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© Biologie structurale et chimie
Structure du domaine en doigt de zinc de la protéine NEMO, déterminée par Résonance magnétique nucléaire (RMN). Cette protéine jouant un rôle dans des maladies (cancer, inflammation), les connaissances acquises sur sa structure offrent de précieuses informations sur sa fonction.
Publication : Nature Communications

Large Scale Active-Learning-Guided Exploration for in Vitro Protein Production Optimization

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Nature Communications - 20 Apr 2020

Olivier Borkowski, Mathilde Koch, Agnès Zettor, Amir Pandi, Angelo Cardoso Batista, Paul Soudier, Jean-Loup Faulon

Link to Pubmed [PMID] – 32312991

Nat Commun. 2020 Apr 20;11(1):1872

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.

Behind the paper : https://bioengineeringcommunity.nature.com/users/382588-olivier-borkowski/posts/66375-active-learning-leads-to-highly-efficient-predictions-in-cell-free-systems

https://www.nature.com/articles/s41467-020-15798-5