Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry
peer-reviewed · eLife · 2025
| Date | 2025-04-23 |
| Type | peer-reviewed |
| Venue | eLife |
| Publisher | eLife Sciences Publications, Ltd |
| Contribution | adjacent |
| DOI | 10.7554/eLife.101322.3 |
| Citations (OpenAlex) | 8 |
| Venue 2-year citedness | 1.79 |
Abstract
Antibodies are a major component of adaptive immunity against invading pathogens. Here, we explore possibilities for an analytical approach to characterize the antigen-specific antibody repertoire directly from the secreted proteins in convalescent serum. This approach aims to perform simultaneous antibody sequencing and epitope mapping using a combination of single particle cryo-electron microscopy (cryoEM) and bottom-up proteomics techniques based on mass spectrometry (LC-MS/MS). We evaluate the performance of the deep-learning tool ModelAngelo in determining de novo antibody sequences directly from reconstructed 3D volumes of antibody-antigen complexes. We demonstrate that while map quality is a critical bottleneck, it is possible to sequence antibody variable domains from cryoEM reconstructions with accuracies of up to 80–90%. While the rate of errors exceeds the typical levels of somatic hypermutation, we show that the ModelAngelo-derived sequences can be used to assign the used V-genes. This provides a functional guide to assemble de novo peptides from LC-MS/MS data more accurately and improves the tolerance to a background of polyclonal antibody sequences. Following this proof-of-principle, we discuss the feasibility and future directions of this approach to characterize antigen-specific antibody repertoires.
Methods and tools
- cryoEM + MS antibody sequencing workflow: Workflow combining cryoEM-derived sequences (via ModelAngelo) with LC-MS/MS-based assembly (via Stitch) for simultaneous polyclonal antibody sequencing and epitope mapping. Reports 80–90% accuracy on antibody variable domains and improves MS-based assembly against polyclonal backgrounds.
Data used
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