Biology subjects

Georgeon, S.

Publications and source records attributed to Georgeon, S..

At least 19 recordsLinked to original sources

bioRxiv2026

De novo design of light-controlled conformational protein switches

Light-responsive proteins offer the ability to control biology at an unmatched resolution. Yet, optogenetics is limited to a relatively small number of natural and engineered photoreceptors. The design of light-responsive proteins with native-like complexity is challenging as they must incorporate a light-responsive chromophore, couple chemical changes in the chromophore to structural changes in the protein, and ultimately propagate these changes to downstream effectors. Here, we present the computational design of new light-responsive proteins entirely from the bottom-up. We design de novo proteins that accurately bind a retinal chromophore and further design a protein conformational switching motion linked to retinal isomerisation. This switch unlocks a new protein-peptide interaction, creating light-activated heterodimers with an over 30-fold increase in binding upon illumination. Moreover, we show that the designed heterodimers are customisable, with tuneable wavelengths, sensitivities, and specificities. Further, we use the designed heterodimers to control biological function, regulating intracellular protein-protein interactions and cell-signalling receptors. Finally, we use real-time light-activated NMR to demonstrate that they undergo a defined light-activated conformational change. Overall, we computationally design a new class of light-responsive proteins that may open a new optogenetics toolbox with applications in biology.

biochemistry↗

bioRxiv2026

The Human Bindome: A Proteome-scale Atlas of Designed Binder Candidates

Affinity reagents such as antibodies are indispensable for interrogating proteins biological function. Yet they are costly and frequently unreliable, with unknown sequences, posing challenges to reproducible experimental research. Deep learning-based protein design can now in silico generate affinity reagents achieving reliable experimental success rates, but has remained largely confined to specialist laboratories. Here we present the Human Bindome, a proteome-scale atlas of high-confidence in silico protein binder candidates. By embedding the experimentally benchmarked BindCraft method in an accelerated, parallelized framework with automated domain-level target selection, we generated 306,146 binder candidates covering 8,296 human proteins (40.9% of the full proteome). Every candidate carries a defined sequence, a predicted binder-target structure model, and in silico confidence metrics. We characterize proteome-wide coverage and show that binder epitopes frequently overlap functional sites. This positions the Bindome as a resource of genetically encodable perturbagens for site-specific, modular control of protein function. The Bindome is freely available through a web interface (https://bindome.epfl.ch), with agentic, natural-language querying and as data splits for machine-learning model development. We anticipate that the Bindome will be valuable for the scientific community by providing affinity and perturbation reagents with broad applications in dissecting biological mechanisms as well as in drug and target discovery.

synthetic biology↗

bioRxiv2026

A generalizable interface-seeded framework for de novo design of functional oligomers

Protein oligomers are ubiquitous in biological systems and essential for function. However, the de novo design of oligomers that controllably assemble in response to exogenous stimuli remains challenging. Here, we present an AI-based generative approach that leverages an interface-seeded strategy for designing responsive homo-oligomers from isolated interaction modules. Experimentally validated designs are highly accurate and explore new-to-nature topologies. We show that designs effectively respond to their chemical triggers with conditional oligomerization or to phosphorylation-driven conformational changes with reversible oligomerization. We further functionalized our responsive assemblies to build ligand-dependent membrane binding systems and phosphorylation-controlled gene regulatory switches. Our framework enables the generalizable design of responsive protein complexes, opening novel possibilities for the engineering of biosynthetic systems with sophisticated regulatory mechanisms.

