Nous avons le plaisir d’annoncer que G.CLIPS Biotech a remporté le prix du public du meilleur poster scientifique des AFSSI Connexions 2025 !
La session posters est un temps fort des AFSSI Connexions qui permet aux membres de mettre en avant leurs travaux de recherches.
Pour cette troisième édition, 15 posters ont été exposés par les membres de l’AFSSI. Ils ont pu présenter leurs expertises et recherches aux participants et au jury de cette session. Deux prix étaient à remporter :
- Prix du jury, dont le lauréat est Charles Rive Laboratories.
- Prix du public, dont les lauréats est G.CLIPS Biotech.
En tant que lauréat du prix du public du meilleur poster scientifique, G.CLIPS Biotech a remporté :
- Une prise de parole lors de la soirée networking
- Une mise en visibilité sur les canaux AFSSI
Regardez l’interview de G.CLIPS Biotech !
Découvrez l’abstract !
Integrated High-Throughput Protein Expression Platform combining cell-free and bioluminescence technologies with Artificial Intelligence (AI)
Authors : Y. Abdelaziz, M. Gransagne, K. Jondot, J. Spiaczka, B. Tillier
Abstract:
With the growing use of artificial intelligence (AI) to optimize protein expression, particularly in cell-free systems, new challenges have arisen in efficiently generating high-quality, protein-specific data. Traditional methods often require multiple rounds of experimentation, making them both time-consuming and costly. To address these limitations, we are developing an AI-driven Design of Experiments (DOE) framework. Our approach leverages protein sequence data to extract key parameters, which are then processed by a recommendation system to propose optimal experimental conditions. This enables faster iterations, greater flexibility, and significant cost and time savings compared to traditional active learning strategies.
By combining this approach with our next-generation bioluminescence technology, we can rapidly establish optimal expression conditions in our cell-free system for almost any type of protein. Once these conditions are established, our platform has the capability to express hundreds to thousands of DNA sequences, providing purified protein samples ready for initial characterization in less than 48 hours.
Here, we present a case study demonstrating a significant improvement in the expression yield of VHH candidates using our AI-guided cell-free expression system. By optimizing the experimental conditions, such as temperature, buffer composition, and transcription/translation rates, we were able to increase the yield of functional VHHs. This result highlights the power of our integrated platform in rapidly identifying the most efficient conditions for protein production, without the need for time-consuming trial-and-error experiments.
Building on this success, our platform offers a robust and scalable solution for the expression and screening of diverse protein libraries. Our process not only saves valuable time and resources, but also provides a more flexible and reproducible method for selecting protein-based therapeutics, diagnostics, or research tools.
