Large scale active-learning-guided exploration for in vitro protein production optimization

Olivier Borkowski (1) , Mathilde Koch (2) , Agnes Zettor (3) , Amir Pandi (2) , Angelo Cardoso Batista (2) , Paul Soudier (2) , Jean-Loup Faulon (2, 1, 4)

1 UMR 8030 - Génomique métabolique
2 MICALIS - MICrobiologie de l'ALImentation au Service de la Santé
3 PF-CCB - Criblage chémogénomique et biologique (Plateforme) - Chemogenomic and Biological Screening Platform
4 University of Manchester [Manchester]

Lysate-based cell-free systems have become a major platform to study gene expression but batch-to-batch variation makes protein production difficult to predict. Here we describe an active learning approach to explore a combinatorial space of~4,000,000 cell-free buffer compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provide a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality.

Domaines: Biologie structurale [q-bio.BM]; Sciences du Vivant [q-bio]; Génomique, Transcriptomique et Protéomique [q-bio.GN]; Bio-Informatique, Biologie Systémique [q-bio.QM]

Olivier Borkowski, Mathilde Koch, Agnes Zettor, Amir Pandi, Angelo Cardoso Batista, et al.. Large scale active-learning-guided exploration for in vitro protein production optimization. Nature Communications, 2020, 11 (1), pp.1872. ⟨10.1038/s41467-020-15798-5⟩. ⟨pasteur-02552065⟩