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            <name>Title</name>
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                <text>Coronavirus</text>
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                <text>Dominio científico: Coronavirus</text>
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              <text>Identification and mapping of linear antibody epitopes in human serum albumin using high-density Peptide arrays.</text>
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              <text>Lajla Bruntse Hansen, Søren Buus, Claus Schafer-Nielsen</text>
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              <text>We have recently developed a high-density photolithographic, peptide array technology with a theoretical upper limit of 2 million different peptides per array of 2 cm(2). Here, we have used this to perform complete and exhaustive analyses of linear B cell epitopes of a medium sized protein target using human serum albumin (HSA) as an example. All possible overlapping 15-mers from HSA were synthesized and probed with a commercially available polyclonal rabbit anti-HSA antibody preparation. To allow for identification of even the weakest epitopes and at the same time perform a detailed characterization of key residues involved in antibody binding, the array also included complete single substitution scans (i.e. including each of the 20 common amino acids) at each position of each 15-mer peptide. As specificity controls, all possible 15-mer peptides from bovine serum albumin (BSA) and from rabbit serum albumin (RSA) were included as well. The resulting layout contained more than 200.000 peptide fields and could be synthesized in a single array on a microscope slide. More than 20 linear epitope candidates were identified and characterized at high resolution i.e. identifying which amino acids in which positions were needed, or not needed, for antibody interaction. As expected, moderate cross-reaction with some peptides in BSA was identified whereas no cross-reaction was observed with peptides from RSA. We conclude that high-density peptide microarrays are a very powerful methodology to identify and characterize linear antibody epitopes, and should advance detailed description of individual specificities at the single antibody level as well as serologic analysis at the proteome-wide level.</text>
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              <text>2013</text>
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          <name>Identifier</name>
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              <text>DOI: 10.1371/journal.pone.0068902</text>
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              <text>PLoS ONE</text>
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          <name>Publisher</name>
          <description>An entity responsible for making the resource available</description>
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              <text>Public Library of Science (PLoS)</text>
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          <name>Coverage</name>
          <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <text>Science, Medicine</text>
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          <name>Language</name>
          <description>A language of the resource</description>
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              <text>EN</text>
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