Designing a protein with emergent function by combinedin silico, in vitroandin vivoscreening

Abstract

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 comprehensivein silico,in vitro, andin vivoscreening 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 inEscherichia 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.

Translation initiation consistency between in vivo and in vitro bacterial protein expression systems

“…Therefore, the development of an emerging platform for the precise prediction of 5′-UTRs should pay more attention to actual implementation in different expression systems ( Na and Lee, 2010 ; Seo et al, 2013 ; Pandi et al, 2022 ). Looking forward, a novel strategy for characterizing biological systems based on the CFPS platform can provide a valuable tool for advancing the development of metabolic engineering and synthetic biology, similar to the comprehensive i 3 -screening pipeline ( Kohyama et al, 2023 ).…”