Efficient encoding of large antigenic spaces by epitope prioritization with Dolphyn

Anna-Maria Liebhoff, Thiagarajan Venkataraman, William R. Morgenlander, Miso Na, Tomasz Kula, Kathleen Waugh, Charles Morrison, Marian Rewers, Randy Longman, June Round, Stephen Elledge, Ingo Ruczinski, Ben Langmead and H. Benjamin Larman ()
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Anna-Maria Liebhoff: Johns Hopkins University
Thiagarajan Venkataraman: Johns Hopkins University
William R. Morgenlander: Johns Hopkins University
Miso Na: Johns Hopkins University
Tomasz Kula: Harvard Medical School
Kathleen Waugh: University of Colorado Denver
Charles Morrison: Clinical and Epidemiologic Sciences, FHI 360
Marian Rewers: University of Colorado Denver
Randy Longman: Division of Gastroenterology and Hepatology, Department of Medicine, Weill Cornell Medicine
June Round: University of Utah School of Medicine
Stephen Elledge: Harvard Medical School
Ingo Ruczinski: Johns Hopkins University
Ben Langmead: Johns Hopkins University
H. Benjamin Larman: Johns Hopkins University

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract We investigate a relatively underexplored component of the gut-immune axis by profiling the antibody response to gut phages using Phage Immunoprecipitation Sequencing (PhIP-Seq). To cover large antigenic spaces, we develop Dolphyn, a method that uses machine learning to select peptides from protein sets and compresses the proteome through epitope-stitching. Dolphyn compresses the size of a peptide library by 78% compared to traditional tiling, increasing the antibody-reactive peptides from 10% to 31%. We find that the immune system develops antibodies to human gut bacteria-infecting viruses, 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.

Date: 2024
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DOI: 10.1038/s41467-024-45601-8

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