notebook | Purpose |
|---|---|
grasp_oneshot_designer.ipynb |
One target RNA → binder protein → joint Golden Gate gene design (cuts, overhangs, sequence) → orderable oligos |
grasp_library_designer.ipynb |
Redesign / anneal the 42-module combinatorial library, then GAP-compile a target |
One-shot does not emit combinatorial library modules. It builds an ORF that
starts with ATG-Met, then the native solvating helix and PPR repeats, co-designs
codon-aligned cut sites, high-fidelity 4-nt overhangs, and a synonymous CDS
(codon optimality, cut-site depletion, synthesis heuristics), then wraps each
fragment with inward-facing Type IIS arms and destination sticky ends. Design
also writes a multi-record .gb (assembled ORF, ligated insert with destination
overhangs, and each order oligo) labelled with CDS, PPR repeats, 4-nt overhangs,
Type IIS cut sites, and the shared pool PCR primers.
Hard constraints (library path): the protein sequence is fixed and every movable
four-base cut is restricted to the invariant ARELF motif. The search explores
all motif-relative offsets 0–11 rather than only the four cut positions chosen
in the paper. A candidate is therefore an (overhang, ARELF offset) pair, and
each part is rematerialized before codon optimization. Objectives are ligation
fidelity, codon optimality, and synthesis fitness.
Ligation fidelity is reported per physical six-overhang Level 0 reaction (and
optionally as an explicitly labelled product across independently transformed
blocks). The scalar is the orientation-invariant geometric mean of the two
directional products. Stage-matched Pryor et al. 37↔16 °C Golden Gate cycling
matrices are used by default: BsaI-HFv2 for Levels −1 and 1, and BbsI-HF
(BpiI isoschizomer) for Level 0 redesign scoring. Potapov’s ligase-only data
remain available as optional Level 0 surrogates; they contain no measured 16 °C
matrix, so the program does not interpolate or blend static temperature
matrices. These scores are optimization surrogates, not cloning guarantees.
Synthesis QC distinguishes PASS, WARNING, and FAIL; vendor profiles remain
transparent heuristics with vendor_acceptance_confirmed=False.
The order file contains double-stranded synthesis fragments with paired, inward-facing BsaI sites. The dashboard exposes exactly one physical 5′/3′ overhang pair for each cloning level. Every overhang is written 5′→3′. The deposited GRASP toolbox defaults are:
- Level −1:
ACAT / ACAA. - Level 0:
CTCA / CTCG. - Level 1:
GGAG / AGCG.
The 3′ sticky ends are reverse-complemented internally when constructing the
coding-oriented sequence. Thus the retained 3′ coding sites are TTGT,
CGAG, and CGCT, respectively. Internal five-part and ARELF junctions are
derived from the GRASP architecture rather than presented as extra dashboard
overhang fields. When no custom acceptor sequence is provided, the exporter
validates interface requirements but does not claim backbone simulation.
The exported GRASP tract is a PPR block set, not a standalone expression plasmid. The PPR block-chain check does not validate an entire Level 1 expression construct; promoter, upstream domain, effector, terminator, and acceptor context must be supplied separately.
For 14S and 19S, intermediate junctions are generated inside the invariant
ARELF motif. They are architecture-derived and can be explored by the
overhang redesign search without adding more dashboard fields.
Develop from source
git clone https://github.com/JustABiologist/grasp-library-designer.git
cd grasp-library-designer
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[notebook,dev]"
Package layout
grasp_library/ # installable Python package
data/profiles/.../genbank/ # bundled GRASP GenBank modules
notebooks/ # Colab Forms notebooks (also at repo root)
paths.py # materialize_project()
...
third_party/dawdlib_golden_gate/ # Potapov ligation fidelity (AGPL)
License
AGPL-3.0 (required by the vendored GGAssembler / dawdlib ligation engine). See LICENSE and THIRD_PARTY_LICENSES.md.
GRASP sequences: Farley et al., Nucleic Acids Res. 2025.
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