⏱️ 30-second version. Pick a crop and one or more traits. The tool
maps each trait to its validated effector protein, generates a fresh,
crop-optimized DNA coding sequence for it with a translation-aware optimizer,
and returns a full report on expression, gene-editing targets, stability and
safety — ready to synthesize. No AI, no cloud; runs on your machine.
Scientists who want a plant to do something new (survive drought, resist disease,
improve yield) need a custom piece of DNA designed for it. Writing that DNA by
hand is slow and error-prone: the plant might ignore it, silence it, or read it
incorrectly.
Plant DNA Designer turns a crop + trait choice into a ready-to-test DNA
cassette. It supports 46 validated traits across 18 crops — from submergence
tolerance (SUB1A) and Golden-Rice provitamin-A (PSY) to nitrogen-use efficiency
(GRF4), stem-rust resistance (Sr35), cotton fiber quality (GhMYB25) and sugarcane
sucrose accumulation (SPS1). The key is its biological target layer:
each trait is mapped to a validated plant mechanism and the effector protein
known to drive it (e.g. drought tolerance → DRE/CRT dehydration signaling → the
DREB2A transcription factor). The tool then generates a de-novo, crop-optimized
coding sequence for that effector using a translation-aware evolutionary
optimizer that balances codon adaptation, codon harmony, mRNA structure,
translational pausing, expression quality, and sequence safety — built by
optimization rather than retrieved from a database — and runs a full health check
covering expression, gene-editing targets, stability, and safety. Everything runs
on your own machine with simple, transparent math.
What "de novo" means here: the protein is a validated plant effector
(with citations); the nucleotide sequence is generated fresh — synonymous,
crop-codon-optimized, and expression-engineered — not copied from any gene in a
database. You can also override the target with your own validated protein.
💡 No AI, no cloud, no cost. Every score is computed with published, open
formulas — there are no paid AI/LLM calls and nothing leaves your computer.
The biology is tuned specifically for plants, not humans or animals.
★ What makes it different: unlike ordinary codon optimizers, it designs the
whole expression cassette — and is one of the few plant-focused design
platforms that automatically designs IME-enhancing introns (Intron-Mediated
Enhancement), a plant-exclusive trick that can lift expression 10–50× by
auto-designing an enhancing intron for the 5′UTR. (See the flagship section below.)
Translation-Aware Optimization Engine
Plant DNA Designer does not simply maximize the Codon Adaptation Index (CAI).
The optimization engine uses a multi-objective evolutionary framework that
models several stages of gene expression simultaneously:
Position-dependent mRNA structure — keeps the translation-initiation region
accessible while maintaining beneficial downstream structure.
Codon-harmony optimization — matches natural codon-usage distributions
instead of forcing CAI toward unrealistic extremes.
Translation-dynamics optimization — models the ribosome's velocity
trajectory along the transcript (local speed, velocity gradients, congestion)
and shapes it to a folding-friendly schedule: a slow start-up ramp, fast domain
interiors, and pauses at predicted inter-domain linkers so each domain can
fold before the next emerges. Optimization of translation dynamics, not
isolated codon scores.
Dimensionless objective framework — every objective is normalized and
weighted on a common scale, preventing any single metric from dominating the
search.
Liability-directed evolution — mutations preferentially target problematic
sequence regions rather than relying entirely on random exploration.
Crop-aware throughout (monocot vs dicot) — the grass/broadleaf clade of the
chosen crop drives a species-specific Kozak initiation context, intron
splicing composition (AU-rich for dicots, GC-balanced for monocots), and
miRNA library — not just the codon table.
The result is a translation-aware design strategy that optimizes initiation,
elongation, folding, and sequence quality together, rather than treating codon
optimization as a single-metric problem.
Why this matters: most tools do one of — trait→mechanism→effector mapping,
translation-aware sequence design, or plant-specific optimization. Plant DNA
Designer combines all three in a single workflow.
