Biology subjects

Rossini, E.

Publications and source records attributed to Rossini, E..

2 recordsLinked to original sources

bioRxiv2026

Combining evolution and machine learning-guided pathway optimization to engineer a novel methylsuccinate module for synthetic C1 metabolism in vivo

De novo metabolic pathways open possibilities for sustainable biotransformations in microbes. However, the in vivo-implementation of such new-to-nature pathways is highly challenging and heavily relies on adaptive laboratory evolution (ALE) of the hosts native metabolic network. Here, we assess how much this need for host-centric ALE can be overcome and/or complemented through the informed design of the newly introduced pathway. Exemplifying for a synthetic CO2-fixation module via methyl-succinate, we established methylsuccinate-dependent growth of Escherichia coli over six months by ALE of E. colis native metabolism. In parallel, we developed a machine-learning guided workflow (MEVIS) for the automated engineering of the synthetic pathway, resulting in methylsuccinate-dependent growth within three weeks. Critically, performing MEVIS in the background of the ALE-evolved strain is necessary to further approach wild-type like growth, demonstrating how ALE in combination with machine-learning-guided lab automation holds great potential to accelerate and improve design-build-test-learn cycles in contemporary metabolic engineering.

bioengineering↗

bioRxiv2018

Analysis and Engineering of Substrate Shuttling by the Acyl Carrier Protein (ACP) in Fatty Acid Synthases (FASs)

In the large enzyme complexes of natural biosynthetic pathways, molecules are assembled like in a factory. Carrier domains shuttle substrates and intermediates as covalently attached cargo within the enzyme complex between active sites. The physical confinement of the reaction increases reaction rates and hinders pathway branching. Alternating interactions of substrate-loaded carrier domains with different catalytic domains modulate the chemical environment. In this study, we aim at assessing the impact of domain-domain interactions (DDIs) on the reaction progress of a multienzyme type I fatty acid synthase (FAS) in quantitative terms. We modulate DDIs by single interface mutations, and read out the impact on substrate shuttling by recording fatty acid (FA) chain length product spectra and FAS activities. Our data show that even single interface point mutations can severely affect FA synthesis. With molecular dynamics simulations and modeling, we relate the mutation effects to specific alterations in the molecular interaction networks and domain-domain binding energetics. Some of the presented mutations induce the synthesis of short-chain FAs. These compounds are important commodity products and potent precursors for microbial biofuel production.

bioengineering↗