Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102:a bioinformatic approach to the prediction of new epitopes

Walshe, Valerie A., Hattotuwagama, Channa K., Doytchinova, Irini A., Wong, MaiLee, Macdonald, Isabel K., Mulder, Arend, Claas, Frans H.J., Pellegrino, Pierre, Turner, Jo, Williams, Ian, Turnbull, Emma L., Borrow, Persephone and Flower, Darren R (2009). Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102:a bioinformatic approach to the prediction of new epitopes. PLoS ONE, 4 (11),

Abstract

Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102.

Publication DOI: https://doi.org/10.1371/journal.pone.0008095
Divisions: Life & Health Sciences > Pharmacy
Life & Health Sciences
Additional Information: © 2009 Walshe et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Uncontrolled Keywords: alleles,amino acid motifs,computational biology,edetic acid,epitopes,HIV-1,HLA-C antigens,histocompatibility antigens class I,humans,mononuclear leukocytes,major histocompatibility complex,statistical models,peptides,protein binding,tertiary protein structure,Agricultural and Biological Sciences(all),Biochemistry, Genetics and Molecular Biology(all),Medicine(all)
Full Text Link: http://www.plos ... al.pone.0008095
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2009
Authors: Walshe, Valerie A.
Hattotuwagama, Channa K.
Doytchinova, Irini A.
Wong, MaiLee
Macdonald, Isabel K.
Mulder, Arend
Claas, Frans H.J.
Pellegrino, Pierre
Turner, Jo
Williams, Ian
Turnbull, Emma L.
Borrow, Persephone
Flower, Darren R ( 0000-0002-8542-7067)

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License: Creative Commons Attribution


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