Biology subjects

Wehrle, S.

Publications and source records attributed to Wehrle, S..

6 recordsLinked to original sources

bioRxiv2025

Predicting TCR antigen specificity at proteome-scale with synthetic immune cells and machine learning

TCR specificity to peptide-HLA antigens is central to immunology, impacting responses in infection, autoimmunity and cancer. Achieving precise recognition while avoiding off-target reactivity is critical for effective immunity and safe therapeutic interventions. Comprehensive, proteome-wide specificity profiling of TCRs is challenging with current methods, which notably lack integrated machine learning for large-scale analysis. Here, we report a synthetic immune cell system coupled with machine learning to enable TCR functional and specificity mapping of peptide-HLA antigens at proteome-scale. Multi-step immunogenomic engineering of synthetic antigen-presenting cells (APCs) was performed to enable stable mono-allelic integration and precise display of peptide antigens and HLA class I from a defined genomic locus, ensuring genomically-encoded antigen presentation. Compatible with a synthetic TCR displaying T cell system, this platform incorporates a fluorescent reporter of cytokine-mediated signaling for real-time activation detection in both synthetic APCs and T cells. We combined this screening with diverse peptide antigen libraries and deep sequencing to train supervised machine learning models. These models were applied to predict TCR specificity to peptide-HLA antigens across the entire human proteome. Experimental validation confirmed novel off-targets for therapeutic TCR candidates, including for a clinically-approved TCR therapeutic. This integrated synthetic immune cell and machine learning approach provides unprecedented proteome-wide peptide-HLA specificity mapping to support the development of safer TCR-based therapies. One sentence summaryWe present a synthetic immune cell platform integrated with machine learning that enables prediction of TCR specificity to peptide-HLA antigens at proteome-scale.

immunology↗

bioRxiv2025

Computationally designed stem-epitope mimetics elicit broadly reactive antibodies

Broad protection against diverse influenza viruses can be conferred by broadly neutralizing antibodies (bnAbs) targeting a conserved site on the hemagglutinin (HA) stem domain. However, the low immunogenicity of this antigenic region hinders the robust induction of such antibodies. Here, we showcase a structure-based immunogen design strategy focusing on the surface mimicry of antigenic sites. By leveraging the structural definition of a stem epitope, we apply computational protein design to develop epitope mimetics to focus the immune response against this site of viral vulnerability. The structurally complex antigenic site is displayed on heterologous protein scaffolds, retaining excellent binding towards known HA stem-specific bnAbs. Our epitope-mimetic induces stem-specific antibodies against highly divergent group 1 and 2 subtypes. The results provide a general framework for the design of novel immunogens eliciting focused immune responses which may be a valuable tool in the development of effective vaccine candidates against other variable pathogens.

biochemistry↗

bioRxiv2024

Structure-based Design of Chimeric Influenza Hemagglutinins to Elicit Cross-group Immunity

Antigenic variability among influenza virus strains poses a significant challenge to developing broadly protective, long-lasting vaccines. Current annual vaccines target specific strains, requiring accurate prediction for effective neutralization. Despite sequence diversity across phylogenetic groups, the hemagglutinin (HA) head domains structure remains highly conserved. Utilizing this conservation, we designed cross-group chimeric HAs that combine antigenic surfaces from distant strains. By structure-guided transplantation of receptor-binding site (RBS) residues, we displayed an H3 RBS on an H1 HA scaffold. These chimeric immunogens elicit cross-group polyclonal responses capable of neutralizing both base and distal strains. Additionally, the chimeras integrate heterotrimeric immunogens, enhancing modular vaccine design. This approach enables the inclusion of diverse strain segments to generate broad polyclonal responses. In the future, such modular immunogens may serve as tools for evaluating immunodominance and refining immunization strategies, offering potential to bridge and enhance immune responses in individuals with pre-existing immunity. This strategy holds promise for advancing universal influenza vaccine development. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/628867v2_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@18dec5borg.highwire.dtl.DTLVardef@98587forg.highwire.dtl.DTLVardef@1d9fb2dorg.highwire.dtl.DTLVardef@1f97884_HPS_FORMAT_FIGEXP M_FIG Graphical abstract: Overview of cross-group RBS transplantation approachPhylogenetically diverse HA strains can be incorporated into chimeric immunogens by RBS transplantation. The chimera are evaluated for cross-reactivity to subtype-specific antibodies and the ability to elicit neutralizing antibodies to multiple strains. C_FIG

biochemistry↗

bioRxiv2022

De novo design of site-specific protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic, and structural data grows. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction (PPI) networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. We exploit a geometric deep learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features critical to drive PPIs. We hypothesized these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof-of-principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1, and CTLA-4. Several designs were experimentally optimized while others were purely generated in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling a novel approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

bioengineering↗

bioRxiv2019

Structural basis for recognition of RALF peptides by LRX proteins during pollen tube growth

Plant reproduction relies on the highly regulated growth of the pollen tube for proper sperm delivery. This process is controlled by secreted RALF signaling peptides, which have been previously shown to be perceived by CrRLK1Ls membrane receptor-kinases and leucine-rich (LRR) extensin proteins (LRXs). Here we demonstrate that RALF peptides are active as folded, disulfide bond-stabilized proteins, which can bind to the LRR domain of LRX proteins with nanomolar affinity. Crystal structures of the LRX-RALF signaling complexes reveal LRX proteins as constitutive dimers. The N-terminal LRR domain containing the RALF binding site is tightly linked to the extensin domain via a cysteine-rich tail. Our biochemical and structural work reveals a complex signaling network by which RALF ligands may instruct different signaling proteins - here CrRLK1Ls and LRXs - through structurally different binding modes to orchestrate cell wall remodeling in rapidly growing pollen tubes.\n\nSignificancePlant reproduction relies on proper pollen tube growth to reach the female tissue and release the sperm cells. This process is highly regulated by a family of secreted signaling peptides that are recognized by cell-wall monitoring proteins to enable plant fertilization. Here, we report the crystal structure of the LRX-RALF cell-wall complex and we demonstrate that RALF peptides are active as folded proteins. RALFs are autocrine signaling proteins able to instruct LRX cell-wall modules and CrRKL1L receptors, through structurally different binding modes to coordinate pollen tube integrity.

plant biology↗

bioRxiv2018

Rosetta FunFolDes - a general framework for the computational design of functional proteins

The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function, nevertheless, it remains a challenge for state-of-the-art methodologies. Here, we present a computational design approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins. We performed extensive benchmarks, where the most unexpected finding was that the design of function into proteins may not necessarily reside in the global minimum of the energetic landscape, which could have important implications in the field. We have computationally designed and experimentally characterized a distant structural template and a de novo \"functionless\" fold, two prototypical design challenges, to present important viral epitopes. Overall, we present an accessible strategy to repurpose old protein folds for new functions, which may lead to important improvements on the computational design of functional proteins.

bioinformatics↗