biochemistry↗

bioRxiv2026

De novo Design of Polymorph-Specific Binders Targeting α-Synuclein Fibrils

Alpha-synuclein (aSyn) aggregation into amyloid fibrils is a hallmark of synucleinopathies, a group of neurodegenerative disorders that includes Parkinsons disease. Beyond the central role of fibrils in disease pathology and propagation, distinct aSyn fibril polymorphs have been associated with different synucleinopathies, presenting opportunities for differential diagnosis and structure-based disease-specific phenotyping. Here, we present AmyBind, a computational pipeline for the de novo design of mini-protein binders that target polymorph-specific structural features of amyloid fibrils. Using this approach, we developed two aSyn fibril-specific binders, one of which exhibited polymorph-specificity. Neither binder interfered with fibril elongation in vitro, consistent with their designed binding mode on lateral fibril surfaces. In cell-based models, both binders showed polymorph-specific colocalization but did not affect fibril uptake or seeding, indicating their utility as fibril-selective labeling agents in cellular contexts. These findings demonstrate the feasibility of structure-guided, polymorph-specific amyloid targeting and provide a foundation for developing early-stage diagnostics and therapeutics for synucleinopathies and other amyloid-related diseases.

bioengineering↗

bioRxiv2025

Rational design of chemically responsive cytokines for cancer immunotherapy

Cytokines are key signal mediators of the immune system, playing essential roles in regulating central immunological processes. Despite their unique ability to modulate the immune system, the translation of cytokine-based therapies to the clinic has been significantly hindered by severe toxicities resulting from the pleiotropy and off-target effects of many cytokines. Here, we present a general strategy for the design of switchable cytokines that enables precise control over cytokine activity using the clinically approved drug, Venetoclax. We rationally designed self-inhibited Switchable InterLeukins (SwILs) by embedding the chemically controlled interface (Bcl-2: BIM-BH3) into the cytokines structure, resulting in conditional cytokines activated by Venetoclax. We showed the broad applicability of this strategy across various cytokines, including IL-2, IL-15, and IL-10. In vivo studies with IL-15 demonstrated that the SwILs achieved Venetoclax-dependent tumor control comparable to that of the native cytokine. Additionally, we showed that this strategy can be expanded to respond to other tumor-intrinsic stimuli, such as tumor-specific proteases. Overall, SwILs offer control of cytokine activity, enhancing the safety and clinical applicability of cytokine-based therapeutics.

bioengineering↗

bioRxiv2025

Humanized Caffeine-Inducible Systems for Controlling Cellular Functions

Current cell therapies are limited by the lack of tools for controlling gene expression using humanized systems responsive to non-toxic stimuli. Starting from nanobodies that homodimerize in response to caffeine, we computationally designed inducible heterodimers and humanized the best-performing pairs. We used the resulting caffeine-inducible domains in engineered cytokine receptors for caffeine-inducible STAT3 signaling and in split transcription factors (caff-TFs) containing human-derived zinc-finger proteins. Heterodimerization of split transcription factors drastically enhanced their performance compared to homodimerization. We demonstrate that caff-TFs are compatible with lentiviral and retroviral delivery to Jurkat T-cells and enable inducible expression of therapeutic genes such as chimeric antigen receptors (CARs) in response to caffeine concentrations consistent with normal coffee consumption. By using the common, non-toxic molecule caffeine, and exclusively humanized protein components, these systems promise to be a safer alternative to existing systems and may be used in synthetic biology applications and for safer, more effective cell therapies.

synthetic biology↗

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↗

bioRxiv2024

Mapping targetable sites on the human surfaceome for the design of novel binders

The human cell surfaceome, integral to cell communication and disease mechanisms, presents a prime target for therapeutic intervention. De novo protein binder design against these cell surface proteins offers a promising yet underexplored strategy for drug development. However, the vast search space and limited data on natural or competitive binders have historically limited experimental success. In this study, we systematically analyzed the entire human surfaceome, identifying approximately 4,500 targetable sites and introducing high-quality binder seeds tailored for protein design applications. To validate these seeds, we implemented two experimental approaches (protein scaffolding and peptide cyclization) on three representative targets (FGFR2, IFNAR2, and HER3). Our results revealed a high success rate, emphasizing the precision and therapeutic potential of these seeds, as well as the need for constant improvements of computational protein design pipelines utilizing machine learning and physics-based methods. Additionally, we present SURFACE-Bind, an interactive database offering open access to all generated data. The high-throughput computational design methods and target-specific binder seeds established here pave the way for a new generation of targeted therapeutics for the human surfaceome.

