Biology subjects

Driscoll, C. L.

Publications and source records attributed to Driscoll, C. L..

6 recordsLinked to original sources

bioRxiv2026

Duality Vaccination through Nanodesign of Click-Ligated B-Cell Inhibitory Receptor Bridging

Synthesis of precise and robust molecular architectures is critical to harness the power of the immune system. The dimensions, rigidity and multivalency of nanoparticles contribute to eliciting high levels of antibodies from vaccination. However, antibodies vary hugely in quality: some antibodies are highly effective at neutralizing pathogens, while others provide little or no benefit. Here we establish switchable and modular platforms for protein and nanoparticle assembly that program the target of antibody responses. We introduce Duality vaccination: the simultaneous delivery of activating and inhibitory stimuli for focusing immune responses. To construct activatory immunogens, we employ click biology with spontaneous amidation to functionalize plug-and-display self-assembling porous dodecahedral nanocages. We construct inhibitory immunogens using SpyMask protease-gated bispecific assembly, ligating the antigen to binders that bridge the B cell receptor to immune checkpoint receptors. This dual immunogen architecture tailors the elicited antibodies to down-modulate undesired B cell responses. We establish the effect of temporal and spatial separation of activatory and inhibitory immunogen delivery. We further explore sensitivity to valency, linker flexibility and adjuvant delivery, employing model targets and coronavirus antigens. Dual programmable functionalization identifies key nanodesign features to control in vivo outputs, providing logic-gating towards urgent challenges in the creation of protective vaccines.

synthetic biology↗

bioRxiv2026

Coevolutionary mining of prokaryotic non-coding elements with a genome language model

Microbial genomes encode compact molecular machines and diverse non-coding RNAs (ncRNAs) essential to gene regulation, pathogenesis, and many foundational biotechnologies. However, annotation remains largely protein-centric and homology-driven. Here, we introduce Minerva, a framework for coevolutionary mining that uses genome language models to predict local interactions directly from sequence as two-dimensional maps. Introducing two complementary techniques, categorical Jacobian fingerprinting and interaction heads, we demonstrate fast, accurate, alignment-free prediction of ncRNA base-pairing, monomeric protein contacts, and repetitive sequence motifs. Applied to 150 bacterial genomes, Minerva recovers known systems and predicts that 84.3% of predicted intergenic base-pairing falls outside of known annotations. In Pseudomonas, we find that the widespread TwoAYGGAY ncRNA family carries large secondary-structure extensions and is often flanked by short upstream repetitive motifs and larger downstream genomic repeats. Interpreting the coevolution maps, we find that Minerva emergently detects open-reading-frame (ORF) signatures at the DNA level despite never being trained to do so. In prophages within these genomes, we discover that Unknown Group 27 (UG27) reverse transcriptase systems encode arrays of structurally conserved yet sequence-diverse ncRNAs that template complementary DNA (cDNA) hairpin products. Together, these results establish coevolutionary mining as a scalable route to genome annotation and biological discovery across the rapidly expanding microbial universe.

bioinformatics↗

bioRxiv2026

Forecasting viral evolution from phylogenetic trees

Viral mutation forecasting plays a key role in pandemic preparedness by enabling researchers to anticipate novel variants and design proactive interventions. Evolutionary histories, represented as phylogenetic trees, offer key insights into the emergence of past and present strains, yet their role in predicting future sequence changes remains largely unexplored. We introduce antiGen, a machine learning model that forecasts the evolutionary future of viruses by learning from their evolutionary past. antiGen achieves state-of-the-art performance for predicting mutations to the SARS-CoV-2 spike protein, anticipating never-before-seen mutations and mutations that emerge years after the model's training window. antiGen-forecasted spike mutations also retain pseudoviral infectivity in vitro. Moreover, antiGen demonstrates leading predictive performance on surface proteins of influenza virus, respiratory syncytial virus, and dengue virus despite far less available sequencing data. Viral evolution models that explicitly learn from phylogenetic structure offer a valuable resource for applications ranging from epidemiological modeling to therapeutic development.