Benchmark
Sophisticated ideas only count if they produce different, measurable outcomes —
and the comparison has to be external, not a set of in-house strawmen. The repo
ships a reproducible benchmark (cool/benchmark.py) in which every comparator
faithfully reproduces the published algorithm of a named, widely-used external
tool — JCat / OPTIMIZER / ATGme (one-amino-acid-one-codon CAI-max), IDT
(balanced codon usage), and TISIGNER (5′-initiation optimization) — run on the
same panel of 6 real plant effector proteins (DREB2A, SUB1A, PSY, GRF4,
Sr35, Ferritin) with the same plant metrics. Reproducing the algorithm is fairer
than scraping each tool's web output, which targets generic microbial/mammalian
hosts and varies by host and GC settings:
cd cool && python -m benchmark
Strategy (← external tool reproduced)
CAI
Harmony
tAI
MTDR
Dynamics
5′-openness
Body ΔG
Safety
GC dev
Random synonymous (naive floor)
0.731
0.747
0.391
0.776
0.722
−0.248
0.279
3.47
3.5
JCat / OPTIMIZER / ATGme (CAI-max)
1.000
0.422
0.514
0.834
0.649
−0.347
0.358
2.33
21.8
IDT (balanced usage)
0.793
0.930
0.422
0.791
0.740
−0.139
0.251
3.00
7.5
TISIGNER (5′ initiation)
0.955
0.495
0.495
0.829
0.699
−0.153
0.358
2.92
18.9
CAI+GC heuristic
1.000
0.433
0.512
0.833
0.650
−0.351
0.382
2.58
21.0
Plant DNA Designer
0.790
0.774
0.422
0.782
0.741
−0.114
0.387
0.00
4.0
Plant DNA Designer (folding mode)
0.775
0.815
0.416
0.796
0.761
−0.129
0.388
0.25
4.1
Higher is better for CAI, Harmony, tAI, MTDR, Dynamics, Body ΔG; closer-to-0 is
better for 5′-openness; lower is better for Safety (liability penalty) and GC dev.
tAI now uses the dos Reis wobble weights (so a codon read only through an inefficient
wobble tRNA scores below its Watson–Crick synonym), MTDR is the tRNA supply/demand
match. Note CAI-max scores highest on tAI/MTDR — in GC-rich rice the argmax
codons are also the high-tGCN ones, which is exactly why tAI alone isn't sufficient
and PDD trades a little of it for harmony, safety and GC fidelity. "Dynamics" is the
ribosome-velocity-trajectory match; GC dev is deviation from a neutral 50% synthesis
target.
What the numbers show — including where we don't win. Each external-tool
algorithm excels on the one axis it optimizes and fails on the others. JCat/
OPTIMIZER/ATGme hit a perfect CAI (1.000) but the worst harmony (0.422) and — on the
real GC-rich rice codon table — a catastrophic 21.8 pp GC deviation (argmax
drives GC to ~72%). IDT's balanced-usage algorithm beats Plant DNA Designer on
harmony (0.930 vs 0.774) — exactly as expected, since matching natural frequencies
is IDT's sole objective. TISIGNER gets decent start-codon openness but among the
worst safety. That a dedicated single-objective tool beats us on its own axis is
evidence the benchmark is fair, not rigged.
Plant DNA Designer's contribution is not winning any single axis but being the
only strategy that holds every axis within bounds at once:
Codon harmony +84% vs CAI-max (0.422 → 0.774) — far more native-like.
Better ribosome dynamics (0.649 → 0.741, 0.761 in folding mode).
Start codon far more accessible (−0.347 → −0.114).
More downstream structure (0.358 → 0.387) — for mRNA half-life.
Safety liabilities — the decisive gap: every external-tool algorithm leaves
2.3–3.5 cloning-blocking liabilities per sequence; PDD cuts this to 0.00
(default) / 0.25 (folding). No single-metric tool addresses safety at all.
GC fidelity — the other decisive gap: naive CAI-max lands 21.8 pp off a
50% target on real rice codons; PDD steers to 4.0 pp.