synthetic biology↗

bioRxiv2024

BindCraft: one-shot design of functional protein binders

Protein-protein interactions (PPIs) are at the core of all key biological processes. However, the complexity of the structural features that determine PPIs makes their design challenging. We present BindCraft, an open-source and automated pipeline for de novo protein binder design with experimental success rates of 10-100%. BindCraft leverages the weights of AlphaFold21 to generate binders with nanomolar affinity without the need for high-throughput screening or experimental optimization, even in the absence of known binding sites. We successfully designed binders against a diverse set of challenging targets, including cell-surface receptors, common allergens, de novo designed proteins, and multi-domain nucleases, such as CRISPR-Cas9. We showcase the functional and therapeutic potential of designed binders by reducing IgE binding to birch allergen in patient-derived samples, modulating Cas9 gene editing activity, and reducing the cytotoxicity of a foodborne bacterial enterotoxin. Lastly, we utilize cell surface receptor-specific binders to redirect AAV capsids for targeted gene delivery. This work represents a significant advancement towards a "one design-one binder" approach in computational design, with immense potential in therapeutics, diagnostics, and biotechnology.

bioinformatics↗

bioRxiv2024

Drug-controlled CAR-T cells through the regulation of cell-cell interactions

CAR T-cell therapy is constrained by on-target, off-tumor toxicities as well as cellular exhaustion due to chronic antigen exposure. CARs comprising small-molecule controlled switches can enhance both safety and therapeutic efficacy but are limited by the scarcity of non-immunogenic protein elements responsive to non-immunosuppressive, clinically approved drugs with favorable pharmacodynamics. Here, we combine rational design and library-based optimization of a protein-protein interaction (PPI) of human origin to develop venetoclax-controlled Drug-Regulated Off-switch PPI (DROP)-CARs. DROP-CARs enable dose-dependent release of the tumor-targeting scFv and consequent T-cell dissociation from the target tumor cell. Additionally, we present proof-of-concept for a dual DROP-CAR controlled by different small molecules, as well as for logic-gated synthetic receptors enabling STAT3 signaling. We demonstrate in vitro and in vivo function of DROP-CAR T cells and conclude that the approach holds promise for clinical application.

synthetic biology↗

bioRxiv2024

Accurate single domain scaffolding of three non-overlapping protein epitopes using deep learning

De novo protein design has seen major success in scaffolding single functional motifs, however, in nature most proteins present multiple functional sites. Here we describe an approach to simultaneously scaffold multiple functional sites in a single domain protein using deep learning. We designed small single domain immunogens, under 130 residues, that simultaneously present three distinct and irregular motifs from respiratory syncytial virus. These motifs together comprise nearly half of the designed proteins, and hence the overall folds are quite unusual with little global similarity to proteins in the PDB. Despite this, X-ray crystal structures confirm the accuracy of presentation of each of the motifs, and the multi-epitope design yields improved cross-reactive titers and neutralizing response compared to a single-epitope immunogen. The successful presentation of three distinct binding surfaces in a small single domain protein highlights the power of generative deep learning methods to solve complex protein design problems.

bioengineering↗

bioRxiv2024

Targeting protein-ligand neosurfaces using a generalizable deep learning approach

Molecular recognition events between proteins drive biological processes in living systems. However, higher levels of mechanistic regulation have emerged, where protein-protein interactions are conditioned to small molecules. Here, we present a computational strategy for the design of proteins that target neosurfaces, i.e. surfaces arising from protein-ligand complexes. To do so, we leveraged a deep learning approach based on learned molecular surface representations and experimentally validated binders against three drug-bound protein complexes. Remarkably, surface fingerprints trained only on proteins can be applied to neosurfaces emerging from small molecules, serving as a powerful demonstration of generalizability that is uncommon in deep learning approaches. The designed chemically-induced protein interactions hold the potential to expand the sensing repertoire and the assembly of new synthetic pathways in engineered cells.

