Mots-Clés
protein binder design
deep learning
synthetic biology
immunosuppression
sepsis
Description
AI-based design of short-acting LILRB1 agonists for immunosuppressive therapy in early hyperinflammatory sepsis
Context
Sepsis is a life-threatening condition driven by a dysregulated host immune response, in which excessive early inflammation can cause endothelial injury and uncontrolled cytokine bursts. Current therapeutic strategies lack the precision needed to selectively dampen early hyperinflammation while preserving the antimicrobial response required for pathogen clearance. This project addresses this gap by designing artificial mini-proteins targeting LILRB1, a key inhibitory receptor expressed on immune cells.
Our approach is inspired by malaria RIFIN proteins, which naturally engage LILRB1 to suppress immune activation. Structural studies have revealed that different RIFIN families bind LILRB1 through two distinct domain regions, suggesting at least two pharmacologically distinct agonist geometries. The intern will contribute to the computational design of artificial proteins reproducing these binding modes, with the ultimate goal of modulating LILRB1 inhibitory signalling in a controlled, transient, and cell-targeted manner.
The intern will work at the Integrative Bioinformatics Platform, BIOI2, (I2BC, Gif-sur-Yvette) within a multidisciplinary consortium that includes immunopathology (IDMIT, CEA Fontenay), experimental protein screening (AlphaRep platform, I2BC), and immunogenicity assessment (SiMoS, CEA Saclay).
Scientific objectives of the internship
The intern will pursue two complementary design strategies: (1) direct trans-agonists, designed to target LILRB1 from outside the target immune cell by mimicking MHC-I-like engagement at the D1/D2 region; and (2) allosteric agonists, designed to stabilize a LILRB1 conformation that favors its interaction with endogenous MHC-I on the same cell, modulating inhibitory signalling through a more cell-intrinsic mechanism. Both strategies require the intern to prepare and analyze target structures, run generative protein design pipelines, and apply in silico validation filters.
Workflow
Starting from experimentally resolved RIFIN–LILRB1 complex structures, the intern will extract the relevant binding motifs, scaffold them using state-of-the-art deep learning-based design tools, and evaluate candidate structures using affinity prediction, structural quality metrics, and interface analysis. A selection of computationally validated designs will be forwarded to the experimental AlphaRep platform for yeast display screening and downstream functional assays (ITIM phosphorylation, phosphatase recruitment, cytokine suppression).
This internship is ideal for a student interested in the intersection of structural biology, deep learning, and therapeutic protein engineering, and offers exposure to a full design-to-experiment pipeline.