Canavan, C. | bioRxiv Science

bioRxiv2025

CoV-UniBind: A Unified Antibody Binding Database for SARS-CoV-2

Since the emergence of SARS-CoV-2, numerous studies have investigated antibody interactions with viral variants in vitro, and several datasets have been curated to compile available protein structures and experimental measurements. However, existing data remain fragmented, limiting their utility for the development and validation of machine learning models for antibody-antigen interaction prediction. Here, we present CoV-UniBind, a unified database comprising over 75,000 entries of SARS-CoV-2 antibody-antigen sequence, binding, and structural data, integrated and standardised from three public sources and multiple peer-reviewed publications. To demonstrate its utility, we benchmarked multiple protein folding and inverse folding models across tasks relevant to antibody design and vaccine development. We expect CoV-UniBind to facilitate future computational efforts in antibody and vaccine development against SARS-CoV-2. Availability and implementationThe curated datasets, structures, model scores and antibody synonyms are free to download at https://huggingface.co/datasets/InstaDeepAI/cov-unibind. Folded structures are available upon request.

immunology↗

bioRxiv2023

On-grid purification of electron microscopy samples via a 3D-printed flow-cell

While recent advances in cryo-EM, coupled with single particle analysis, have the potential to allow structure determination in a near-native state from vanishingly few individual particles, this vision has yet to be realised in practise. Requirements for particle numbers that currently far exceed the theoretical lower limits, challenges with the practicalities of achieving high concentrations for difficult-to-produce samples, and inadequate sample-dependent imaging conditions, all result in significant bottlenecks preventing routine structure determination using cryo-EM. Therefore, considerable efforts are being made to circumvent these bottlenecks by developing affinity purification of samples on-grid; at once obviating the need to produce large amounts of protein, as well as more directly controlling the variable, and sample-dependent, process of grid preparation. In this proof-of-concept study, we demonstrate a further practical step towards this paradigm, developing a 3D-printable flow-cell device to allow on-grid affinity purification from raw inputs such as whole cell lysates, using graphene oxide-based affinity grids. Our flow-cell device can be interfaced directly with routinely-used laboratory equipment such as liquid chromatographs, or peristaltic pumps, fitted with standard chromatographic (1/16") connectors, and can be used to allow binding of samples to affinity grids in a controlled environment prior to the extensive washing required to remove impurities. Furthermore, by designing a device which can be 3D printed and coupled to routinely used laboratory equipment, we hope to increase the accessibility of the techniques presented herein to researchers working towards single-particle macromolecular structures.

biophysics↗