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. 2021:2344:3-6.
doi: 10.1007/978-1-0716-1562-1_1.

Protein Microarray-Based Proteomics for Disease Analysis

Affiliations

Protein Microarray-Based Proteomics for Disease Analysis

Rodrigo Barderas et al. Methods Mol Biol. 2021.

Abstract

As we approach the twentieth anniversary of completing the international Human Genome Project, the next (and arguably most significant) frontier in biology consists of functionally understanding the proteins, which are encoded by the genome and play a crucial role in all of biology and medicine. To accomplish this challenge, different proteomics strategies must be devised to examine the activities of gene products (proteins) at scale. Among them, protein microarrays have been used to accomplish a wide variety of investigations such as examining the binding of proteins and proteoforms to DNA, small molecules, and other proteins; characterizing humoral immune responses in health and disease; evaluating allergenic proteins; and profiling protein patterns as candidate disease-specific biomarkers. In Protein Microarray for Disease Analysis: Methods and Protocols, expert researchers involved in the field of protein microarrays provide concise descriptions of the methodologies that they currently use to fabricate microarrays and how they apply them to analyze protein interactions and responses of proteins to dissect human disease.

Keywords: Disease analysis; Functional analysis; Microarray data analysis; Protein microarrays; Protein science; Proteomics.

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References

    1. Piovesan A, Antonaros F, Vitale L, Strippoli P, Pelleri MC, Caracausi M (2019) Human protein-coding genes and gene feature statistics in 2019. BMC Res Notes 12(1):315. https://doi.org/10.1186/s13104-019-4343-8 - DOI - PubMed - PMC
    1. Ezkurdia I, Juan D, Rodriguez JM, Frankish A, Diekhans M, Harrow J, Vazquez J, Valencia A, Tress ML (2014) Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes. Hum Mol Genet 23(22):5866–5878. https://doi.org/10.1093/hmg/ddu309 - DOI - PubMed - PMC
    1. Smith LM, Kelleher NL, Consortium for Top Down Proteomics (2013) Proteoform: a single term describing protein complexity. Nat Methods 10(3):186–187. https://doi.org/10.1038/nmeth.2369 - DOI - PubMed - PMC
    1. Perdigao N, Rosa A (2019) Dark Proteome Database: studies on dark proteins. High Throughput 8(2). https://doi.org/10.3390/ht8020008
    1. Perdigao N, Heinrich J, Stolte C, Sabir KS, Buckley MJ, Tabor B, Signal B, Gloss BS, Hammang CJ, Rost B, Schafferhans A, O’Donoghue SI (2015) Unexpected features of the dark proteome. Proc Natl Acad Sci U S A 112(52):15898–15903. https://doi.org/10.1073/pnas.1508380112 - DOI - PubMed - PMC

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