bioRxiv · 10.1101/2023.07.30.551179
Efficient encoding of large antigenic spaces by epitope prioritization with Dolphyn
Abstract
We investigated a relatively underexplored component of the gut-immune axis by profiling the antibody response to gut phages using Phage Immunoprecipitation Sequencing (PhIP-Seq). To enhance this approach, we developed Dolphyn, a novel method that uses machine learning to select peptides from protein sets and compresses the proteome through epitope-stitching. Dolphyn improves the fraction of gut phage library peptides bound by antibodies from 10% to 31% in healthy individuals, while also reducing the number of synthesized peptides by 78%. In our study on gut phages, we discovered that the immune system develops antibodies to bacteria-infecting viruses in the human gut, particularly E.coli-infecting Myoviridae. Cost-effective PhIP-Seq libraries designed with Dolphyn enable the assessment of a wider range of proteins in a single experiment, thus facilitating the study of the gut-immune axis.
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Liebhoff, A.-M., Venkataraman, T., Morgenlander, W. R., Na, M., Kula, T., Waugh, K., Morrison, C., Rewers, M., Longman, R., Round, J., Elledge, S., Ruczinski, I., Langmead, B., Larman, H. B.. 2023-07-31. Efficient encoding of large antigenic spaces by epitope prioritization with Dolphyn. https://doi.org/10.1101/2023.07.30.551179
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