Public articles linked to the same research event.
New Biotechnology 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
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