Biology subjects

Capel, H. L.

Publications and source records attributed to Capel, H. L..

5 recordsLinked to original sources

bioRxiv2026

SAbDab2: The structural antibody database in the age of machine learning

The Structural Antibody Database (SAbDab) is a publicly available repository of experimentally determined antibody structures, first released in 2013. Explicit support for single-domain antibodies was added in 2021, with SAbDab-nano. Recently, increasing interest in antibodies has led to a proliferation of novel antibody formats, while simultaneous advances in machine learning have increased demand for standardised, high-quality structure data. Here, we present SAbDab2, re-engineered for the machine-learning age. It introduces support for a variety of new formats, and makes it easy to retrieve and compare all known structures of a given antibody. In addition, SAbDab2 provides ready access to ML-grade structures of antibody and antibody-antigen-complexes, with standardised, versioned train/test splits. These will be updated every six months going forward, and are available at https://zenodo.org/records/20083995. SAbDab2 itself is updated weekly and is freely available at https://sabdab2.opig.stats.ox.ac.uk.

bioinformatics↗

bioRxiv2025

Pox-AbDab: the Orthopoxvirus Antibody Database

In August 2024, the World Health Organisation declared the mpox orthopoxvirus to be a Public Health Emergency of International Concern for the second time in three years, emphasising the need for continued studies into its microbiology and potential therapeutic interventions. Here, we present the Orthopoxvirus Antibody Database (Pox-AbDab), a repository of data on antibodies known to bind or neutralise viruses from the same genus as mpox (https://opig.stats.ox.ac.uk/webapps/poxabdab). Beyond standardising and centralising the data, we highlight challenges in translating knowledge across orthopoxviruses, such as the absence of a function-based nomenclature for virion surface antigens. We also performed an exploratory analysis of the known orthopoxvirus-binding antibody landscape, highlighting their aggregate molecular properties, cross-binding/cross-neutralisation profiles, evidence for immunodominance or immune escape from their epitopes, and gaps in coverage to help orient future research.

immunology↗

bioRxiv2025

LICHEN: Light-chain Immunoglobulin sequence generation Conditioned on the Heavy chain and Experimental Needs

In developing therapeutic antibodies, the heavy chain is often prioritised due to its higher variability and its central role in antigen binding. An appropriate pairing of the light sequence is however important for antibody function. Here we present LICHEN, a heavy chain conditioned light sequence generation tool that enables collaborative light sequence design by leveraging computational capabilities alongside experimental expertise. LICHEN generates light sequences which are valid (antibodylike), diverse in sequence and structure, and conditioned on a specific heavy chain. LICHEN can also condition on germline and CDRs and automatically filter generated sequences for required properties. This allows LICHEN to be used across multiple antibody development use cases. We carry out experimental validation of the method conditioning only on the heavy sequence and on the heavy sequence and binding information. Our in vitro results show that sequences created by LICHEN have effective expression yields and can retain antigen-binding.

bioinformatics↗

bioRxiv2025

ANARCII: A Generalised Language Model for Antigen Receptor Numbering

Antigen receptor numbering allows the rapid delineation of the antigen-binding regions of antibody and T cell receptor (TCR) sequences, from sequence alone. It also allows the comparison of the vast diversity of antigen receptors in a consistent frame of reference. Numbering of antigen receptors is currently achieved by aligning sequences to a reference set. This approach may result in different numbering, depending on the reference set used or may fail to number query sequences derived from new species or rare sequence types. To address this problem, we have built a new numbering method (ANARCII) which requires no alignment step and is based on a Seq2Seq language model. Our results show that ANARCII can deal with the complexity that arises in experimentally collected sequencing data and generalise to sequences which are highly dissimilar to those in training. In test sets designed to contain challenging and ambiguous sequence patterns ANARCII numbering was identical to existing methods for over 99.99% of conserved residues and over 99.94% for complete CDR regions. The lightweight architecture allows numbering of over 90,000 sequences per minute on a single A100 GPU. Furthermore, the ANARCII package can be conditioned to fit rare sequence types and provide new training data for fine-tuning. We demonstrate that fine-tuned versions of ANARCII can correctly number other immunoglobulin domains such as TCRs and VNARs. Our model is freely available as a web tool (https://github.com/oxpig/ANARCII), as well as a package for high throughput numbering of next generation sequencing data (https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabpred/anarcii/).

bioinformatics↗

bioRxiv2023

A comparison of the binding sites of antibodies and single-domain antibodies

Antibodies are the largest class of biotherapeutics. However, in recent years, single-domain antibodies have gained traction due to their smaller size and comparable binding affinity. Antibodies (Abs) and single-domain antibodies (sdAbs) differ in the structures of their binding sites: most significantly, single-domain antibodies lack a light chain and so have just three CDR loops. Given this inherent structural difference, it is important to understand whether Abs and sdAbs are distinguishable in how they engage a binding partner and thus, whether they are suited to different types of epitopes. In this study, we use non-redundant sequence and structural datasets to compare the paratopes, epitopes and antigen interactions of Abs and sdAbs. We demonstrate that even though sd-Abs have smaller paratopes, they target epitopes of equal size to those targeted by Abs. To achieve this, the paratopes of sdAbs contribute more interactions per residue than the paratopes of Abs. Additionally, we find that conserved framework residues are of increased importance in the paratopes of sd-Abs, suggesting that they include non-specific interactions to achieve comparable affinity. Further-more, the epitopes of sdAbs and Abs cannot be distinguished by their shape. For our datasets, sd-Abs do not target more concave epitopes than Abs: we posit that this may be explained by differences in the orientation and compaction of sdAb and Ab CDR-H3 loops. Overall, our results have important implications for the engineering and humanization of sdAbs, as well as the selection of the best modality for targeting a particular epitope.

immunology↗