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# Codon Optimization

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researchsquare.com Oct 16, 2026
Enzyme design moves past amino acids: structures now yield E. coli-ready coding sequences, and two Cel5E sites gain heat tolerance and activity together
Generative enzyme design has mostly optimized at the amino acid level, rarely accounting for coding sequences and host expression at once. EZPro-Fit places protein 3D structure, coding sequences and taxonomic information in one generative framework, pretrained on thermophilic microbial data and fine-tuned on highly expressed E. coli genes, so it generates host-adapted coding sequences directly from structure. On the industrial endoglucanase Cel5E, its variants N291D and D259E raised both melting temperature and catalytic activity in experiments.
Generative enzyme design has mostly optimized at the amino acid level, rarely accounting for coding sequences and host expression at once. EZPro-Fit places protein 3D structure, coding sequences and taxonomic information in one generative framework, pretrained on thermophilic microbial data and fine-tuned on highly expressed E. coli genes, so it generates host-adapted coding sequences directly from structure. On the industrial endoglucanase Cel5E, its variants N291D and D259E raised both melting temperature and catalytic activity in experiments.
Nanyang Technological University Macao Polytechnic University Nanjing Agricultural University
Ran Xu（First author）
EZPro-Fit Flow Matching Codon Optimization Protein Engineering
New Biotechnology Sep 14, 2026
Sequence optimization targeting mRNA stability enhances monoclonal antibody titers in CHO cells
This study treats mRNA stability as a tunable codon-optimization design parameter: it built a combinatorial library of synonymous coding-sequence variants of an IgG1 light chain integrated as single copies at a defined genomic locus in CHO cells, used steady-state mRNA abundance quantified by deep sequencing of gDNA and mRNA as a proxy for stability, trained a machine-learning model predicting mRNA abundance from coding sequence using embeddings from a pre-trained nucleotide transformer, and incorporated this predictor with established translational metrics into a genetic algorithm for multi-objective codon optimization; as proof-of-concept with Trastuzumab-encoding sequences, high-abundance designs raised intracellular mRNA by 41%, protein titer by 59%, and cell-specific productivity by 8
This study treats mRNA stability as a tunable codon-optimization design parameter: it built a combinatorial library of synonymous coding-sequence variants of an IgG1 light chain integrated as single copies at a defined genomic locus in CHO cells, used steady-state mRNA abundance quantified by deep sequencing of gDNA and mRNA as a proxy for stability, trained a machine-learning model predicting mRNA abundance from coding sequence using embeddings from a pre-trained nucleotide transformer, and incorporated this predictor with established translational metrics into a genetic algorithm for multi-objective codon optimization; as proof-of-concept with Trastuzumab-encoding sequences, high-abundance designs raised intracellular mRNA by 41%, protein titer by 59%, and cell-specific productivity by 8
Bayer (Germany)
Sebastian Schneckener（First author）
Codon Optimization Genetic Algorithm mRNA Stability Nucleotide Transformer Monoclonal Antibody Expression

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