Machine Learning Method for Optimizing Coding Sequences in Mammalian Cells

Clicks: 8
ID: 331719
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #30 of 33 articles by views in Biology Methods and Protocols

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Mammalian cell lines are the preferred hosts for producing commercially relevant therapeutic proteins such as antibodies, antibody derivatives, and cytokine fusion proteins. Even though significant investment is made to optimize upstream and downstream processes, the optimal gene design parameters for heterologous recombinant protein expression remain poorly understood. We describe a generic approach to gene optimization in which design features are systematically sampled and iteratively modulated using machine learning (ML). Synthetic gene variants encoding the Dasher fluorescent protein, differing only in synonymous codons, were used to interrogate the gene-sequence preferences of transient antibody-expressing HEK293 cells. Synonymous codon variations influenced expression by more than two orders of magnitude. This variation in protein yield was used to build ML models relating gene design features, which were then employed to design further-improved genes. The ML models were shown to be expression system-specific. Messenger RNA levels and ribosome occupancy were highly correlated with protein levels, suggesting that mRNA lifetime is influenced by coding bias. Our results illustrate a novel, generally applicable method to improve gene expression via synonymous re-coding for any protein target or host cell.
Reference Key
openalex_W7220843948 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Elias Theodorou, Michael Stadler, Claes Gustafsson, Mark Welch
Are you one of these authors? Claim your profile
Journal Biology Methods and Protocols
Year 2026
DOI
10.1093/biomethods/bpag057
URL
https://doi.org/10.1093/biomethods/bpag057
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.