ArticleMolecular & cellular proteomics : MCP2015
Epitope identification from fixed-complexity random-sequence peptide microarrays.
Article in Molecular & cellular proteomics : MCP, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 30 citations in OpenAlex.
- A survey of k-mer methods and applications in bioinformatics.Computational and structural biotechnology journal · 2024Review
- Highly heterogenous humoral immune response in Lyme disease patients revealed by broad machine learning-assisted antibody binding profiling with random peptide arrays.Frontiers in immunology · 2024Article
- Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease.PLoS computational biology · 2023Article
- Serological Approaches for Trypanosoma cruzi Strain Typing.Trends in parasitology · 2021Review
- APRANK: Computational Prioritization of Antigenic Proteins and Peptides From Complete Pathogen Proteomes.Frontiers in immunology · 2021Article
- Protein-Based Immunome Wide Association Studies (PIWAS) for the Discovery of Significant Disease-Associated Antigens.Frontiers in immunology · 2021Article
- Rapid Response to Pandemic Threats: Immunogenic Epitope Detection of Pandemic Pathogens for Diagnostics and Vaccine Development Using Peptide Microarrays.Journal of proteome research · 2020Review
- Antibody characterization using immunosignatures.PloS one · 2020Article
- A general approach for predicting protein epitopes targeted by antibody repertoires using whole proteomes.PloS one · 2019Article
- Humoral Immunity Profiling of Subjects with Myalgic Encephalomyelitis Using a Random Peptide Microarray Differentiates Cases from Controls with High Specificity and Sensitivity.Molecular neurobiology · 2018Article
- General Assessment of Humoral Activity in Healthy Humans.Molecular & cellular proteomics : MCP · 2016Article
- A General Method to Discover Epitopes from Sera.PloS one · 2016Article
- Time-Frequency Analysis of Peptide Microarray Data: Application to Brain Cancer Immunosignatures.Cancer informatics · 2015Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibodies play an important role in modern science and medicine. They are essential in many biological assays and have emerged as an important class of therapeutics. Unfortunately, current methods for mapping antibody epitopes require costly synthesis or enrichment steps, and no low-cost universal platform exists. In order to address this, we tested a random-sequence peptide microarray consisting of over 330,000 unique peptide sequences sampling 83% of all possible tetramers and 27% of pentamers. It is a single, unbiased platform that can be used in many different types of tests, it does not rely on informatic selection of peptides for a particular proteome, and it does not require iterative rounds of selection. In order to optimize the platform, we developed an algorithm that considers the significance of k-length peptide subsequences (k-mers) within selected peptides that come from the microarray. We tested eight monoclonal antibodies and seven infectious disease cohorts. The method correctly identified five of the eight monoclonal epitopes and identified both reported and unreported epitope candidates in the infectious disease cohorts. This algorithm could greatly enhance the utility of random-sequence peptide microarrays by enabling rapid epitope mapping and antigen identification.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.