Plant DNA Designer: A Computational Framework for Multi-Objective Codon Optimisation and Synthetic Gene Design in Crop Biotechnology
Synthetic gene design for plant transformation requires simultaneous optimisation of multiple, often competing, molecular objectives: translational efficiency, mRNA structural accessibility, codon-pair compatibility, regulatory safety, and species-specific expression context. Existing tools address these objectives in isolation, typically maximising a single metric such as the Codon Adaptation Index (CAI) and neglecting the broader determinants of in-plant expression. We present Plant DNA Designer (PDD), a web-based platform that integrates a 19-objective genetic algorithm with expression-cassette co-design, clade-aware translation-initiation logic, ribosome-velocity trajectory shaping, CRISPR guide-RNA design, and multi-gene pathway balancing across 18 crop species spanning monocot and dicot clades -- each using its own measured codon-usage table from the Kazusa Codon Usage Database. We benchmark PDD against faithful reproductions of the published algorithms of five external tools (JCat/OPTIMIZER/ATGme, IDT, TISIGNER, a CAI+GC heuristic, and a random floor) across six validated rice effector proteins. PDD is the only strategy that holds every objective within acceptable bounds at once: it reduces transgene safety liabilities from 2.3-3.5 to 0.0, and cuts deviation from a 50 % GC synthesis target from 21.8 to 4.0 percentage points, while raising codon harmony from 0.42 to 0.77 -- at a deliberate, moderate cost in raw CAI (0.79 vs 1.00). Consistent with a fair comparison rather than a strawman, a dedicated single-objective tool (IDT) still outperforms PDD on its own axis (harmony 0.93). We anchor the two central proxies against real biology: on 456 real rice genes, CAI and the wobble-weighted tAI are significantly higher in highly-expressed ribosomal-protein genes than in the genomic background (Mann-Whitney p [≤] 10-; tAI AUC 0.75) and correlate at Spearman{rho} = 0.93. Beyond this expression-class anchor, the reported design metrics are in-silico proxies, not wet-lab yield measurements. PDD is released as open-source software under an MIT licence and is freely accessible as a FastAPI web application.