Biology subjects

Lecomte, M.

Publications and source records attributed to Lecomte, M..

2 recordsLinked to original sources

bioRxiv2023

PROxAb Shuttle: A non-covalent plug-and-play platform for the rapid generation of tumor-targeting antibody-PROTAC conjugates

Proteolysis-targeting chimeras (PROTACs) have evolved in recent years from an academic idea to a therapeutic modality with more than 25 active clinical programs. However, achieving oral bioavailability and cell-type specificity remains a challenge, especially for PROTACs recruiting the von Hippel-Lindau (VHL) E3 ligase. Herein, we present an unprecedented, plug- and-play platform for VHL-recruiting PROTACs to overcome these limitations. Our platform allows for the generation of non-covalent antibody-PROTAC complexes within minutes and obviates the need for prior PROTAC modification, antibody-drug linker chemistry optimization or bioconjugation. Our technology relies on camelid-derived antibody domains (VHHs) which can easily be engineered into existing therapeutic antibody scaffolds. The resulting targeted, bispecific fusion proteins can be complexed with PROTACs at defined PROTAC-to-antibody ratios and have been termed PROxAb Shuttles. PROxAb Shuttles can prolong the half-life of PROTACs from hours to days, demonstrate anti-tumor efficacy in vivo and have the potential for reloading in vivo to further boost efficacy.

biochemistry↗

bioRxiv2023

A digital twin of bacterial metabolism during cheese production

Cheese organoleptic properties result from complex metabolic processes occurring in microbial communities. A deeper understanding of such mechanisms makes it possible to improve both industrial production processes and end-product quality through the design of microbial consortia. In this work, we caracterise the metabolism of a three-species community consisting of Lactococcus lactis, Lactobacillus plantarum and Propionibacterium freudenreichii during a seven-week cheese production process. Using genome-scale metabolic models and omics data integration, we modeled and calibrated individual dynamics using monoculture experiments, and coupled these models to capture the metabolism of the community. This digital twin accurately predicted the dynamics of the community, enlightening the contribution of each microbial species to organoleptic compound production. Further metabolic exploration raised additional possible interactions between the bacterial species. This work provides a methodological framework for the prediction of community-wide metabolism and highlights the added-value of dynamic metabolic modeling for the comprehension of fermented food processes.

bioinformatics↗