Consistent across the panel, not an averaging artifact. Per protein, PDD beats
all four external-tool algorithms on safety (6/6) and GC fidelity (6/6),
and on body structure (4/6), start-openness (3/6) and dynamics (2/6), folding mode
highest overall. It does not win harmony — IDT's single-objective design takes
that — nor tAI/MTDR, which CAI-max wins because in GC-rich rice the argmax codons
are also the high-tGCN ones. An honest non-dominance rather than a clean sweep.
python -m benchmark prints the full per-protein table and win counts.
Folding mode (the harmonize toggle) pushes hardest on the folding-relevant
axes — best translation dynamics and start openness — at a deliberately lower CAI
(0.775), exactly the trade the codon-harmony / translation-dynamics literature
argues for.
Caveat: these are in-silico proxy metrics on a fixed panel — evidence that the
design strategies produce measurably different sequences, not a wet-lab
expression claim. The hosted tools are web-only and host-generic, so each
comparator reproduces the tool's documented algorithm rather than scraping
its server. Definitive external validation requires head-to-head wet-lab
expression of PDD-designed vs. tool-designed sequences.
Screenshots
Every panel is rendered as self-contained SVG/HTML — no external chart libraries.
Design Report Card & simple view — a one-word grade, a 0–100 score, and four
plain-language traffic-light checks; switch to Expert for all 15 analysis panels.
Report Card and simple view
Translation dynamics (flagship) — the ribosome's velocity trajectory (green)
shaped to an ideal folding schedule (orange): a slow start-up ramp, fast domain
interiors, and a pause at each predicted inter-domain linker.
Translation dynamics
Intron-Mediated Enhancement (flagship) — scores intron potential (IMEter) and
auto-designs a splice-competent, enhancer-packed intron for the 5′UTR — a
plant-only route to 10–50× higher expression.
Intron-Mediated Enhancement
Full expression cassette — promoter → 5′UTR → IME intron → CDS → terminator,
assembled from validated, cited parts and compatibility-checked.
Full expression cassette
📸 More screenshots — biological target, GA/Pareto, CRISPR, pathway, safety
You select a crop and one or more of the traits it supports.
The biological target layer resolves the trait to its validated mechanism
and the effector protein that confers it (with literature citations). This is
the protein the cassette will encode. You can override it with your own
validated sequence.
A genetic algorithm generates the DNA de novo — it evolves thousands of
candidate coding sequences for that effector (each carrying the regulatory
signals for your traits), scores them on many goals at once (codon fit,
stability, safety) and keeps the best. The nucleotide sequence is produced by
optimization, not retrieved from a database.
The winner is analyzed by every science module and shown in the report —
the sequence and its biological target first, then all the quality and safety
checks.
Glossary
Term
What it means
Codon optimization
Swapping DNA "words" for synonyms the crop reads most efficiently, so it makes more protein.
CAI / tAI
Scores (0–1) for how well the codons match the crop's preferences and tRNA supply. Higher = better.
GC content / GC3
How much of the DNA is G or C. Balanced GC helps stable, strong expression.
CRISPR guide RNA (gRNA)
A short sequence that directs the Cas protein to cut DNA at a chosen spot.
On-target / off-target
How well a guide cuts where you want vs. how likely it is to cut elsewhere.
Base editing (CBE/ABE)
Changing a single DNA letter without cutting the whole strand.
mRNA
The working copy of a gene that the cell translates into protein.
miRNA silencing (PTGS)
A plant's natural way of switching off a gene; best avoided for a new transgene.
TFBS
Tissue-specific "switches" in DNA that decide where a gene turns on.
Transgene
A gene moved into an organism from elsewhere.
Trait vs. mechanism
A trait (e.g. "drought tolerance") is a phenotype; it's produced by a mechanism (a pathway) and the effector protein(s) that act in it.
Effector protein
The actual protein that delivers a trait — what the designed DNA encodes (e.g. DREB2A for drought).
Intron
A piece of DNA cut out of the mRNA before translation. In plants, the right intron near the start boosts expression.
IME / IMEter
Intron-Mediated Enhancement — a plant-only effect; the IMEter score predicts how strongly an intron will enhance expression.
