2026-03-04 北里大学
研究の概要
生成AI「OrthologTransformer」でDNA配列を再設計したプラスチック分解酵素を枯草菌に導入
＜関連情報＞
- https://www.kitasato-u.ac.jp/jp/news/20260304-01.html
- https://www.kitasato-u.ac.jp/jp/albums/abm.php&n=20260304_プレスリリース「【北里大学・JST】DNA言語に対する生成AI基盤モデルを開発」.pdf
- https://www.nature.com/articles/s41467-026-69966-0
オーソログ情報と生成モデリングを活用した種間遺伝子再設計 Cross-species gene redesign leveraging ortholog information and generative modeling
Manato Akiyama,Motohiko Tashiro,Ying Huang,Mika Uehara,Taiki Kanzaki,Mitsuhiro Itaya,Masakazu Kataoka,Kenji Miyamoto & Yasubumi Sakakibara
Nature Communications Published:03 March 2026
DOI:https://doi.org/10.1038/s41467-026-69966-0
Abstract
Conventional approaches to heterologous gene expression rely on codon optimization, which is limited to swapping synonymous codons and fails to capture deeper adaptive changes. In contrast, naturally evolved orthologous genes include non-synonymous mutations, insertions, and deletions that confer functional adaptation to different host contexts. Here we present OrthologTransformer, a Transformer-based deep learning model that converts orthologous genes between species by learning from large-scale orthologous gene datasets. The model recapitulates evolutionary differences—from synonymous codon swaps to amino acid-changing mutations and indels—to predict coding sequences optimized for target species while preserving protein function. In extensive tests across diverse bacterial species pairs, the model’s context-aware gene designs more closely resembled native host orthologs, preserved protein functionality, and achieved superior expression yields compared to codon-optimized sequences. As proof of concept, an OrthologTransformer-redesigned PETase expressed in Bacillus subtilis showed robust activity, producing approximately 10-fold more reaction product than the codon-optimized enzyme, and achieving higher expression levels, thereby demonstrating improved enzyme performance via AI-guided gene design.
