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

ABSTRACT: 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.

SUBMITTER: Borkowski O

PROVIDER: S-EPMC7170859 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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

Borkowski Olivier O Koch Mathilde M Zettor Agnès A Pandi Amir A Batista Angelo Cardoso AC Soudier Paul P Faulon Jean-Loup JL

Nature communications 20200420 1

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 experim ...[more]

PMID: 32312991

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Dataset Information

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

Publications

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

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