Biology subjects

Kelton, W.

Publications and source records attributed to Kelton, W..

6 recordsLinked to original sources

bioRxiv2025

Generative design of antibody Fc-variants with synthetic and programmable functional profiles

Beyond antigen recognition, antibodies direct diverse immune effector functions through their constant (Fc) domain. While the Fc domain is central to antibody biology and therapeutic efficacy, our understanding of how Fc sequence encodes function remains limited, as most of Fc sequence space has not been experimentally mapped or linked to Fc-receptor engagement. Furthermore, the extensive overlap in Fc-receptor binding sites on the Fc domain has impeded efforts to engineer antibodies with tailored, multi-receptor engagement profiles that can precisely control downstream immunity. Here we introduce a novel framework for Fc engineering that integrates protein engineering with deep learning to rationally predict and engineer antibody Fc function. Using a yeast-based, aglycosylated Fc display system, we performed deep mutational scanning across the entire human IgG1 Fc domain, allowing the rational design of a diverse combinatorial library of more than 108 Fc-variants. This library was sorted based on binding to a panel of eight canonical Fc-receptors, and the resulting populations were deep sequenced to generate a high-quality dataset comprising millions of unique Fc sequences annotated with their respective Fc-receptor binding profiles. Deep learning-based classifiers trained on this dataset accurately predicted Fc-receptor binding activity from Fc sequence across all Fc-receptors tested. We further developed FcGPT, a domain-specific autoregressive protein language model pre-trained on over three million unique Fc sequences, and refined by post-training through reinforcement learning with experimental feedback (RLXF) and synthetic verifiers. FcGPT enables the computational design of novel Fc-variants with user-defined Fc-receptor binding profiles, providing a foundational tool for understanding and programming antibody-mediated immunity.

bioengineering↗

bioRxiv2025

Simple high-throughput encoding of deep mutational scanning libraries by oligo-based Golden Gate assembly

Control over mutational library diversity is an essential consideration when engineering proteins, but is often fraught with trade-offs between diversity, specificity, and affordability. Contemporary library assembly approaches often incorporate oligonucleotide pool synthesis to achieve affordable, precise mutagenesis; however, these oligos are often reliant on complex designs to facilitate downstream PCR and/or restriction digests. Direct hybridisation of oligo pools is an overlooked strategy to simplify mutagenesis, especially when paired with a type IIS restriction cloning approach. We validate this approach by designing, hybridising, and deep sequencing single and dual substitution CDR region parts derived from nanobody GA10. Assembly of these parts into a full-length nanobody CDS facilitated the phage display of variant libraries for affinity maturation against its cyclic peptide target. Variants identified through enrichment analysis were expressed in isolation and yielded improved affinities by more than 100-fold. Recent advances in machine learning have successfully inferred improved variants outside of screened library space, but require controlled, multi-mutant libraries. The library assembly approach outlined in this research is well-suited for such approaches.

synthetic biology↗

bioRxiv2024

A rapid approach for linear epitope vaccine profiling reveals unexpected epitope tag immunogenicity

Antibody epitope profiling is essential for assessing the robustness of vaccine-induced immune responses, particularly while in development. Despite advancements in computational tools, high throughput experimental epitope validation remains an important step. Here, we describe a readily accessible method for rapid linear epitope profiling using phage-displayed oligo pools in combination with Nanopore deep sequencing. We applied this approach to TeeVax3, a Group A Streptococcus vaccine candidate, to investigate the antibody response generated in a pre-clinical rabbit model and assess antigen immunogenicity. Surprisingly, we found a strong bias in antibody binding response towards the N-terminal epitope tag used for purification. These tags are widely reported to have low immunogenicity and are frequently left uncleaved in pre-clinical studies. We further confirmed that the observed immune response against the epitope tag dominated even the conformational binding response and, using synthetic peptides, narrowed the epitope down to a set of 10 residues inclusive of the Histidine residues. Our findings highlight the importance of epitope-tag removal in pre-clinical studies and demonstrate the utility of rapid nanopore sequencing for early-stage vaccine evaluation.

immunology↗

bioRxiv2022

Immunogenic fusion proteins induce neutralizing SARS-CoV-2 antibodies in the serum and milk of sheep

Antigen-specific polyclonal immunoglobulins derived from the serum, colostrum, or milk of immunized ruminant animals have potential as scalable therapeutics for the control of viral diseases such as COVID-19. Enhancing the efficacy of vaccine antigens to induce robust and specific antibody responses remains central to developing highly effective formulations. The direct fusion of immunoglobulin (IgG) Fc domains or other immune-stimulating proteins to antigens has shown promise in several mammalian species but has not yet been tested and optimized in commercially-relevant ruminant species. Here we show that the immunization of sheep with fusions of the receptor binding domain (RBD) of SARS-CoV-2 to ovine IgG2a Fc domains promotes significantly higher levels of antigen-specific antibodies compared to native RBD or full-length spike antigens. This antibody population was shown to contain elevated levels of neutralizing antibodies that suppress binding between the RBD and soluble hACE2 receptors in vitro. The parallel evaluation of a second immune-stimulating fusion candidate, Granulocyte-macrophage colony-stimulating factor (GM-CSF), induced high neutralizing responses in select animals but narrowly missed achieving significance at the group level. Furthermore, we demonstrate that the antibodies induced by these fusion antigens are transferred from maternal serum into colostrum/milk. These antibodies also demonstrate cross-neutralizing activity against diverse SARS-CoV-2 variants including delta and omicron. Our findings highlight a new pathway for recombinant antigen design in ruminant animals with applications in immune milk production and animal health.

bioengineering↗

bioRxiv2021

Predictive profiling of SARS-CoV-2 variants by deep mutational learning

The continual evolution of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the emergence of variants that show resistance to vaccines and neutralizing antibodies (1-4) threaten to prolong the coronavirus disease 2019 (COVID-19) pandemic (5). Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine learning-guided protein engineering technology, which is used to interrogate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as omicron (B.1.1.529), thus supporting decision making for public heath as well as guiding the development of therapeutic antibody treatments and vaccines for COVID-19.

synthetic biology↗

bioRxiv2019

Molecular design, optimization and genomic integration of chimeric B cell receptors in murine B cells

Immune cell therapies based on the integration of synthetic antigen receptors provide a powerful strategy for the treatment of diverse diseases, most notably retargeting T cells engineered to express chimeric antigen receptors (CAR) for cancer therapy. In addition to T lymphocytes, B lymphocytes may also represent valuable immune cells that can be engineered for therapeutic purposes such as protein replacement therapy or recombinant antibody production. In this article, we report a promising concept for the molecular design, optimization and genomic integration of a novel class of synthetic antigen receptors, chimeric B cell receptors (CBCR). We initially optimized CBCR expression and detection by modifying the extracellular surface tag, the transmembrane regions and intracellular signaling domains. For this purpose, we stably integrated a series of CBCR variants into immortalized B cell hybridomas using CRISPR-Cas9. Subsequently, we developed a reliable and consistent pipeline to precisely introduce cassettes of several kilobases size into the genome of primary murine B cells, again via CRISPR-Cas9 induced HDR. Finally, we were able to show the robust surface expression and antigen recognition of a synthetic CBCR in primary B cells. We anticipate that CBCRs and our approach for engineering primary B cells will be a valuable tool for the advancement of future B cell-based immune therapies.

immunology↗