Designing a protein with emergent function by combined in silico, in vitro and in vivo screening

Recently, utilization of machine learning (ML)-based methods has led to astonishing progress in protein design and, thus, the design of new biological functionality. However, emergent functions that require higher-order molecular interactions, such as the ability to self-organize, are still extremel...

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Bibliographic Details
Published inbioRxiv
Main Authors Kohyama, Shunshi, Frohn, Bela Paul, Babl, Leon, Schwille, Petra
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 19.02.2023
Subjects
Cell division
Pattern formation
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Summary:Recently, utilization of machine learning (ML)-based methods has led to astonishing progress in protein design and, thus, the design of new biological functionality. However, emergent functions that require higher-order molecular interactions, such as the ability to self-organize, are still extremely challenging to implement. Here, we describe a comprehensive in silico, in vitro, and in vivo screening pipeline (i3-screening) to develop and validate ML-designed artificial homologs of a bacterial protein that confers its role in cell division through the emergent function of spatiotemporal pattern formation. Moreover, we present complete substitution of a wildtype gene by an ML-designed artificial homolog in Escherichia coli. These results raise great hopes for the next level of synthetic biology, where ML-designed synthetic proteins will be used to engineer cellular functions.Competing Interest StatementThe authors have declared no competing interest.
DOI:10.1101/2023.02.16.528840