What's under the hood
All the biology lives in a clean, testable core with no web or UI code, so it can
be reused and trusted independently.
cool/
├── core/ # The science — pure Python, fully tested
│ ├── motifs.py # Plant regulatory motif definitions
│ ├── codons.py # Codon usage / CAI / tAI accessors (loads real per-species tables)
│ ├── codon_usage_tables.py # 18 real per-species codon tables (generated from Kazusa CUTG)
│ ├── data/cutg_plant_subset.spsum # committed Kazusa CUTG source data (18 species)
│ ├── species.py # 18 crop profiles (monocot + dicot)
│ ├── mechanisms.py # Trait → mechanism → effector protein knowledge base
│ ├── intron.py # ★ Intron-Mediated Enhancement: IMEter + intron designer
│ ├── cassette.py # Full-cassette co-design: validated part libraries + compatibility
│ ├── pathway.py # Multi-gene pathway: codon-optimality expression balancing + HDGS check
│ ├── gc3.py # GC3 balance + tRNA-demand (MTDR) scoring
│ ├── stability.py # mRNA stability + plant miRNA scanners
│ ├── base_editor.py # CBE / ABE / Cas12a / chromatin scoring
│ ├── expression.py # Tissue-specificity + expression-quality scoring
│ ├── analyzer.py # Pulls every metric together
│ ├── crispr.py # CRISPR on-target (Doench RS2) + off-target (CFD)
│ └── designer.py # Multi-objective GA: dimensionless weights, position-
│ # dependent structure, distribution matching, NSGA-II
├── api/ # FastAPI: turns a request into the full JSON report
├── web/ # Browser frontend (no build step, no Node/npm)
├── benchmark.py # Reproducible benchmark vs external-tool algorithms (JCat/IDT/TISIGNER)
└── tests/ # 172 tests covering the science and the API
Built with: Python · FastAPI · BioPython · a little HTML/CSS/JS. Optional
ViennaRNA gives exact RNA folding; without it, a built-in fallback keeps results
meaningful.
API endpoints
Method
Path
Purpose
GET
/
Web UI
POST
/api/design
Run the full design + analysis, return JSON
WS
/ws/design
Same, but streams live progress
POST
/api/analyze
Quick metrics + motif scan for a sequence you paste in
POST
/api/grna
CRISPR guide design for a sequence
GET
/api/export/{fmt}
Download as genbank, fasta, or sbol
GET
/api/health
Health check
The science
For readers who want the detail behind each check (and for the full
specification — every objective, formula, weight, normalization and citation —
see the Technical Reference):
Biological target layer — a curated knowledge base maps each supported
trait to its established plant mechanism and the validated effector gene(s)
that confer it (e.g. drought → DREB2A / LEA / P5CS1; bacterial blight → Xa21 /
NPR1 / PR1; salt → NHX1 / HKT1), each with a primary-literature reference. The
selected trait's primary effector becomes the design seed; the encoded protein
is shown explicitly and can be overridden with a user-validated sequence. This
is a reference/design layer — validate the target experimentally before use.
Intron-Mediated Enhancement (flagship) — an IMEter-style pentamer log-odds
model scores how much a sequence resembles promoter-proximal (enhancing)
Arabidopsis introns; the designer assembles a splice-competent intron
(GT…AG, branch point, polypyrimidine tract) enriched with the TTNGATYTG
enhancing signal, for 5′UTR insertion within ~1 kb of the TSS. Refs: Rose 2008
(Plant Cell); Parra & Rose 2011 (NAR 39:5328); Gallegos & Rose 2025 (NAR
53:gkaf097).
Expression-cassette co-design — assembles promoter → 5′UTR → intron → CDS →
terminator by selecting validated, published parts (CaMV 35S, maize Ubi1,
rice Act1, UBQ10, NOS; NOS/35S/rbcS-E9/HSP18.2/Os terminators — each cited)
matched to clade/strength/context, and compatibility-checks the assembly
(Type-IIS sites, cross-part repeats, clade consistency). Conservative by design:
regulatory parts are recommended by identity, not invented; only the
synthetic-minimal promoter (validated CREs), validated leaders (TMV Ω, AMV) and
the IME intron are emitted as ready DNA. See Technical Reference §4b.
