Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry - a perspective.
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eLife Assessment
This manuscript describes a method using EM polyclonal epitope mapping to help elucidate endogenous antibodies. Overall the work described is interesting and the contribution will be of use to the field that is expected to only increase in impact and value over time. The significance of the work is considered valuable and the strength of evidence to support its findings is considered solid.
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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.
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eLife Assessment
This manuscript describes a method using EM polyclonal epitope mapping to help elucidate endogenous antibodies. Overall the work described is interesting and the contribution will be of use to the field that is expected to only increase in impact and value over time. The significance of the work is considered valuable and the strength of evidence to support its findings is considered solid.
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Reviewer #1 (Public review):
Summary:
The paper addresses the problem of optimising the mapping of serum antibody responses against a known antigen. It uses the croEM analysis of polyclonal Fabs to antibody genes, with the ultimate aim of getting complete and accurate antibody sequences. The method, commonly termed EMPEM, is becoming increasingly used to understand responses in convalescent sera and optimisation of the workflows and provision of openly available tools is of genuine value to a growing number of people.
The authors do not address the experimental aspects of the methods and do not present novel computational tools, rather they use a series of established computational methods to provide workflows that simplify the interpretation of the EM map in terms of the sequences of dominant antibodies.
Strengths:
The paper is …
Reviewer #1 (Public review):
Summary:
The paper addresses the problem of optimising the mapping of serum antibody responses against a known antigen. It uses the croEM analysis of polyclonal Fabs to antibody genes, with the ultimate aim of getting complete and accurate antibody sequences. The method, commonly termed EMPEM, is becoming increasingly used to understand responses in convalescent sera and optimisation of the workflows and provision of openly available tools is of genuine value to a growing number of people.
The authors do not address the experimental aspects of the methods and do not present novel computational tools, rather they use a series of established computational methods to provide workflows that simplify the interpretation of the EM map in terms of the sequences of dominant antibodies.
Strengths:
The paper is well-written and clearly argued. The tests constructed seem appropriate and fair and demonstrate that the workflow works pretty well. For a small subset (~17%) of the EMPEM maps analysed the workflow was able to get convincing assignments of the V-genes.
Weaknesses:
The AI methods used are not a substitute for high quality data and at present very few of the results obtained from EMPEM will be of sufficient quality to robustly assign the sequence of the antibody. However, rather more are likely to be good enough, especially in combination with MS data, to provide a pretty good indication of the V-gene family.
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Reviewer #2 (Public review):
In this manuscript, the authors seek to demonstrate that it is possible to sequence antibody variable domains from cryoEM reconstructions in combination with bottom-up LC-MSMS. In particular, they extract de novo sequences from single particle-cryo-EM-derived maps of antibodies using the "deep-learning tool ModelAngelo", which are run through the program Stitch to try to select the top scoring V-gene and construct a placeholder sequence for the CDR3 of both the heavy and light chain of the antibody under investigation. These reconstructed variable domains are then used as templates to guide the assembly of de novo peptides from LC-MS/MS data to improve the accuracy of the candidate sequence.
Using this approach the authors claim to have demonstrated that "cryoEM reconstructions of monoclonal antigen-antibody …
Reviewer #2 (Public review):
In this manuscript, the authors seek to demonstrate that it is possible to sequence antibody variable domains from cryoEM reconstructions in combination with bottom-up LC-MSMS. In particular, they extract de novo sequences from single particle-cryo-EM-derived maps of antibodies using the "deep-learning tool ModelAngelo", which are run through the program Stitch to try to select the top scoring V-gene and construct a placeholder sequence for the CDR3 of both the heavy and light chain of the antibody under investigation. These reconstructed variable domains are then used as templates to guide the assembly of de novo peptides from LC-MS/MS data to improve the accuracy of the candidate sequence.
Using this approach the authors claim to have demonstrated that "cryoEM reconstructions of monoclonal antigen-antibody complexes may contain sufficient information to accurately narrow down candidate V-genes and that this can be integrated with proteomics data to improve the accuracy of candidate sequences".
WhiIe the approach is clearly a work in progress, the manuscript should made easier to understand for the general reader. Indeed, I had a hard time understanding the workflow until I got to Fig. 3. So re-ordering the figures, for example, may be helpful in this regard.
It would be useful to provide additional concrete examples where the described workflow would assist in the elucidation of CDR3's, in cases where this isn't already known. (In the benchmark dataset from the Electron Microscopy Data Bank, all the antibodies and Fabs are presumably known, as is the case for the monoclonal antibody CR3022). I am having difficulty envisioning how one would prepare samples from actual plasma samples that would be appropriate for single particle cryo-EM and MS data on dominant antibodies of interest. In my experience, most of these samples tend to be quite complex mixtures. So additional discussion of this point would be helpful.
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