Smooth $\%$MinMax: A Differentiable Relaxation for Codon Harmonization
Codon harmonization aims to adapt the coding sequences for heterologous expression while preserving the native-like patterns of frequent and rare codons that may influence local translation dynamics and co-translational protein folding. However, widely used harmonization metrics, such as $\%$MinMax, are defined on discrete codon sequences and are, therefore, not readily compatible with gradient-based neural codon design. Here, we introduce Smooth $\%$MinMax, denoted as $\%{\rm MinMax}_{(s)}$, a differentiable relaxation of the conventional hard $\%$MinMax metric, denoted as $\%{\rm MinMax}_{(h)}$. $\%{\rm MinMax}_{(s)}$ replaces the discrete codon-usage values with probability-weighted sy...
| Authors | Yoonho Jeong, Hyunwoo Choi, Ryan Fernandez Medina Hariri et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 4 July 2026 |
| Last updated at source | 26 August 2026 |
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