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Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease
Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these a...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313026/ https://www.ncbi.nlm.nih.gov/pubmed/37339137 http://dx.doi.org/10.1371/journal.pcbi.1010773 |
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author | Chowdhury, Robayet Taguchi, Alexander T. Kelbauskas, Laimonas Stafford, Phillip Diehnelt, Chris Zhao, Zhan-Gong Williamson, Phillip C. Green, Valerie Woodbury, Neal W. |
author_facet | Chowdhury, Robayet Taguchi, Alexander T. Kelbauskas, Laimonas Stafford, Phillip Diehnelt, Chris Zhao, Zhan-Gong Williamson, Phillip C. Green, Valerie Woodbury, Neal W. |
author_sort | Chowdhury, Robayet |
collection | PubMed |
description | Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. This “immunosignature” approach, is based on a statistical evaluation of the binding pattern for each sample but it ignores the information contained in the amino acid sequences that the antibodies are binding to. Here, similar array-based antibody profiles are instead used to train a neural network to model the sequence dependence of molecular recognition involved in the immune response of each sample. The binding profiles used resulted from incubating serum from 5 infectious disease cohorts (Hepatitis B and C, Dengue Fever, West Nile Virus and Chagas disease) and an uninfected cohort with 122,926 peptide sequences on an array. These sequences were selected quasi-randomly to represent an even but sparse sample of the entire possible combinatorial sequence space (~10(12)). This very sparse sampling of combinatorial sequence space was sufficient to capture a statistically accurate representation of the humoral immune response across the entire space. Processing array data using the neural network not only captures the disease-specific sequence-binding information but aggregates binding information with respect to sequence, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared with the raw binding data. Because the neural network model is trained on all samples simultaneously, a highly condensed representation of the differential information between samples resides in the output layer of the model, and the column vectors from this layer can be used to represent each sample for classification or unsupervised clustering applications. |
format | Online Article Text |
id | pubmed-10313026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103130262023-07-01 Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease Chowdhury, Robayet Taguchi, Alexander T. Kelbauskas, Laimonas Stafford, Phillip Diehnelt, Chris Zhao, Zhan-Gong Williamson, Phillip C. Green, Valerie Woodbury, Neal W. PLoS Comput Biol Research Article Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. This “immunosignature” approach, is based on a statistical evaluation of the binding pattern for each sample but it ignores the information contained in the amino acid sequences that the antibodies are binding to. Here, similar array-based antibody profiles are instead used to train a neural network to model the sequence dependence of molecular recognition involved in the immune response of each sample. The binding profiles used resulted from incubating serum from 5 infectious disease cohorts (Hepatitis B and C, Dengue Fever, West Nile Virus and Chagas disease) and an uninfected cohort with 122,926 peptide sequences on an array. These sequences were selected quasi-randomly to represent an even but sparse sample of the entire possible combinatorial sequence space (~10(12)). This very sparse sampling of combinatorial sequence space was sufficient to capture a statistically accurate representation of the humoral immune response across the entire space. Processing array data using the neural network not only captures the disease-specific sequence-binding information but aggregates binding information with respect to sequence, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared with the raw binding data. Because the neural network model is trained on all samples simultaneously, a highly condensed representation of the differential information between samples resides in the output layer of the model, and the column vectors from this layer can be used to represent each sample for classification or unsupervised clustering applications. Public Library of Science 2023-06-20 /pmc/articles/PMC10313026/ /pubmed/37339137 http://dx.doi.org/10.1371/journal.pcbi.1010773 Text en © 2023 Chowdhury et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Chowdhury, Robayet Taguchi, Alexander T. Kelbauskas, Laimonas Stafford, Phillip Diehnelt, Chris Zhao, Zhan-Gong Williamson, Phillip C. Green, Valerie Woodbury, Neal W. Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease |
title | Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease |
title_full | Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease |
title_fullStr | Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease |
title_full_unstemmed | Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease |
title_short | Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease |
title_sort | modeling the sequence dependence of differential antibody binding in the immune response to infectious disease |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313026/ https://www.ncbi.nlm.nih.gov/pubmed/37339137 http://dx.doi.org/10.1371/journal.pcbi.1010773 |
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