biochemistry↗

bioRxiv2023

Exploring "dark matter" protein folds using deep learning

De novo protein design aims to explore uncharted sequence-and structure areas to generate novel proteins that have not been sampled by evolution. One of the main challenges in de novo design involves crafting "designable" structural templates that can guide the sequence search towards adopting the target structures. Here, we present an approach to learn patterns of protein structure based on a convolutional variational autoencoder, dubbed Genesis. We coupled Genesis with trRosetta to design sequences for a set of protein folds and found that Genesis is capable of reconstructing native-like distance-and angle distributions for five native folds and three novel, so-called "dark-matter" folds as a demonstration of generalizability. We used a high-throughput assay to characterize protease resistance of the designs, obtaining encouraging success rates for folded proteins and further biochemically characterized folded designs. The Genesis framework enables the exploration of the protein sequence and fold space within minutes and is not bound to specific protein topologies. Our approach addresses the backbone designability problem, showing that structural patterns in proteins can be efficiently learned by small neural networks and could ultimately contribute to the de novo design of proteins with new functions.

bioinformatics↗

bioRxiv2023

An atlas of protein homo-oligomerization across domains of life

Protein structures are essential to understand cellular processes in molecular detail. While advances in AI revealed the tertiary structure of proteins at scale, their quaternary structure remains mostly unknown. Here, we describe a scalable strategy based on AlphaFold2 to predict homo-oligomeric assemblies across four proteomes spanning the tree of life. We find that 50% of archaeal, 45% of bacterial, and 20% of eukaryotic proteomes form homomers. Our predictions accurately capture protein homo-oligomerization, recapitulate megadalton complexes, and unveil hundreds of novel homo-oligomer types. Analyzing these datasets reveals coiled-coil regions as major enablers of quaternary structure evolution in Eukaryotes. Integrating these structures with omics data shows that a majority of known protein complexes are symmetric. Finally, these datasets provide a structural context for interpreting disease mutations, which we find enriched at interfaces. Our strategy is applicable to any organism and provides a comprehensive view of homo-oligomerization in proteomes, protein networks, and disease. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/544317v1_ufig1.gif" ALT="Figure 1"> View larger version (79K): org.highwire.dtl.DTLVardef@1507a12org.highwire.dtl.DTLVardef@7e522aorg.highwire.dtl.DTLVardef@1445410org.highwire.dtl.DTLVardef@eb09f7_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Computational design of soluble analogues of integral membrane protein structures

De novo design of complex protein folds using solely computational means remains a significant challenge. Here, we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from GPCRs, are not found in the soluble proteome and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses reveal high thermal stability of the designs and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, standing as a proof-of-concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.

bioinformatics↗

bioRxiv2022

Rational design of chemically controlled antibodies and protein therapeutics

Protein-based therapeutics such as monoclonal antibodies and cytokines are important therapies in various pathophysiological conditions such as oncology, auto-immune disorders, and viral infections. However, the wide application of such protein therapeutics is often hindered by dose-limiting toxicities and adverse effects, namely cytokine storm syndrome, organ failure and others. Therefore, spatiotemporal control of the activities of these proteins is crucial to further expand their application. Here, we report the design and application of small molecule-controlled switchable protein therapeutics by taking advantage of a previously engineered OFF-switch system. We used Rosetta modeling suite to computationally optimize the affinity between B-cell lymphoma 2 (Bcl-2) protein and a previously developed computationally designed protein partner (LD3) to obtain a fast and efficient heterodimer disruption upon addition of a competing drug (Venetoclax). The incorporation of the engineered OFF-switch system into CTLA4, anti-HER2 antibodies or an Fc-fused IL-15 cytokine demonstrated an efficient disruption in vitro, as well as fast clearance in vivo upon addition of the competing drug Venetoclax. These results provide a proof-of-concept for the rational design of controllable biologics by introducing a drug-induced OFF-switch into existing protein-based therapeutics.

bioengineering↗

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↗