Multi-gene pathway design — selecting ≥2 traits coordinates their effector
genes into a balanced pathway: each gene is dialed to a target relative
expression by codon-optimality tuning (Welch 2009; Boël 2016), the realized
stoichiometry is scored, and each gene gets a distinct validated
promoter/terminator to limit homology-dependent silencing (HDGS). Predicted
expression is a relative proxy, not absolute; see Technical Reference §4c.
Codon optimization — a multi-objective genetic algorithm balances CAI, tAI
(using real per-species tRNA gene-copy-number (tGCN) tables counted from
GtRNAdb — 11 crops have their own sequenced genome; the rest map to their
nearest sequenced relative by phylogeny, e.g. potato→tomato, peanut→soybean,
sugarcane→sorghum, wheat/barley→Brachypodium, with the source FASTAs
committed and rebuildable via tools/build_tgcn.py), GC3, tRNA-demand (MTDR),
and mRNA-stability motifs. Each of the 18 crops uses its own real codon-usage
table measured from the Kazusa Codon Usage Database (CUTG, plant division) —
no cross-species proxies. The committed source data and the generator
(tools/build_codon_usage.py) make the tables fully reproducible, and the real
data cleanly separates the monocot/dicot clade axis (monocots are GC3-rich,
dicots AT-/balanced). Rather than driving CAI to 1.0 (argmax at every codon,
which over-optimises and de-tunes folding), it also rewards matching the
natural codon-usage distribution (1 − total-variation distance to the
reference) — aiming for "high but natural" usage where plain CAI saturates
(Angov 2008; Mignon 2018).
Position-dependent mRNA structure — secondary structure is not uniformly
good. The ribosome-loading window (~first 45 nt from the AUG) is optimised to
stay open for efficient translation initiation, while the body is optimised
to be structured for mRNA half-life (Kudla 2009; Zur & Tuller 2012; Jores
et al. 2021), instead of one global "more structure = better" term that fought
initiation.
CRISPR — on-target uses the Doench 2016 RS2 model (80 single + 9 dinucleotide
terms, logistic sigmoid, poly-T penalty); off-target uses a CFD 12×20 mismatch
matrix.
Base editing — CBE edits C→T in window 4–8, ABE edits A→G in window 4–7,
Cas12a uses a TTTV PAM; chromatin openness from AA/TT 10-bp periodicity + GC.
miRNA safety — seed-match scan against 10 conserved plant miRNAs to avoid
silencing, plus lineage-specific families (e.g. the monocot/grass-specific
miR528) layered in by clade.
Tissue specificity — TFBS enrichment across 8 plant tissue/stress contexts
from PlantCARE and the literature.
Species-specific expression logic (monocot vs dicot) — the crop's clade
drives biology beyond the codon table: a GC-richer Kozak initiation context
for monocots vs the A-rich Joshi consensus for dicots (Sawant 2001; Joshi 1997);
and intron splicing composition — dicot introns require AU-richness for
recognition, monocots splice GC-richer introns efficiently (Goodall &
Filipowicz 1989, 1991). Both the Kozak fitness term and the designed IME intron
adapt automatically to the selected crop.
Ground rules
No AI / LLM calls — every score is plain, transparent math; nothing is sent
to a paid model or external service.
Plant-specific — all codon tables, miRNA seeds, regulatory motifs and TFBS
sets come from plant biology, not human or animal data.
Citation
If you use Plant DNA Designer in your research, please cite the preprint:
Dinesh K, Swetha H. Plant DNA Designer: A Computational Framework for
Multi-Objective Codon Optimisation and Synthetic Gene Design in Crop
Biotechnology.bioRxiv (2026). https://doi.org/10.64898/2026.08.02.742271
1@article{dineshk2026plantdnadesigner,
2 title = {Plant DNA Designer: A Computational Framework for Multi-Objective
3 Codon Optimisation and Synthetic Gene Design in Crop Biotechnology},
4 author = {Dinesh K and H. Swetha},
5 journal = {bioRxiv},
6 year = {2026},
7 doi = {10.64898/2026.08.02.742271},
8 url = {https://doi.org/10.64898/2026.08.02.742271}
9}