evolutionary biology↗

bioRxiv2025

Efficient generation of epitope-targeted de novo antibodies with Germinal

Obtaining novel antibodies against specific protein targets is a widely important yet experimentally laborious process. Meanwhile, computational methods for antibody design have been limited by low success rates that currently require resource-intensive screening. Here, we introduce Germinal, a broadly enabling generative pipeline that designs antibodies against specific epitopes with nanomolar binding affinities while requiring only low-n experimental testing. Our method co-optimizes antibody structure and sequence by integrating a structure predictor with an antibody-specific protein language model to perform de novo design of functional complementarity-determining regions (CDRs) onto a user-specified structural framework. When tested against four diverse protein targets, Germinal successfully designed functional antibodies across all targets and binder formats, testing only 43-101 designs for each antigen. Validated designs also exhibited robust expression in mammalian cells and high sequence and structural novelty. We provide open-source code and full computational and experimental protocols to facilitate wide adoption. Germinal represents a milestone in efficient, epitope-targeted de novo antibody design, with notable implications for the development of molecular tools and therapeutics.

synthetic biology↗

bioRxiv2025

Generative design of novel bacteriophages with genome language models

Many important biological functions arise not from single genes, but from complex interactions encoded by entire genomes. Genome language models have emerged as a promising strategy for designing biological systems, but their ability to generate functional sequences at the scale of whole genomes has remained untested. Here, we report the first generative design of viable bacteriophage genomes. We leveraged frontier genome language models, Evo 1 and Evo 2, to generate whole-genome sequences with realistic genetic architectures and desirable host tropism, using the lytic phage {Phi}X174 as our design template. Experimental testing of AI-generated genomes yielded 16 viable phages with substantial evolutionary novelty. Cryo-electron microscopy revealed that one of the generated phages utilizes an evolutionarily distant DNA packaging protein within its capsid. Multiple phages demonstrate higher fitness than {Phi}X174 in growth competitions and in their lysis kinetics. A cocktail of the generated phages rapidly overcomes {Phi}X174-resistance in three E. coli strains, demonstrating the potential utility of our approach for designing phage therapies against rapidly evolving bacterial pathogens. This work provides a blueprint for the design of diverse synthetic bacteriophages and, more broadly, lays a foundation for the generative design of useful living systems at the genome scale.

synthetic biology↗

bioRxiv2023

SpyCombinator Assembly of Bispecific Binders

Bispecific antibodies are a successful and expanding therapeutic class, bridging two cell-types or engaging two different molecules on the same cell. Bispecifics unlock avenues towards synergy, resistance evasion, and signaling bias. Standard approaches to generate bispecifics are complicated by the need for disulfide reduction/oxidation or cloning of each binder molecule in two different formats. Here we present a modular approach to bispecifics using SpyTag/SpyCatcher spontaneous amidation, where all binders are cloned in the same format, bearing a SpyTag. Two SpyTag-fused antigen-binding modules can be precisely conjugated onto DoubleCatcher, a tandem SpyCatcher where the second Catcher is unreactive until unveiling of reactivity using site-specific protease. Assembly on DoubleCatcher is efficient in phosphate-buffered saline at 37 {degrees}C, with half-times less than 5 min for both SpyCatcher arms and over 97% bispecific homogeneity. We engineer a panel of DoubleCatchers, locked through disulfide bonds to direct binders to project in different directions from the hub. We establish a generalized methodology for one-pot assembly and purification of bispecifics in 96-well plate format. A panel of Fab, affibody or nanobody binders recognizing different sites on HER2 were coupled to DoubleCatcher, revealing unexpected combinations with anti-proliferative or pro-proliferative activity on HER2-addicted cancer cells. Bispecific activity depended sensitively on both the order of the binders within the assembly and the geometry of DoubleCatcher scaffolds. These findings support the need for straightforward assembly in different formats. SpyMask provides a simple and scalable tool to discover synergy in bispecific activity, through modulating receptor organization and geometry.

bioengineering↗